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-->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EIn this quickstart, you will build a real-time IoT sensor dashboard powered by \u003Cstrong\u003ESnowflake Postgres\u003C/strong\u003E, \u003Cstrong\u003EStreamlit in Snowflake (SiS)\u003C/strong\u003E, and \u003Cstrong\u003ESnowflake Cortex AI\u003C/strong\u003E. You will create a managed PostgreSQL instance, populate it with realistic smart-building sensor data, build interactive charts and filters, add a natural language chart generator, and wire up an AI chatbot that answers questions about your data in plain English using real numbers — all without leaving Snowflake.\u003C/p\u003E\n","\u003Cp\u003EThis guide also shows you how to build the entire app using \u003Cstrong\u003ESnowflake CoCo (CoCo)\u003C/strong\u003E prompts — Snowflake's AI coding assistant. Each section includes the exact CoCo prompt you can use to generate that section of the app or you can skip to the bottom for the full CoCo prompt.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Will Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA \u003Cstrong\u003ESnowflake Postgres instance\u003C/strong\u003E loaded with IoT sensor data (buildings, sensors, readings)\u003C/li\u003E\u003Cli\u003EA \u003Cstrong\u003Emulti-page Streamlit dashboard\u003C/strong\u003E with KPI metrics, daily trend charts, and building summaries\u003C/li\u003E\u003Cli\u003EA \u003Cstrong\u003ECharts on Demand\u003C/strong\u003E page with interactive filters, heatmaps, distribution histograms, and a \u003Cstrong\u003Enatural language chart generator\u003C/strong\u003E powered by Cortex AI\u003C/li\u003E\u003Cli\u003EAn \u003Cstrong\u003EAI Agent Search\u003C/strong\u003E chatbot that answers questions about your sensor data in plain English using real numbers from the database — not SQL queries\u003C/li\u003E\u003Cli\u003EA \u003Cstrong\u003ESnow CLI deployment\u003C/strong\u003E using \u003Ccode\u003Esnowflake.yml\u003C/code\u003E for reproducible SiS deployments\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Will Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to create and configure a Snowflake Postgres instance\u003C/li\u003E\u003Cli\u003EHow to generate realistic IoT data using PostgreSQL's \u003Ccode\u003Egenerate_series()\u003C/code\u003E function\u003C/li\u003E\u003Cli\u003EHow to build a multi-page Streamlit in Snowflake app with container runtime and \u003Ccode\u003Epsycopg2\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EHow to call Snowflake Cortex AI from both SiS (container runtime) and local development\u003C/li\u003E\u003Cli\u003EHow to build a natural language chart generator (NL to SQL to chart)\u003C/li\u003E\u003Cli\u003EHow to build an AI chatbot that gives direct answers with real data (not SQL queries) by injecting live data context into the prompt\u003C/li\u003E\u003Cli\u003EHow to deploy to SiS using \u003Ccode\u003Esnow streamlit deploy\u003C/code\u003E with \u003Ccode\u003Esnowflake.yml\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EHow to pass Postgres credentials to container runtime using \u003Ccode\u003E.streamlit/secrets.toml\u003C/code\u003E as a deployment artifact\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA role with the required privileges (see table below). \u003Cstrong\u003EACCOUNTADMIN\u003C/strong\u003E has all of these by default, but each can be granted individually to other roles.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECortex AI\u003C/strong\u003E access — your role needs the \u003Ccode\u003ESNOWFLAKE.CORTEX_USER\u003C/code\u003E database role\u003C/li\u003E\u003Cli\u003EA \u003Cstrong\u003Ewarehouse\u003C/strong\u003E for the Streamlit app's query execution\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESnowflake CoCo CLI\u003C/strong\u003E (recommended, for AI-assisted development) — \u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-cli\"\u003Egetting started\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERequired privileges:\u003C/strong\u003E\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPrivilege\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat it's for\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EGrant statement\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ECREATE POSTGRES INSTANCE ON ACCOUNT\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECreate the Postgres instance\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EGRANT CREATE POSTGRES INSTANCE ON ACCOUNT TO ROLE my_role;\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ECREATE INTEGRATION ON ACCOUNT\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECreate the External Access Integration\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EGRANT CREATE INTEGRATION ON ACCOUNT TO ROLE my_role;\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ECREATE NETWORK POLICY ON ACCOUNT\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECreate network policies for Postgres ingress\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EGRANT CREATE NETWORK POLICY ON ACCOUNT TO ROLE my_role;\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ECREATE STREAMLIT ON SCHEMA\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECreate the Streamlit app\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EGRANT CREATE STREAMLIT ON SCHEMA my_db.my_schema TO ROLE my_role;\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ESNOWFLAKE.CORTEX_USER\u003C/code\u003E database role\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECall Cortex AI LLM functions\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EGRANT DATABASE ROLE SNOWFLAKE.CORTEX_USER TO ROLE my_role;\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate a Postgres Instance\u003C/h2\u003E\n","\u003Cp\u003EIn this step, you will create a managed PostgreSQL instance inside Snowflake and configure network access so your Streamlit app can connect to it.\u003C/p\u003E\n","\u003Ch3\u003EStep 1.1: Configure network access\u003C/h3\u003E\n","\u003Cp\u003ERun the following in a \u003Cstrong\u003ESnowsight SQL worksheet\u003C/strong\u003E with a role that has the required privileges (ACCOUNTADMIN works, or a custom role with the grants from the Prerequisites table):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE NETWORK RULE IF NOT EXISTS iot_lab_ingress\n  TYPE = IPV4\n  VALUE_LIST = ('0.0.0.0/0')\n  MODE = POSTGRES_INGRESS;\n\nCREATE NETWORK POLICY IF NOT EXISTS iot_lab_policy\n  ALLOWED_NETWORK_RULE_LIST = ('iot_lab_ingress');\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E The \u003Ccode\u003E0.0.0.0/0\u003C/code\u003E rule allows connections from any IP address. This is fine for a lab environment. For production, restrict this to specific IP ranges or set up additional networking.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECoCo prompt:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECreate a network rule and network policy for a Postgres instance.\nAllow all IPv4 ingress (0.0.0.0/0) for lab purposes.\nName them iot_lab_ingress and iot_lab_policy.\n\u003C/code\u003E\u003C/pre\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 1.2: Create the Postgres instance\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE POSTGRES INSTANCE iot_sensors\n  COMPUTE_FAMILY = 'STANDARD_M'\n  STORAGE_SIZE_GB = 10\n  AUTHENTICATION_AUTHORITY = POSTGRES\n  NETWORK_POLICY = 'iot_lab_policy'\n  COMMENT = 'IoT sensor dashboard lab';\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETip:\u003C/strong\u003E This creates two users: \u003Ccode\u003Esnowflake_admin\u003C/code\u003E and \u003Ccode\u003Eapplication\u003C/code\u003E with auto-generated passwords. \u003Cstrong\u003ECopy the passwords immediately\u003C/strong\u003E — they are only shown once.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 1.3: Verify the instance is ready\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Wait until state = READY (typically 3-5 minutes)\nDESCRIBE POSTGRES INSTANCE iot_sensors;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 1.4: Connect and verify\u003C/h3\u003E\n","\u003Cp\u003EUsing psql (replace \u003Ccode\u003E<hostname>\u003C/code\u003E and \u003Ccode\u003E<password>\u003C/code\u003E with your values):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epsql "postgresql://snowflake_admin:<password>@<hostname>:5432/postgres?sslmode=require"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERun a quick test:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT version();\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see PostgreSQL 18 (or your selected version).\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECoCo prompt:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECreate a Snowflake Postgres instance called iot_sensors with STANDARD_M\ncompute, 10GB storage, and the iot_lab_policy network policy. Then save\nthe connection details when it is ready for building with this instance.\n\u003C/code\u003E\u003C/pre\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EGenerate IoT Sensor Data\u003C/h2\u003E\n","\u003Cp\u003ENow you will create the schema and generate realistic smart-building sensor data entirely with SQL — no external files or downloads needed.\u003C/p\u003E\n","\u003Ch3\u003EStep 2.1: Create the schema\u003C/h3\u003E\n","\u003Cp\u003EConnect to your Postgres instance and run:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Buildings in the sensor network\nCREATE TABLE buildings (\n    building_id   SERIAL PRIMARY KEY,\n    name          TEXT NOT NULL,\n    location      TEXT NOT NULL,\n    floors        INT NOT NULL\n);\n\n-- Sensors installed in buildings\nCREATE TABLE sensors (\n    sensor_id        SERIAL PRIMARY KEY,\n    building_id      INT NOT NULL REFERENCES buildings(building_id),\n    sensor_type      TEXT NOT NULL,    -- 'temperature', 'humidity', 'energy'\n    unit             TEXT NOT NULL,    -- '°F', '%RH', 'kW'\n    install_location TEXT NOT NULL,    -- e.g. 'Floor 1 - Lobby'\n    is_active        BOOLEAN DEFAULT TRUE\n);\n\n-- Time-series sensor readings\nCREATE TABLE readings (\n    reading_id    BIGSERIAL PRIMARY KEY,\n    sensor_id     INT NOT NULL REFERENCES sensors(sensor_id),\n    reading_time  TIMESTAMP NOT NULL,\n    value         NUMERIC(10,2) NOT NULL\n);\n\n-- Index for efficient time-range queries\nCREATE INDEX idx_readings_sensor_time ON readings(sensor_id, reading_time);\nCREATE INDEX idx_readings_time ON readings(reading_time);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 2.2: Seed buildings and sensors\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EINSERT INTO buildings (name, location, floors) VALUES\n    ('HQ Tower',        'Downtown Campus',  12),\n    ('Data Center',     'East Campus',       3),\n    ('Warehouse Alpha', 'West Campus',       1);\n\nINSERT INTO sensors (building_id, sensor_type, unit, install_location) VALUES\n    -- HQ Tower\n    (1, 'temperature', '°F',  'Floor 1 - Lobby'),\n    (1, 'temperature', '°F',  'Floor 6 - Office'),\n    (1, 'temperature', '°F',  'Floor 12 - Executive'),\n    (1, 'humidity',    '%RH', 'Floor 1 - Lobby'),\n    (1, 'humidity',    '%RH', 'Floor 6 - Office'),\n    (1, 'energy',      'kW',  'Main Panel'),\n    -- Data Center\n    (2, 'temperature', '°F',  'Server Room A'),\n    (2, 'temperature', '°F',  'Server Room B'),\n    (2, 'humidity',    '%RH', 'Server Room A'),\n    (2, 'energy',      'kW',  'UPS System'),\n    (2, 'energy',      'kW',  'Cooling Unit'),\n    -- Warehouse Alpha\n    (3, 'temperature', '°F',  'Loading Dock'),\n    (3, 'temperature', '°F',  'Cold Storage'),\n    (3, 'humidity',    '%RH', 'Cold Storage'),\n    (3, 'energy',      'kW',  'Main Panel');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 2.3: Generate realistic sensor readings\u003C/h3\u003E\n","\u003Cp\u003EThis query generates 30 days of readings at 15-minute intervals for every sensor. The values follow realistic patterns — daily temperature cycles, stable data center temps, and energy usage that peaks during business hours:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EINSERT INTO readings (sensor_id, reading_time, value)\nSELECT\n    s.sensor_id,\n    ts,\n    CASE s.sensor_type\n        -- Temperature: sine wave for daily cycle + random noise\n        WHEN 'temperature' THEN\n            CASE\n                -- Data center server rooms: tight 65-72°F range\n                WHEN s.install_location LIKE 'Server Room%' THEN\n                    round((68 + 3 * sin(extract(HOUR FROM ts) * pi() / 12)\n                           + (random() - 0.5) * 2)::numeric, 2)\n                -- Cold storage: 34-38°F\n                WHEN s.install_location = 'Cold Storage' THEN\n                    round((36 + 1.5 * sin(extract(HOUR FROM ts) * pi() / 12)\n                           + (random() - 0.5) * 1)::numeric, 2)\n                -- Office/lobby: 68-78°F daily cycle\n                ELSE\n                    round((72 + 4 * sin((extract(HOUR FROM ts) - 6) * pi() / 12)\n                           + (random() - 0.5) * 3)::numeric, 2)\n            END\n        -- Humidity: inverse of temperature pattern\n        WHEN 'humidity' THEN\n            CASE\n                WHEN s.install_location LIKE 'Server Room%' THEN\n                    round((45 + 3 * sin(extract(HOUR FROM ts) * pi() / 12)\n                           + (random() - 0.5) * 2)::numeric, 2)\n                WHEN s.install_location = 'Cold Storage' THEN\n                    round((80 + 5 * sin(extract(HOUR FROM ts) * pi() / 12)\n                           + (random() - 0.5) * 3)::numeric, 2)\n                ELSE\n                    round((50 + 8 * sin((extract(HOUR FROM ts) - 6) * pi() / 12)\n                           + (random() - 0.5) * 4)::numeric, 2)\n            END\n        -- Energy: peaks during business hours (8am-6pm)\n        WHEN 'energy' THEN\n            CASE\n                WHEN s.install_location = 'UPS System' THEN\n                    round((200 + 80 * GREATEST(0, sin((extract(HOUR FROM ts) - 6) * pi() / 12))\n                           + (random() - 0.5) * 20)::numeric, 2)\n                WHEN s.install_location = 'Cooling Unit' THEN\n                    round((150 + 100 * GREATEST(0, sin((extract(HOUR FROM ts) - 8) * pi() / 10))\n                           + (random() - 0.5) * 15)::numeric, 2)\n                ELSE\n                    round((120 + 60 * GREATEST(0, sin((extract(HOUR FROM ts) - 7) * pi() / 11))\n                           + (random() - 0.5) * 10)::numeric, 2)\n            END\n    END\nFROM sensors s\nCROSS JOIN generate_series(\n    now() - INTERVAL '30 days',\n    now(),\n    INTERVAL '15 minutes'\n) AS ts;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 2.4: Verify the data\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT count(*) AS total_readings FROM readings;\n\nSELECT s.sensor_type, count(*) AS readings, round(avg(r.value), 1) AS avg_value\nFROM readings r\nJOIN sensors s ON r.sensor_id = s.sensor_id\nGROUP BY s.sensor_type\nORDER BY readings DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see approximately 43,000 total readings across temperature, humidity, and energy sensors.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECoCo prompt:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EConnect to my iot_sensors Postgres instance and create a schema for\nIoT sensor data: buildings, sensors, and readings tables. Then generate\n30 days of realistic smart-building sensor readings at 15-minute\nintervals using generate_series(). Include temperature (with daily\ncycles), humidity, and energy (with business-hour peaks) sensors\nacross 3 buildings. Verify the data loaded correctly.\n\u003C/code\u003E\u003C/pre\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESet Up Networking and Dependencies\u003C/h2\u003E\n","\u003Cp\u003EThe container runtime needs an \u003Cstrong\u003EExternal Access Integration (EAI)\u003C/strong\u003E to make outbound connections to PyPI (for package installs) and to your Postgres instance. Cortex AI calls go through the Snowpark session internally, so no outbound egress rule is needed for Cortex.\u003C/p\u003E\n","\u003Ch3\u003EStep 3.1: Create the EAI\u003C/h3\u003E\n","\u003Cp\u003ERun this in a Snowsight SQL worksheet. Replace \u003Ccode\u003E<your-postgres-hostname>\u003C/code\u003E with your actual Postgres host from Step 2.3:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE NETWORK RULE iot_app_egress\n  MODE = EGRESS\n  TYPE = HOST_PORT\n  VALUE_LIST = (\n    'pypi.org',\n    'files.pythonhosted.org',\n    '<your-postgres-hostname>:5432'\n  );\n\nCREATE OR REPLACE EXTERNAL ACCESS INTEGRATION iot_app_eai\n  ALLOWED_NETWORK_RULES = (iot_app_egress)\n  ENABLED = TRUE;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECoCo prompt:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECreate an egress network rule that allows outbound access to PyPI\n(pypi.org, files.pythonhosted.org) and my iot_sensors Postgres host\non port 5432. Then create an External Access Integration called\niot_app_eai. My Postgres host is <paste-your-host-here>.\n\u003C/code\u003E\u003C/pre\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 3.2: Create the database and schema\u003C/h3\u003E\n","\u003Cp\u003EIf you don't already have a database and schema for the app:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE DATABASE IF NOT EXISTS iot_lab;\nCREATE SCHEMA IF NOT EXISTS iot_lab.sensors;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 3.3: Project structure\u003C/h3\u003E\n","\u003Cp\u003ECreate a project directory with this structure:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eiot-streamlit-dashboard/\n  streamlit_app.py          # Main entry point\n  data.py                   # Shared Postgres connection + query layer\n  cortex_ai.py              # Cortex AI helper functions\n  pyproject.toml            # Dependencies\n  snowflake.yml             # SiS deployment manifest\n  .streamlit/\n    secrets.toml            # Postgres connection credentials\n  app_pages/\n    home.py                 # Dashboard page\n    charts.py               # Charts on Demand page\n    agent.py                # AI Agent Search page\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 3.4: Add dependencies — \u003Ccode\u003Epyproject.toml\u003C/code\u003E\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-toml\"\u003E[project]\nname = "iot-streamlit-dashboard"\nversion = "0.1.0"\nrequires-python = ">=3.11"\n    dependencies = [\n        "streamlit>=1.54.0",\n        "psycopg2-binary>=2.9.10",\n        "snowflake-connector-python>=3.3.0",\n        "snowflake-ml-python>=1.7.0",\n        "altair>=5.5.0",\n        "pandas>=2.2.0",\n        "numpy>=1.26.0",\n    ]\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 3.5: Add connection secrets — \u003Ccode\u003E.streamlit/secrets.toml\u003C/code\u003E\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-toml\"\u003E[postgres]\nhost = "<your-postgres-hostname>"\nport = 5432\ndbname = "postgres"\nuser = "snowflake_admin"\npassword = "<your-password>"\n\n[snowflake_cortex]\nconnection_name = "<your-snowflake-connection>"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EReplace the placeholder values with your actual Postgres connection details (from Step 2.3) and your Snowflake connection name from \u003Ccode\u003E~/.snowflake/connections.toml\u003C/code\u003E.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EImportant:\u003C/strong\u003E The \u003Ccode\u003E[postgres]\u003C/code\u003E section is needed in both local development and SiS. The \u003Ccode\u003E[snowflake_cortex]\u003C/code\u003E section is only needed for local development — in SiS, Cortex AI calls go through \u003Ccode\u003Est.connection("snowflake")\u003C/code\u003E automatically. When deploying to SiS, you \u003Cstrong\u003Emust include \u003Ccode\u003E.streamlit/secrets.toml\u003C/code\u003E in your deployment artifacts\u003C/strong\u003E (see Step 9). Without it, psycopg2 will fail with \u003Ccode\u003Efe_sendauth: no password supplied\u003C/code\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 3.6: Deployment manifest — \u003Ccode\u003Esnowflake.yml\u003C/code\u003E\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003Edefinition_version: 2\nentities:\n  iot_streamlit_dashboard:\n    type: streamlit\n    identifier:\n      name: IOT_STREAMLIT_DASHBOARD\n      database: IOT_LAB\n      schema: SENSORS\n    query_warehouse: <your-warehouse>\n    compute_pool: <your-compute-pool>\n    runtime_name: SYSTEM$ST_CONTAINER_RUNTIME_PY3_11\n    external_access_integrations:\n      - IOT_APP_EAI\n    main_file: streamlit_app.py\n    artifacts:\n      - streamlit_app.py\n      - pyproject.toml\n      - data.py\n      - cortex_ai.py\n      - app_pages/home.py\n      - app_pages/charts.py\n      - app_pages/agent.py\n      - .streamlit/secrets.toml\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETip:\u003C/strong\u003E Notice that \u003Ccode\u003E.streamlit/secrets.toml\u003C/code\u003E is listed in \u003Ccode\u003Eartifacts\u003C/code\u003E. This is the key to making Postgres credentials available in the container runtime. Without this, \u003Ccode\u003Est.secrets["postgres"]\u003C/code\u003E will not find the password and the app will fail to connect.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECoCo prompt:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EI am building a Streamlit app in Snowflake (SiS) called IOT Streamlit Dashboard for Streamlit in Snowflake that connects\nto my iot_sensors Postgres instance. Create the project skeleton:\npyproject.toml (with streamlit, psycopg2-binary, snowflake-connector-python,\naltair, pandas, numpy), .streamlit/secrets.toml with [postgres] section,\nand snowflake.yml for SiS container runtime deployment with the EAI\niot_app_eai. Include .streamlit/secrets.toml in the deployment artifacts and use the saved Postgres credentials for the secrets file.\n\u003C/code\u003E\u003C/pre\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBuild the Data Layer\u003C/h2\u003E\n","\u003Cp\u003EThe data layer provides a shared Postgres connection and cached query functions used by all three pages.\u003C/p\u003E\n","\u003Ch3\u003E\u003Ccode\u003Edata.py\u003C/code\u003E\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E"""Shared database connection and query functions for the IOT dashboard."""\n\nimport psycopg2\nimport pandas as pd\nimport streamlit as st\n\n\n# ---------------------------------------------------------------------------\n# Postgres connection\n# ---------------------------------------------------------------------------\n\n@st.cache_resource\ndef _get_pg_connection():\n    """Create a persistent Postgres connection.\n\n    Reads credentials from .streamlit/secrets.toml (works both locally and in\n    SiS when the secrets file is included in the deployment artifacts).\n    """\n    pg = st.secrets["postgres"]\n    conn = psycopg2.connect(\n        host=pg["host"],\n        port=pg["port"],\n        dbname=pg["dbname"],\n        user=pg["user"],\n        password=pg["password"],\n        sslmode="require",\n    )\n    conn.autocommit = True\n    return conn\n\n\ndef run_pg_query(sql: str) -> pd.DataFrame:\n    """Run a SQL query against the Postgres instance and return a DataFrame."""\n    conn = _get_pg_connection()\n    if conn is None:\n        return pd.DataFrame()\n    try:\n        return pd.read_sql_query(sql, conn)\n    except Exception:\n        st.cache_resource.clear()\n        try:\n            conn = _get_pg_connection()\n            return pd.read_sql_query(sql, conn)\n        except Exception as retry_err:\n            st.error(f"Query failed: {retry_err}")\n            return pd.DataFrame()\n\n\n@st.cache_data(ttl=300)\ndef get_buildings() -> pd.DataFrame:\n    return run_pg_query("SELECT * FROM buildings ORDER BY building_id")\n\n\n@st.cache_data(ttl=300)\ndef get_sensors() -> pd.DataFrame:\n    return run_pg_query("SELECT * FROM sensors ORDER BY sensor_id")\n\n\n@st.cache_data(ttl=300)\ndef get_sensor_summary() -> pd.DataFrame:\n    return run_pg_query("""\n        SELECT\n            s.sensor_type,\n            s.unit,\n            COUNT(DISTINCT s.sensor_id) AS sensor_count,\n            COUNT(r.reading_id) AS total_readings,\n            ROUND(AVG(r.value)::numeric, 2) AS avg_value,\n            ROUND(MIN(r.value)::numeric, 2) AS min_value,\n            ROUND(MAX(r.value)::numeric, 2) AS max_value\n        FROM sensors s\n        JOIN readings r ON s.sensor_id = r.sensor_id\n        GROUP BY s.sensor_type, s.unit\n        ORDER BY s.sensor_type\n    """)\n\n\n@st.cache_data(ttl=300)\ndef get_building_summary() -> pd.DataFrame:\n    return run_pg_query("""\n        SELECT\n            b.name AS building,\n            b.location,\n            COUNT(DISTINCT s.sensor_id) AS sensors,\n            COUNT(r.reading_id) AS readings,\n            ROUND(AVG(r.value)::numeric, 2) AS avg_value\n        FROM buildings b\n        JOIN sensors s ON b.building_id = s.building_id\n        JOIN readings r ON s.sensor_id = r.sensor_id\n        GROUP BY b.name, b.location\n        ORDER BY b.name\n    """)\n\n\n@st.cache_data(ttl=300)\ndef get_daily_avg_by_type() -> pd.DataFrame:\n    return run_pg_query("""\n        SELECT\n            DATE(r.reading_time) AS day,\n            s.sensor_type,\n            ROUND(AVG(r.value)::numeric, 2) AS avg_value\n        FROM readings r\n        JOIN sensors s ON r.sensor_id = s.sensor_id\n        GROUP BY DATE(r.reading_time), s.sensor_type\n        ORDER BY day\n    """)\n\n\n@st.cache_data(ttl=300)\ndef get_hourly_avg(sensor_type: str, building: str | None = None) -> pd.DataFrame:\n    where = "AND s.sensor_type = %s"\n    params = [sensor_type]\n    if building:\n        where += " AND b.name = %s"\n        params.append(building)\n\n    sql = f"""\n        SELECT\n            DATE(r.reading_time) AS day,\n            EXTRACT(HOUR FROM r.reading_time)::int AS hour,\n            ROUND(AVG(r.value)::numeric, 2) AS avg_value\n        FROM readings r\n        JOIN sensors s ON r.sensor_id = s.sensor_id\n        JOIN buildings b ON s.building_id = b.building_id\n        WHERE 1=1 {where}\n        GROUP BY DATE(r.reading_time), EXTRACT(HOUR FROM r.reading_time)\n        ORDER BY day, hour\n    """\n    conn = _get_pg_connection()\n    try:\n        return pd.read_sql_query(sql, conn, params=params)\n    except Exception:\n        st.cache_resource.clear()\n        conn = _get_pg_connection()\n        return pd.read_sql_query(sql, conn, params=params)\n\n\n@st.cache_data(ttl=300)\ndef get_readings_by_sensor(sensor_type: str, building: str | None = None,\n                           days: int = 7) -> pd.DataFrame:\n    where = "AND s.sensor_type = %s"\n    params: list = [sensor_type]\n    if building:\n        where += " AND b.name = %s"\n        params.append(building)\n\n    sql = f"""\n        SELECT\n            r.reading_time,\n            s.install_location AS location,\n            b.name AS building,\n            r.value,\n            s.unit\n        FROM readings r\n        JOIN sensors s ON r.sensor_id = s.sensor_id\n        JOIN buildings b ON s.building_id = b.building_id\n        WHERE r.reading_time >= NOW() - INTERVAL '{days} days'\n        {where}\n        ORDER BY r.reading_time\n    """\n    conn = _get_pg_connection()\n    try:\n        return pd.read_sql_query(sql, conn, params=params)\n    except Exception:\n        st.cache_resource.clear()\n        conn = _get_pg_connection()\n        return pd.read_sql_query(sql, conn, params=params)\n\n\n@st.cache_data(ttl=300)\ndef get_latest_readings() -> pd.DataFrame:\n    return run_pg_query("""\n        SELECT DISTINCT ON (s.sensor_id)\n            b.name AS building,\n            s.sensor_type,\n            s.install_location AS location,\n            r.value,\n            s.unit,\n            r.reading_time AS last_reading\n        FROM readings r\n        JOIN sensors s ON r.sensor_id = s.sensor_id\n        JOIN buildings b ON s.building_id = b.building_id\n        ORDER BY s.sensor_id, r.reading_time DESC\n    """)\n\n\n@st.cache_data(ttl=300)\ndef get_readings_for_chart(sensor_type: str, building: str | None = None,\n                           days: int = 30) -> pd.DataFrame:\n    where = "AND s.sensor_type = %s"\n    params: list = [sensor_type]\n    if building:\n        where += " AND b.name = %s"\n        params.append(building)\n\n    sql = f"""\n        SELECT\n            r.reading_time,\n            b.name AS building,\n            s.install_location AS location,\n            r.value\n        FROM readings r\n        JOIN sensors s ON r.sensor_id = s.sensor_id\n        JOIN buildings b ON s.building_id = b.building_id\n        WHERE r.reading_time >= NOW() - INTERVAL '{days} days'\n        {where}\n        ORDER BY r.reading_time\n    """\n    conn = _get_pg_connection()\n    try:\n        return pd.read_sql_query(sql, conn, params=params)\n    except Exception:\n        st.cache_resource.clear()\n        conn = _get_pg_connection()\n        return pd.read_sql_query(sql, conn, params=params)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EKey design decisions:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003Est.secrets["postgres"]\u003C/code\u003E\u003C/strong\u003E reads from \u003Ccode\u003E.streamlit/secrets.toml\u003C/code\u003E — this works identically in local dev and in SiS when the file is deployed as an artifact\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003E@st.cache_resource\u003C/code\u003E\u003C/strong\u003E keeps a single persistent connection across reruns\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003E@st.cache_data(ttl=300)\u003C/code\u003E\u003C/strong\u003E caches query results for 5 minutes\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERetry logic\u003C/strong\u003E clears the cached connection and reconnects if a query fails (handles stale connections)\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECoCo prompt:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECreate a data.py module with a shared Postgres connection using\nst.secrets["postgres"] and psycopg2. Add cached query functions for:\nget_buildings, get_sensors, get_sensor_summary, get_building_summary,\nget_daily_avg_by_type, get_hourly_avg (with sensor_type and optional\nbuilding filters), get_readings_by_sensor, get_latest_readings, and\nget_readings_for_chart. Use @st.cache_data with 5-minute TTL and\nretry logic for stale connections.\n\u003C/code\u003E\u003C/pre\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBuild the Cortex AI Layer\u003C/h2\u003E\n","\u003Cp\u003EThe Cortex AI helper provides a dual-mode interface — it detects whether the app is running in SiS or locally and calls Cortex accordingly.\u003C/p\u003E\n","\u003Ch3\u003E\u003Ccode\u003Ecortex_ai.py\u003C/code\u003E\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E"""Shared Snowflake Cortex AI helper functions."""\n\nimport os\nimport streamlit as st\n\n\ndef _is_sis() -> bool:\n    """Return True if running inside Streamlit in Snowflake (container runtime)."""\n    return os.path.exists("/opt/streamlit-runtime")\n\n\ndef cortex_complete(prompt: str) -> str:\n    """Call Snowflake Cortex COMPLETE and return the response text."""\n    escaped = prompt.replace("\\\\", "\\\\\\\\").replace("'", "\\\\'")\n    sql = f"SELECT SNOWFLAKE.CORTEX.COMPLETE('llama3.1-70b', '{escaped}')"\n\n    if _is_sis():\n        conn = st.connection("snowflake")\n        df = conn.query(sql, ttl=0)\n        if not df.empty:\n            return df.iloc[0, 0]\n    else:\n        import snowflake.connector\n\n        @st.cache_resource\n        def _get_sf_conn():\n            conn_name = st.secrets["snowflake_cortex"]["connection_name"]\n            return snowflake.connector.connect(connection_name=conn_name)\n\n        conn = _get_sf_conn()\n        cursor = conn.cursor()\n        try:\n            cursor.execute(sql)\n            result = cursor.fetchone()\n        except Exception:\n            st.cache_resource.clear()\n            conn = _get_sf_conn()\n            cursor = conn.cursor()\n            cursor.execute(sql)\n            result = cursor.fetchone()\n        finally:\n            cursor.close()\n        if result and result[0]:\n            return result[0]\n\n    return "Sorry, I couldn't generate a response."\n\n\ndef cortex_complete_stream(prompt: str):\n    """Yield Cortex COMPLETE response in chunks for streaming UX."""\n    response = cortex_complete(prompt)\n    words = response.split(" ")\n    chunk = []\n    for word in words:\n        chunk.append(word)\n        if len(chunk) >= 3:\n            yield " ".join(chunk) + " "\n            chunk = []\n    if chunk:\n        yield " ".join(chunk)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EHow the dual-mode works:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EIn SiS (container runtime):\u003C/strong\u003E Detects SiS via \u003Ccode\u003E/opt/streamlit-runtime\u003C/code\u003E and calls Cortex through \u003Ccode\u003Est.connection("snowflake")\u003C/code\u003E, which uses the built-in Snowpark session. No extra credentials needed.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EIn local dev:\u003C/strong\u003E Uses \u003Ccode\u003Esnowflake-connector-python\u003C/code\u003E with \u003Ccode\u003Econnection_name\u003C/code\u003E from \u003Ccode\u003E~/.snowflake/connections.toml\u003C/code\u003E (supports OAuth, SSO, and other auth methods). The connection name is read from \u003Ccode\u003E.streamlit/secrets.toml\u003C/code\u003E.\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhy not \u003Ccode\u003Eget_active_session()\u003C/code\u003E?\u003C/strong\u003E The \u003Ccode\u003Esnowflake.snowpark.context.get_active_session()\u003C/code\u003E approach works in SiS but requires \u003Ccode\u003Esnowflake-snowpark-python\u003C/code\u003E as a dependency and doesn't work for local development. The dual-mode approach here works in both environments without Snowpark.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhy not \u003Ccode\u003E_snowflake\u003C/code\u003E?\u003C/strong\u003E The \u003Ccode\u003E_snowflake\u003C/code\u003E module is only available in classic SiS runtime, not container runtime. If you see \u003Ccode\u003EModuleNotFoundError: No module named '_snowflake'\u003C/code\u003E, you're on container runtime and should use \u003Ccode\u003Est.connection("snowflake")\u003C/code\u003E instead.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECoCo prompt:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E\u003C/code\u003E\u003C/pre\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode\u003E> Create a cortex_ai.py module with dual-mode Cortex AI support.\n> In SiS (detect via /opt/streamlit-runtime), use st.connection("snowflake").\n> Locally, use snowflake-connector-python with connection_name from\n> st.secrets["snowflake_cortex"]["connection_name"]. Include a\n> cortex_complete() function and a cortex_complete_stream() function\n> that yields word chunks for streaming UX. Use llama3.1-70b model.\n> Add snowflake-ml-python to pyproject.toml (provides snowflake.cortex).\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n\u003Cpre\u003E\u003Ccode\u003E\u003C/code\u003E\u003C/pre\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBuild the Dashboard Pages\u003C/h2\u003E\n","\u003Cp\u003ENow build the three app pages. The main entry point uses \u003Ccode\u003Est.navigation\u003C/code\u003E for top-bar navigation.\u003C/p\u003E\n","\u003Ch3\u003E\u003Ccode\u003Estreamlit_app.py\u003C/code\u003E — Main entry point\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E"""IOT Streamlit Dashboard - Main Entry Point"""\n\nimport streamlit as st\n\npage = st.navigation(\n    [\n        st.Page("app_pages/home.py", title="Dashboard", icon=":material/dashboard:"),\n        st.Page("app_pages/charts.py", title="Charts on Demand", icon=":material/bar_chart:"),\n        st.Page("app_pages/agent.py", title="AI Agent Search", icon=":material/smart_toy:"),\n    ],\n    position="top",\n)\n\npage.run()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003E\u003Ccode\u003Eapp_pages/home.py\u003C/code\u003E — Dashboard landing page\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E"""Landing page - KPI cards and overview charts."""\n\nimport pandas as pd\nimport altair as alt\nimport streamlit as st\nfrom data import (\n    get_sensor_summary,\n    get_building_summary,\n    get_daily_avg_by_type,\n    get_latest_readings,\n)\n\n# ---------------------------------------------------------------------------\n# Load data\n# ---------------------------------------------------------------------------\nsensor_summary = get_sensor_summary()\nbuilding_summary = get_building_summary()\ndaily_avg = get_daily_avg_by_type()\nlatest = get_latest_readings()\n\n# Cast numeric columns\nfor df in [sensor_summary, building_summary, daily_avg, latest]:\n    for col in df.columns:\n        if col in ("avg_value", "min_value", "max_value", "value", "sensor_count",\n                    "total_readings", "sensors", "readings"):\n            df[col] = pd.to_numeric(df[col], errors="coerce")\n\n# ---------------------------------------------------------------------------\n# Header\n# ---------------------------------------------------------------------------\nst.markdown("# :material/sensors: IOT Streamlit Dashboard")\n\n# ---------------------------------------------------------------------------\n# KPI row\n# ---------------------------------------------------------------------------\nif not sensor_summary.empty:\n    total_sensors = int(sensor_summary["sensor_count"].sum())\n    total_readings = int(sensor_summary["total_readings"].sum())\n    sensor_types = len(sensor_summary)\n\n    with st.container(horizontal=True):\n        st.metric("Total Sensors", total_sensors, border=True)\n        st.metric("Total Readings", f"{total_readings:,}", border=True)\n        st.metric("Sensor Types", sensor_types, border=True)\n        if not building_summary.empty:\n            st.metric("Buildings", len(building_summary), border=True)\n\n# ---------------------------------------------------------------------------\n# Sensor type summary cards\n# ---------------------------------------------------------------------------\nst.subheader("Sensor Overview")\n\nif not sensor_summary.empty:\n    cols = st.columns(len(sensor_summary))\n    for i, (_, row) in enumerate(sensor_summary.iterrows()):\n        with cols[i]:\n            with st.container(border=True):\n                st.markdown(f"**{row['sensor_type'].title()}** ({row['unit']})")\n                st.metric("Avg", f"{row['avg_value']:.1f}", border=True)\n                with st.container(horizontal=True):\n                    st.metric("Min", f"{row['min_value']:.1f}", border=True)\n                    st.metric("Max", f"{row['max_value']:.1f}", border=True)\n\n# ---------------------------------------------------------------------------\n# Daily trend chart\n# ---------------------------------------------------------------------------\nst.subheader("Daily Averages by Sensor Type")\n\nif not daily_avg.empty:\n    daily_avg["day"] = pd.to_datetime(daily_avg["day"])\n\n    chart = (\n        alt.Chart(daily_avg)\n        .mark_line(point=False)\n        .encode(\n            x=alt.X("day:T", title=None),\n            y=alt.Y("avg_value:Q", title="Average Value"),\n            color=alt.Color("sensor_type:N", title="Sensor Type"),\n            tooltip=[\n                alt.Tooltip("day:T", title="Date", format="%Y-%m-%d"),\n                alt.Tooltip("sensor_type:N", title="Type"),\n                alt.Tooltip("avg_value:Q", title="Avg Value", format=",.2f"),\n            ],\n        )\n        .properties(height=350)\n    )\n    st.altair_chart(chart)\n\n# ---------------------------------------------------------------------------\n# Building summary and latest readings\n# ---------------------------------------------------------------------------\ncol1, col2 = st.columns(2)\n\nwith col1:\n    st.subheader("Readings by Building")\n    if not building_summary.empty:\n        st.dataframe(\n            building_summary,\n            hide_index=True,\n            column_config={\n                "building": st.column_config.TextColumn("Building"),\n                "location": st.column_config.TextColumn("Location"),\n                "sensors": st.column_config.NumberColumn("Sensors"),\n                "readings": st.column_config.NumberColumn("Readings", format="%d"),\n                "avg_value": st.column_config.NumberColumn("Avg Value", format="%.2f"),\n            },\n        )\n\nwith col2:\n    st.subheader("Latest Sensor Readings")\n    if not latest.empty:\n        latest["last_reading"] = pd.to_datetime(latest["last_reading"])\n        st.dataframe(\n            latest,\n            hide_index=True,\n            column_config={\n                "building": st.column_config.TextColumn("Building"),\n                "sensor_type": st.column_config.TextColumn("Type"),\n                "location": st.column_config.TextColumn("Location"),\n                "value": st.column_config.NumberColumn("Value", format="%.2f"),\n                "unit": st.column_config.TextColumn("Unit"),\n                "last_reading": st.column_config.DatetimeColumn(\n                    "Last Reading", format="MMM DD, YYYY HH:mm"\n                ),\n            },\n        )\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECoCo prompt:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EBuild the Dashboard landing page (app_pages/home.py) for my IOT\nStreamlit Dashboard. It should show:\n1. KPI row with total sensors, total readings, sensor types, buildings\n2. Sensor overview cards showing avg/min/max for each sensor type\n3. An Altair line chart of daily averages by sensor type\n4. A two-column layout with building summary table and latest readings table\nImport data functions from the data.py module.\n\u003C/code\u003E\u003C/pre\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBuild the Charts on Demand Page\u003C/h2\u003E\n","\u003Cp\u003EThis page provides interactive filtering and a \u003Cstrong\u003Enatural language chart generator\u003C/strong\u003E where users describe the chart they want in plain English and Cortex AI builds it.\u003C/p\u003E\n","\u003Ch3\u003E\u003Ccode\u003Eapp_pages/charts.py\u003C/code\u003E\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E"""Charts on Demand - Interactive sensor data exploration."""\n\nimport pandas as pd\nimport altair as alt\nimport streamlit as st\nfrom data import get_buildings, get_readings_for_chart, get_hourly_avg\nfrom cortex_ai import cortex_complete_stream\n\n# ---------------------------------------------------------------------------\n# Header\n# ---------------------------------------------------------------------------\nst.markdown("# :material/bar_chart: Charts on Demand")\nst.caption("Select a sensor type, building, and time range to generate charts.")\n\n# ---------------------------------------------------------------------------\n# Sidebar filters\n# ---------------------------------------------------------------------------\nbuildings_df = get_buildings()\nbuilding_names = buildings_df["name"].tolist() if not buildings_df.empty else []\n\nwith st.container(horizontal=True):\n    sensor_type = st.selectbox(\n        "Sensor Type",\n        ["temperature", "humidity", "energy"],\n        key="chart_sensor_type",\n    )\n    building = st.selectbox(\n        "Building",\n        ["All Buildings"] + building_names,\n        key="chart_building",\n    )\n    days = st.selectbox(\n        "Time Range",\n        [7, 14, 30],\n        format_func=lambda d: f"Last {d} days",\n        key="chart_days",\n    )\n\nselected_building = None if building == "All Buildings" else building\n\nUNITS = {"temperature": "°F", "humidity": "%RH", "energy": "kW"}\nunit = UNITS.get(sensor_type, "")\n\n# ---------------------------------------------------------------------------\n# Time series chart\n# ---------------------------------------------------------------------------\nst.subheader(f"{sensor_type.title()} Over Time")\n\nreadings = get_readings_for_chart(sensor_type, selected_building, days)\n\nif readings.empty:\n    st.info("No data found for the selected filters.")\nelse:\n    readings["reading_time"] = pd.to_datetime(readings["reading_time"])\n    readings["value"] = pd.to_numeric(readings["value"], errors="coerce")\n\n    # Build label for each series: "Building - Location"\n    readings["series"] = readings["building"] + " - " + readings["location"]\n\n    line_chart = (\n        alt.Chart(readings)\n        .mark_line(opacity=0.8)\n        .encode(\n            x=alt.X("reading_time:T", title=None),\n            y=alt.Y("value:Q", title=f"{sensor_type.title()} ({unit})"),\n            color=alt.Color("series:N", title="Sensor"),\n            tooltip=[\n                alt.Tooltip("reading_time:T", title="Time", format="%Y-%m-%d %H:%M"),\n                alt.Tooltip("series:N", title="Sensor"),\n                alt.Tooltip("value:Q", title="Value", format=",.2f"),\n            ],\n        )\n        .properties(height=400)\n    )\n    st.altair_chart(line_chart)\n\n    # -------------------------------------------------------------------\n    # Summary statistics\n    # -------------------------------------------------------------------\n    st.subheader("Summary Statistics")\n    stats = (\n        readings.groupby("series")["value"]\n        .agg(["mean", "min", "max", "std", "count"])\n        .round(2)\n        .reset_index()\n    )\n    stats.columns = ["Sensor", "Mean", "Min", "Max", "Std Dev", "Readings"]\n    st.dataframe(stats, hide_index=True)\n\n# ---------------------------------------------------------------------------\n# Heatmap: hourly average\n# ---------------------------------------------------------------------------\nst.subheader(f"Hourly Average {sensor_type.title()} Heatmap")\n\nhourly = get_hourly_avg(sensor_type, selected_building)\n\nif hourly.empty:\n    st.info("No hourly data available.")\nelse:\n    hourly["day"] = pd.to_datetime(hourly["day"])\n    hourly["hour"] = pd.to_numeric(hourly["hour"], errors="coerce").astype(int)\n    hourly["avg_value"] = pd.to_numeric(hourly["avg_value"], errors="coerce")\n\n    heatmap = (\n        alt.Chart(hourly)\n        .mark_rect()\n        .encode(\n            x=alt.X("hour:O", title="Hour of Day"),\n            y=alt.Y("day:T", title=None),\n            color=alt.Color(\n                "avg_value:Q",\n                title=f"Avg {unit}",\n                scale=alt.Scale(scheme="redyellowgreen" if sensor_type == "temperature" else "blues"),\n            ),\n            tooltip=[\n                alt.Tooltip("day:T", title="Date", format="%Y-%m-%d"),\n                alt.Tooltip("hour:O", title="Hour"),\n                alt.Tooltip("avg_value:Q", title="Avg Value", format=",.2f"),\n            ],\n        )\n        .properties(height=400)\n    )\n    st.altair_chart(heatmap)\n\n# ---------------------------------------------------------------------------\n# Distribution chart\n# ---------------------------------------------------------------------------\nst.subheader(f"{sensor_type.title()} Value Distribution")\n\nif not readings.empty:\n    histogram = (\n        alt.Chart(readings)\n        .mark_bar(opacity=0.7)\n        .encode(\n            x=alt.X("value:Q", bin=alt.Bin(maxbins=40), title=f"{sensor_type.title()} ({unit})"),\n            y=alt.Y("count():Q", title="Frequency"),\n            color=alt.Color("building:N", title="Building"),\n            tooltip=[\n                alt.Tooltip("value:Q", bin=alt.Bin(maxbins=40), title="Value Range"),\n                alt.Tooltip("count():Q", title="Count"),\n            ],\n        )\n        .properties(height=300)\n    )\n    st.altair_chart(histogram)\n\n# ---------------------------------------------------------------------------\n# Natural Language Chart Generator\n# ---------------------------------------------------------------------------\nst.markdown("---")\nst.subheader(":material/smart_toy: Generate a Chart with AI")\nst.caption("Describe the chart you want in plain English and AI will build it for you.")\n\nNL_CHART_PROMPT = """You are a PostgreSQL query generator for an IoT sensor database.\nGiven a user's natural language request, generate ONLY a JSON object with these fields:\n- "sql": a valid PostgreSQL SELECT query\n- "chart_type": one of "line", "bar", "area", "scatter"\n- "x": the column name for the x-axis\n- "y": the column name(s) for the y-axis (string or list of strings)\n- "color": optional column name for color grouping (or null)\n- "title": a short chart title\n\nDATABASE SCHEMA:\n- buildings (building_id INT PK, name TEXT, location TEXT, floors INT)\n  Data: HQ Tower (Downtown Campus, 12 floors), Data Center (East Campus, 3 floors), Warehouse Alpha (West Campus, 1 floor)\n- sensors (sensor_id INT PK, building_id INT FK, sensor_type TEXT, unit TEXT, install_location TEXT, is_active BOOLEAN)\n  Types: temperature (°F), humidity (%RH), energy (kW). 15 sensors total.\n- readings (reading_id BIGINT PK, sensor_id INT FK, reading_time TIMESTAMP, value NUMERIC(10,2))\n  ~43,000 readings over 30 days.\n\nRULES:\n- Return ONLY valid JSON. No markdown, no explanation, no code fences.\n- Always alias columns to short readable names (e.g. AS day, AS avg_temp).\n- Use ROUND() for averages. Use DATE() or date_trunc() for time grouping.\n- Always ORDER BY the x-axis column.\n- Limit to 1000 rows max.\n\nUser request: {request}\n"""\n\nSUGGESTIONS_NL = [\n    "Average temperature per building over time",\n    "Daily energy usage comparison across buildings",\n    "Humidity distribution by building as a bar chart",\n    "Hourly temperature pattern for Data Center",\n    "Energy consumption trend last 7 days",\n]\n\nif "nl_chart_history" not in st.session_state:\n    st.session_state.nl_chart_history = []\n\n# Suggestion pills\nselected_suggestion = st.pills(\n    "Try these:",\n    SUGGESTIONS_NL,\n    label_visibility="collapsed",\n    key="nl_chart_suggestions",\n)\n\nnl_input = st.text_input(\n    "Describe the chart you want:",\n    value=selected_suggestion or "",\n    placeholder="e.g. Show me average temperature by building over time as a line chart",\n    key="nl_chart_input",\n)\n\nif st.button("Generate Chart", type="primary", key="nl_chart_btn") and nl_input:\n    import json\n    from data import run_pg_query\n    from cortex_ai import cortex_complete\n\n    with st.spinner("Generating chart..."):\n        prompt = NL_CHART_PROMPT.format(request=nl_input)\n        raw = cortex_complete(prompt)\n\n        # Parse JSON from response (strip any accidental markdown fences)\n        cleaned = raw.strip()\n        if cleaned.startswith("```"):\n            cleaned = cleaned.split("\\n", 1)[-1]\n        if cleaned.endswith("```"):\n            cleaned = cleaned.rsplit("```", 1)[0]\n        cleaned = cleaned.strip()\n\n        try:\n            spec = json.loads(cleaned)\n        except json.JSONDecodeError:\n            st.error("AI returned invalid JSON. Try rephrasing your request.")\n            st.code(raw, language="text")\n            spec = None\n\n    if spec:\n        sql = spec.get("sql", "")\n        chart_type = spec.get("chart_type", "line")\n        x_col = spec.get("x", "")\n        y_col = spec.get("y", "")\n        color_col = spec.get("color")\n        title = spec.get("title", nl_input)\n\n        with st.expander("Generated SQL", expanded=False):\n            st.code(sql, language="sql")\n\n        df_result = run_pg_query(sql)\n\n        if df_result.empty:\n            st.warning("Query returned no data. Try a different request.")\n        else:\n            # Cast numeric columns\n            if isinstance(y_col, list):\n                y_cols = y_col\n            else:\n                y_cols = [y_col]\n            for yc in y_cols:\n                if yc in df_result.columns:\n                    df_result[yc] = pd.to_numeric(df_result[yc], errors="coerce")\n            if x_col in df_result.columns:\n                # Try to parse as datetime\n                try:\n                    df_result[x_col] = pd.to_datetime(df_result[x_col])\n                except (ValueError, TypeError):\n                    pass\n\n            st.markdown(f"**{title}**")\n\n            if len(y_cols) == 1:\n                y_arg = y_cols[0]\n            else:\n                y_arg = y_cols\n\n            chart_args = {"x": x_col, "y": y_arg}\n            if color_col and color_col in df_result.columns:\n                chart_args["color"] = color_col\n\n            if chart_type == "bar":\n                st.bar_chart(df_result, **chart_args)\n            elif chart_type == "area":\n                st.area_chart(df_result, **chart_args)\n            elif chart_type == "scatter":\n                st.scatter_chart(df_result, **chart_args)\n            else:\n                st.line_chart(df_result, **chart_args)\n\n            with st.expander("View data", expanded=False):\n                st.dataframe(df_result, hide_index=True)\n\n            # Save to history\n            st.session_state.nl_chart_history.append(\n                {"request": nl_input, "title": title}\n            )\n\n# Show history\nif st.session_state.nl_chart_history:\n    with st.expander(f"Chart history ({len(st.session_state.nl_chart_history)})"):\n        for i, item in enumerate(reversed(st.session_state.nl_chart_history), 1):\n            st.markdown(f"{i}. **{item['title']}** -- _{item['request']}_")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EHow the NL chart generator works\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EThe user types a plain-English description (e.g. "Average temperature per building over time")\u003C/li\u003E\u003Cli\u003ECortex AI generates a JSON spec with the SQL query, chart type, axis columns, and title\u003C/li\u003E\u003Cli\u003EThe app runs the SQL against Postgres and renders the chart using Streamlit's built-in chart functions\u003C/li\u003E\u003Cli\u003EThe generated SQL is shown in a collapsed expander for transparency\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECoCo prompt:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EAdd a natural language chart generator to my Charts on Demand page.\nThe user describes a chart in plain English, Cortex AI generates a\nJSON object with {sql, chart_type, x, y, color, title}, the app\nruns the SQL against Postgres and renders the chart. Include suggestion\npills, a text input, and chart history. Use st.line_chart/bar_chart/\narea_chart/scatter_chart based on the chart_type.\n\u003C/code\u003E\u003C/pre\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBuild the AI Agent Search Page\u003C/h2\u003E\n","\u003Cp\u003EThe AI Agent Search page is the key differentiator of this app. Unlike traditional text-to-SQL chatbots that return SQL queries for the user to run, this chatbot \u003Cstrong\u003Epre-fetches live data from Postgres and injects it into the prompt\u003C/strong\u003E so the LLM can answer directly with real numbers.\u003C/p\u003E\n","\u003Ch3\u003E\u003Ccode\u003Eapp_pages/agent.py\u003C/code\u003E\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E"""AI Agent Search - Ask questions about your IoT sensor data using Cortex AI."""\n\nimport streamlit as st\nfrom data import run_pg_query\nfrom cortex_ai import cortex_complete_stream\n\n# ---------------------------------------------------------------------------\n# Pre-fetch live data summaries so the LLM can answer with real numbers\n# ---------------------------------------------------------------------------\n\n@st.cache_data(ttl=300)\ndef _build_data_context() -> str:\n    """Query the Postgres database and build a data context string for the LLM."""\n    parts = []\n\n    # Sensor summary\n    df = run_pg_query("""\n        SELECT s.sensor_type, s.unit,\n               COUNT(DISTINCT s.sensor_id) AS sensors,\n               COUNT(r.reading_id) AS readings,\n               ROUND(AVG(r.value)::numeric, 1) AS avg_val,\n               ROUND(MIN(r.value)::numeric, 1) AS min_val,\n               ROUND(MAX(r.value)::numeric, 1) AS max_val\n        FROM sensors s JOIN readings r ON s.sensor_id = r.sensor_id\n        GROUP BY s.sensor_type, s.unit ORDER BY s.sensor_type\n    """)\n    if not df.empty:\n        parts.append("SENSOR SUMMARY:\\n" + df.to_string(index=False))\n\n    # Building summary\n    df2 = run_pg_query("""\n        SELECT b.name AS building, b.location, b.floors,\n               COUNT(DISTINCT s.sensor_id) AS sensors,\n               COUNT(r.reading_id) AS readings\n        FROM buildings b\n        JOIN sensors s ON b.building_id = s.building_id\n        JOIN readings r ON s.sensor_id = r.sensor_id\n        GROUP BY b.name, b.location, b.floors ORDER BY b.name\n    """)\n    if not df2.empty:\n        parts.append("BUILDING SUMMARY:\\n" + df2.to_string(index=False))\n\n    # Latest readings per sensor\n    df3 = run_pg_query("""\n        SELECT DISTINCT ON (s.sensor_id)\n            b.name AS building, s.sensor_type, s.install_location,\n            r.value, s.unit, r.reading_time\n        FROM readings r\n        JOIN sensors s ON r.sensor_id = s.sensor_id\n        JOIN buildings b ON s.building_id = b.building_id\n        ORDER BY s.sensor_id, r.reading_time DESC\n    """)\n    if not df3.empty:\n        parts.append("LATEST READINGS (most recent per sensor):\\n" + df3.to_string(index=False))\n\n    # Daily averages last 7 days\n    df4 = run_pg_query("""\n        SELECT DATE(r.reading_time) AS day, s.sensor_type,\n               ROUND(AVG(r.value)::numeric, 1) AS avg_val\n        FROM readings r JOIN sensors s ON r.sensor_id = s.sensor_id\n        WHERE r.reading_time >= NOW() - INTERVAL '7 days'\n        GROUP BY DATE(r.reading_time), s.sensor_type\n        ORDER BY day, s.sensor_type\n    """)\n    if not df4.empty:\n        parts.append("DAILY AVERAGES (last 7 days):\\n" + df4.to_string(index=False))\n\n    # Building + type averages last 7 days\n    df5 = run_pg_query("""\n        SELECT b.name AS building, s.sensor_type, s.unit,\n               ROUND(AVG(r.value)::numeric, 1) AS avg_val,\n               ROUND(MIN(r.value)::numeric, 1) AS min_val,\n               ROUND(MAX(r.value)::numeric, 1) AS max_val\n        FROM readings r\n        JOIN sensors s ON r.sensor_id = s.sensor_id\n        JOIN buildings b ON s.building_id = b.building_id\n        WHERE r.reading_time >= NOW() - INTERVAL '7 days'\n        GROUP BY b.name, s.sensor_type, s.unit\n        ORDER BY b.name, s.sensor_type\n    """)\n    if not df5.empty:\n        parts.append("PER-BUILDING AVERAGES (last 7 days):\\n" + df5.to_string(index=False))\n\n    return "\\n\\n".join(parts)\n\n\nSYSTEM_PROMPT = """You are an IoT sensor data analyst assistant. You answer questions about\nbuilding sensor data using the LIVE DATA provided below. \n\nIMPORTANT RULES:\n- Answer directly with real numbers from the data. Do NOT write SQL queries.\n- Never suggest the user run a query. You have the data -- use it.\n- Give clear, conversational answers. Use specific values, buildings, and sensor locations.\n- Explain what the data means in practical building management terms.\n- If the user explicitly asks for a SQL query, then and only then provide PostgreSQL syntax.\n\nDATABASE SCHEMA (for reference only):\n- buildings: building_id, name, location, floors\n- sensors: sensor_id, building_id, sensor_type (temperature/humidity/energy), unit, install_location, is_active\n- readings: reading_id, sensor_id, reading_time, value\n\nLIVE DATA FROM THE DATABASE:\n{data_context}\n"""\n\n# ---------------------------------------------------------------------------\n# Suggestion chips\n# ---------------------------------------------------------------------------\nSUGGESTIONS = {\n    ":blue[:material/thermostat:] Temperature trends": "What are the temperature trends across buildings over the past week?",\n    ":green[:material/bolt:] Energy usage": "Which building uses the most energy and what patterns do you see?",\n    ":orange[:material/water_drop:] Humidity analysis": "Are there any concerning humidity readings in the Data Center server rooms?",\n    ":red[:material/query_stats:] Write a query": "Write a SQL query to find the top 5 highest temperature readings with their building and location.",\n}\n\n# ---------------------------------------------------------------------------\n# Page UI\n# ---------------------------------------------------------------------------\nst.markdown("# :material/smart_toy: AI Agent Search")\nst.caption("Ask questions about your IoT sensor data using Snowflake Cortex AI.")\n\n# Initialize chat history\nif "agent_messages" not in st.session_state:\n    st.session_state.agent_messages = []\n\n# Show suggestion chips before first message\nif not st.session_state.agent_messages:\n    selected = st.pills(\n        "Try asking:",\n        list(SUGGESTIONS.keys()),\n        label_visibility="collapsed",\n    )\n    if selected:\n        st.session_state.agent_messages.append(\n            {"role": "user", "content": SUGGESTIONS[selected]}\n        )\n        st.rerun()\n\n# Display chat history\nfor msg in st.session_state.agent_messages:\n    with st.chat_message(msg["role"]):\n        st.write(msg["content"])\n\n# Handle new input\nif prompt := st.chat_input("Ask about your IoT sensor data..."):\n    st.session_state.agent_messages.append({"role": "user", "content": prompt})\n\n    with st.chat_message("user"):\n        st.write(prompt)\n\n    with st.chat_message("assistant"):\n        data_context = _build_data_context()\n        system = SYSTEM_PROMPT.format(data_context=data_context)\n        conversation = system + "\\n\\n"\n        for msg in st.session_state.agent_messages:\n            role = "User" if msg["role"] == "user" else "Assistant"\n            conversation += f"{role}: {msg['content']}\\n\\n"\n        conversation += "Assistant:"\n\n        response = st.write_stream(cortex_complete_stream(conversation))\n\n    st.session_state.agent_messages.append(\n        {"role": "assistant", "content": response}\n    )\n\n# Sidebar info\nwith st.sidebar:\n    st.markdown("### About")\n    st.markdown(\n        "This AI agent uses **Snowflake Cortex** (Llama 3.1 70B) "\n        "to answer questions about the IoT sensor data in your "\n        "Postgres instance."\n    )\n    st.markdown("---")\n    st.markdown("**Data available:**")\n    st.markdown("- 3 buildings")\n    st.markdown("- 15 sensors (temp, humidity, energy)")\n    st.markdown("- ~43K readings over 30 days")\n    st.markdown("---")\n    if st.button(":material/restart_alt: Clear Chat", type="tertiary"):\n        st.session_state.agent_messages = []\n        st.rerun()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EHow the "answers first" approach works\u003C/h3\u003E\n","\u003Cp\u003EThe key insight is that instead of asking the LLM to generate SQL (which the user then has to run), we:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EPre-fetch live data\u003C/strong\u003E using \u003Ccode\u003E_build_data_context()\u003C/code\u003E — this runs 5 summary queries against Postgres and formats the results as plain text\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EInject the data into the system prompt\u003C/strong\u003E — the LLM receives real numbers alongside the schema\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EInstruct the LLM to answer directly\u003C/strong\u003E — the system prompt explicitly says "Answer directly with real numbers. Do NOT write SQL queries."\u003C/li\u003E\u003Cli\u003EThe LLM responds with conversational answers like "The average temperature in HQ Tower is 72.3°F" instead of \u003Ccode\u003ESELECT AVG(value) FROM readings...\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThis approach gives users immediate answers without needing to understand SQL, while still allowing them to request SQL queries when explicitly asked.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECoCo prompt:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EBuild an AI Agent Search page for my IoT dashboard. I want the AI to\ngive me direct answers with real numbers from the database, NOT SQL\nqueries. Pre-fetch live data from Postgres (sensor summary, building\nsummary, latest readings, daily averages, per-building averages) and\ninject it into the Cortex AI system prompt. Include suggestion chips,\nchat history, streaming responses, and a clear chat button in the sidebar.\nUse llama3.1-70b via the cortex_ai.py module.\n\u003C/code\u003E\u003C/pre\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EDeploy to Streamlit in Snowflake\u003C/h2\u003E\n","\u003Cp\u003ENow deploy your app to SiS using the Snow CLI.\u003C/p\u003E\n","\u003Ch3\u003EStep 9.1: Deploy\u003C/h3\u003E\n","\u003Cp\u003EFrom your project directory, run:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow streamlit deploy --replace --connection <your-connection-name>\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe first deployment creates the Streamlit app. Subsequent \u003Ccode\u003E--replace\u003C/code\u003E deployments update it in-place.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETip:\u003C/strong\u003E The \u003Ccode\u003E--connection\u003C/code\u003E flag specifies which Snowflake connection from \u003Ccode\u003E~/.snowflake/connections.toml\u003C/code\u003E to use for the deployment. This is the same connection name you use for local development.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 9.2: Verify\u003C/h3\u003E\n","\u003Cp\u003EAfter deployment, Snow CLI prints the app URL. Open it in your browser. The app should load with the Dashboard page showing KPIs and charts.\u003C/p\u003E\n","\u003Cp\u003EIf the app fails to start, check the compute pool:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EDESCRIBE COMPUTE POOL <your-compute-pool>;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf the pool is at capacity (\u003Ccode\u003Enum_services\u003C/code\u003E equals \u003Ccode\u003Emax_nodes\u003C/code\u003E), increase it:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER COMPUTE POOL <your-compute-pool> SET MAX_NODES = 3;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECoCo prompt:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EDeploy my IOT Streamlit Dashboard to SiS using snow streamlit deploy.\nUse the snowflake.yml manifest I already have. If the compute pool\nis full, increase max_nodes.\n\u003C/code\u003E\u003C/pre\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ENetworking\u003C/h2\u003E\n","\u003Cp\u003ETo limit network traffic to the Postgres instance, you might consider using a \u003Ccode\u003EHOST_PORT\u003C/code\u003E (public) egress rule on the EAI side and scope the \u003Ccode\u003EPOSTGRES_INGRESS\u003C/code\u003E rule to Snowflake's egress CIDRs from \u003Ccode\u003ESYSTEM$GET_SNOWFLAKE_EGRESS_IP_RANGES()\u003C/code\u003E. However, egress IPs for SPCS are shared across all accounts in the region and are not account-isolated. This approach is \u003Cstrong\u003Enot recommended for production Postgres instances\u003C/strong\u003E — use PrivateLink instead.\u003C/p\u003E\n","\u003Ch3\u003EPrivateLink\u003C/h3\u003E\n","\u003Cp\u003EStreamlit in Snowflake (SiS) can connect to a Snowflake Postgres instance using an External Access Integration (EAI) with PrivateLink, keeping all traffic off the public internet. This approach uses \u003Ccode\u003EPRIVATE_HOST_PORT\u003C/code\u003E network rules instead of \u003Ccode\u003EHOST_PORT\u003C/code\u003E, routing egress traffic from SiS through an AWS PrivateLink tunnel directly to the Postgres instance's VPC endpoint service.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Step 1: Enable PrivateLink on the Postgres instance (takes ~3-10 min)\nALTER POSTGRES INSTANCE "my_instance" ENABLE PRIVATELINK;\n\n-- Monitor until privatelink_service_identifier is non-NULL\nDESCRIBE POSTGRES INSTANCE "my_instance";\n\n-- Step 2: Provision outbound PrivateLink endpoint from Snowflake compute\nSELECT SYSTEM$PROVISION_PRIVATELINK_ENDPOINT(\n  '<privatelink_service_identifier>',   -- e.g. 'com.amazonaws.vpce.us-west-2.vpce-svc-...'\n  '<postgres_host>'                      -- e.g. 'abc123.account.us-west-2.aws.postgres.snowflake.app'\n);\n\n-- Step 3: Find and authorize the pending connection\nSHOW PRIVATELINK CONNECTIONS IN POSTGRES INSTANCE "my_instance";\n\nALTER POSTGRES INSTANCE "my_instance"\n  AUTHORIZE PRIVATELINK CONNECTIONS = ('<connection_id>');\n\n-- Step 4: Create PRIVATE_HOST_PORT network rule\nCREATE OR REPLACE NETWORK RULE my_db.my_schema.pg_privatelink_rule\n  MODE = EGRESS\n  TYPE = PRIVATE_HOST_PORT\n  VALUE_LIST = ('<postgres_host>:5432');\n\n-- Step 5: Create secret with Postgres credentials\nCREATE OR REPLACE SECRET my_db.my_schema.pg_secret\n  TYPE = GENERIC_STRING\n  SECRET_STRING = '<password>';\n\n-- Step 6: Create External Access Integration\nCREATE OR REPLACE EXTERNAL ACCESS INTEGRATION pg_privatelink_eai\n  ALLOWED_NETWORK_RULES = (my_db.my_schema.pg_privatelink_rule)\n  ALLOWED_AUTHENTICATION_SECRETS = (my_db.my_schema.pg_secret)\n  ENABLED = TRUE;\n\n-- Step 7: Create Streamlit app with the EAI\nCREATE OR REPLACE STREAMLIT my_db.my_schema.my_app\n  ROOT_LOCATION = '@my_db.my_schema.my_stage'\n  MAIN_FILE = 'app.py'\n  QUERY_WAREHOUSE = 'my_wh'\n  EXTERNAL_ACCESS_INTEGRATIONS = (pg_privatelink_eai)\n  SECRETS = ('pg_pass' = my_db.my_schema.pg_secret);\n\n-- Optional: Remove public network policy for PrivateLink-only access\nALTER POSTGRES INSTANCE "my_instance" UNSET NETWORK_POLICY;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECoCo prompt:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESet up a PrivateLink connection between my Snowflake Postgres instance\n"<instance_name>" and a Streamlit in Snowflake app. Use the snowflake-postgres\nskill. The steps are:\n\n1. Enable PrivateLink on the instance with ALTER POSTGRES INSTANCE ENABLE PRIVATELINK\n2. Wait for privatelink_service_identifier to populate via DESCRIBE\n3. Use SYSTEM$PROVISION_PRIVATELINK_ENDPOINT with the service identifier and\n   the PG host to create an outbound endpoint\n4. Authorize the pending connection with ALTER POSTGRES INSTANCE AUTHORIZE\n   PRIVATELINK CONNECTIONS\n5. Create a secret for the Postgres connection\n6. Create a PRIVATE_HOST_PORT (not HOST_PORT) egress network rule for the PG\n   host on port 5432\n7. Create an External Access Integration with the rule and secret\n8. Create or update the Streamlit app with the EAI attached\n9. (Optional) Remove any existing POSTGRES_INGRESS network policies so only PrivateLink\n   can connect\n\u003C/code\u003E\u003C/pre\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Ch3\u003EWhat You Built\u003C/h3\u003E\n","\u003Cp\u003EIn this quickstart, you built a complete IoT sensor monitoring application:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EA \u003Cstrong\u003ESnowflake Postgres instance\u003C/strong\u003E with a smart-building sensor schema and 30 days of realistic generated data\u003C/li\u003E\u003Cli\u003EA \u003Cstrong\u003Emulti-page Streamlit in Snowflake dashboard\u003C/strong\u003E with KPI metrics, interactive Altair charts, and building summary tables\u003C/li\u003E\u003Cli\u003EA \u003Cstrong\u003ECharts on Demand\u003C/strong\u003E page with interactive filters, heatmaps, distribution histograms, and a \u003Cstrong\u003Enatural language chart generator\u003C/strong\u003E powered by Cortex AI\u003C/li\u003E\u003Cli\u003EAn \u003Cstrong\u003EAI Agent Search\u003C/strong\u003E chatbot using Snowflake Cortex AI that answers questions about your sensor data in plain English using real numbers — not SQL queries\u003C/li\u003E\u003Cli\u003EA \u003Cstrong\u003ESnow CLI deployment\u003C/strong\u003E using \u003Ccode\u003Esnowflake.yml\u003C/code\u003E with container runtime, EAI, and secrets management\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to create and configure Snowflake Postgres instances with network policies\u003C/li\u003E\u003Cli\u003EHow to generate realistic time-series data with PostgreSQL's \u003Ccode\u003Egenerate_series()\u003C/code\u003E function\u003C/li\u003E\u003Cli\u003EHow to build a multi-page Streamlit in Snowflake app with \u003Ccode\u003Est.navigation\u003C/code\u003E and container runtime\u003C/li\u003E\u003Cli\u003EHow to connect to Postgres from container runtime using \u003Ccode\u003Epsycopg2\u003C/code\u003E and \u003Ccode\u003E.streamlit/secrets.toml\u003C/code\u003E as a deployment artifact\u003C/li\u003E\u003Cli\u003EHow to call Snowflake Cortex AI from both SiS and local development using dual-mode detection\u003C/li\u003E\u003Cli\u003EHow to build a natural language chart generator (NL to SQL to chart) with JSON spec output\u003C/li\u003E\u003Cli\u003EHow to build an AI chatbot that gives direct answers with real data by injecting live data context into the prompt\u003C/li\u003E\u003Cli\u003EHow to deploy using \u003Ccode\u003Esnow streamlit deploy\u003C/code\u003E with \u003Ccode\u003Esnowflake.yml\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EHow to troubleshoot common container runtime issues (\u003Ccode\u003E_snowflake\u003C/code\u003E module, secrets, compute pool capacity)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EFull CoCo Prompt (All-in-One)\u003C/h3\u003E\n","\u003Cp\u003EIf you prefer to give CoCo the entire app spec as a single prompt, here it is. This combines all the individual prompts above into one request:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EI want to build a complete IoT sensor monitoring app using Snowflake Postgres,\nStreamlit in Snowflake (container runtime), and Snowflake Cortex AI.\n\nINFRASTRUCTURE:\n- Create a network rule (iot_lab_ingress) and network policy (iot_lab_policy)\n  allowing all IPv4 ingress (0.0.0.0/0) for lab purposes.\n- Create a Snowflake Postgres instance called iot_sensors with STANDARD_M\n  compute, 10GB storage, AUTHENTICATION_AUTHORITY = POSTGRES, and the\n  iot_lab_policy network policy.\n- Create an egress network rule (iot_app_egress) allowing outbound access to\n  PyPI (pypi.org, files.pythonhosted.org) and my Postgres host on port 5432.\n  Then create an External Access Integration called iot_app_eai.\n\nDATA:\n- Connect to the iot_sensors Postgres instance and create tables: buildings\n  (building_id, name, location, floors), sensors (sensor_id, building_id,\n  sensor_type, unit, install_location, is_active), and readings (reading_id,\n  sensor_id, reading_time, value) with indexes on (sensor_id, reading_time)\n  and (reading_time).\n- Seed 3 buildings: HQ Tower (Downtown Campus, 12 floors), Data Center\n  (East Campus, 3 floors), Warehouse Alpha (West Campus, 1 floor).\n- Seed 15 sensors across all buildings covering temperature (°F), humidity\n  (%RH), and energy (kW).\n- Generate 30 days of realistic sensor readings at 15-minute intervals using\n  generate_series(). Temperature should follow daily sine-wave cycles (with\n  tight ranges for server rooms and cold storage), humidity should be inverse\n  of temperature, and energy should peak during business hours (8am-6pm).\n\nAPP STRUCTURE:\n- Multi-page Streamlit app using st.navigation with top-bar nav.\n- Project files: streamlit_app.py (entry point), data.py (shared Postgres\n  connection + cached query layer), cortex_ai.py (dual-mode Cortex AI helper),\n  pyproject.toml (dependencies), snowflake.yml (SiS deployment manifest),\n  .streamlit/secrets.toml (Postgres credentials), and app_pages/ directory\n  with home.py, charts.py, and agent.py.\n\nDATA LAYER (data.py):\n- Shared Postgres connection using psycopg2 and st.secrets["postgres"].\n- @st.cache_resource for the persistent connection, @st.cache_data(ttl=300)\n  for query results.\n- Retry logic that clears the cached connection and reconnects on failure.\n- Query functions: get_buildings, get_sensors, get_sensor_summary,\n  get_building_summary, get_daily_avg_by_type, get_hourly_avg (with\n  sensor_type and optional building filter), get_readings_by_sensor,\n  get_latest_readings, get_readings_for_chart, and run_pg_query.\n\nCORTEX AI LAYER (cortex_ai.py):\n- Dual-mode detection: in SiS (detect via /opt/streamlit-runtime) use\n  st.connection("snowflake"); locally use snowflake-connector-python with\n  connection_name from st.secrets["snowflake_cortex"]["connection_name"].\n- cortex_complete(prompt) function calling SNOWFLAKE.CORTEX.COMPLETE with\n  llama3.1-70b.\n- cortex_complete_stream(prompt) function that yields word chunks for\n  streaming UX.\n\nPAGE 1 — DASHBOARD (app_pages/home.py):\n- KPI row: total sensors, total readings, sensor types, buildings.\n- Sensor overview cards showing avg/min/max for each sensor type.\n- Altair line chart of daily averages by sensor type.\n- Two-column layout: building summary table and latest readings table.\n\nPAGE 2 — CHARTS ON DEMAND (app_pages/charts.py):\n- Filter bar: sensor type, building, time range (7/14/30 days).\n- Altair time-series line chart colored by sensor location.\n- Summary statistics table per sensor.\n- Hourly average heatmap (day × hour).\n- Value distribution histogram by building.\n- Natural language chart generator: user describes a chart in plain English,\n  Cortex AI returns a JSON spec {sql, chart_type, x, y, color, title}, the\n  app runs the SQL against Postgres and renders with st.line_chart/bar_chart/\n  area_chart/scatter_chart. Include suggestion pills and chart history.\n\nPAGE 3 — AI AGENT SEARCH (app_pages/agent.py):\n- "Answers first" approach: pre-fetch live data from Postgres (sensor summary,\n  building summary, latest readings, daily averages, per-building averages)\n  and inject it into the Cortex AI system prompt.\n- The LLM answers directly with real numbers. It does NOT write SQL queries\n  unless the user explicitly asks for one.\n- Chat UI with suggestion chips, streaming responses via st.write_stream,\n  chat history in session state, and a clear chat button in the sidebar.\n\nDEPLOYMENT:\n- snowflake.yml for SiS container runtime deployment with the iot_app_eai\n  External Access Integration.\n- Include .streamlit/secrets.toml in the deployment artifacts so the Postgres\n  password is available in the container runtime.\n- Deploy with: snow streamlit deploy --replace --connection <connection-name>\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECleanup\u003C/h3\u003E\n","\u003Cp\u003ETo remove the resources created in this guide:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EDROP POSTGRES INSTANCE IF EXISTS iot_sensors;\nDROP NETWORK POLICY IF EXISTS iot_lab_policy;\nDROP NETWORK RULE IF EXISTS iot_lab_ingress;\nDROP EXTERNAL ACCESS INTEGRATION IF EXISTS iot_app_eai;\nDROP NETWORK RULE IF EXISTS iot_app_egress;\n-- Drop the Streamlit app:\nDROP STREAMLIT IF EXISTS iot_lab.sensors.iot_streamlit_dashboard;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/LIMITEDACCESS/postgres-overview\"\u003ESnowflake Postgres Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/streamlit/about-streamlit\"\u003EStreamlit in Snowflake\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/llm-functions\"\u003ESnowflake Cortex AI Functions\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-cli/installation/installation\"\u003ESnow CLI Installation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code\"\u003ESnowflake CoCo\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"title":"Charts, Graphs, and AI for Postgres Data Using Streamlit and Cortex","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\nIn this quickstart, you will build a real-time IoT sensor dashboard powered by **Snowflake Postgres**, **Streamlit in Snowflake (SiS)**, and **Snowflake Cortex AI**. You will create a managed PostgreSQL instance, populate it with realistic smart-building sensor data, build interactive charts and filters, add a natural language chart generator, and wire up an AI chatbot that answers questions about your data in plain English using real numbers — all without leaving Snowflake.\n\nThis guide also shows you how to build the entire app using **Snowflake CoCo (CoCo)** prompts — Snowflake's AI coding assistant. Each section includes the exact CoCo prompt you can use to generate that section of the app or you can skip to the bottom for the full CoCo prompt.\n\n### What You Will Build\n- A **Snowflake Postgres instance** loaded with IoT sensor data (buildings, sensors, readings)\n- A **multi-page Streamlit dashboard** with KPI metrics, daily trend charts, and building summaries\n- A **Charts on Demand** page with interactive filters, heatmaps, distribution histograms, and a **natural language chart generator** powered by Cortex AI\n- An **AI Agent Search** chatbot that answers questions about your sensor data in plain English using real numbers from the database — not SQL queries\n- A **Snow CLI deployment** using `snowflake.yml` for reproducible SiS deployments\n\n### What You Will Learn\n- How to create and configure a Snowflake Postgres instance\n- How to generate realistic IoT data using PostgreSQL's `generate_series()` function\n- How to build a multi-page Streamlit in Snowflake app with container runtime and `psycopg2`\n- How to call Snowflake Cortex AI from both SiS (container runtime) and local development\n- How to build a natural language chart generator (NL to SQL to chart)\n- How to build an AI chatbot that gives direct answers with real data (not SQL queries) by injecting live data context into the prompt\n- How to deploy to SiS using `snow streamlit deploy` with `snowflake.yml`\n- How to pass Postgres credentials to container runtime using `.streamlit/secrets.toml` as a deployment artifact\n\n### Prerequisites\n- A role with the required privileges (see table below). **ACCOUNTADMIN** has all of these by default, but each can be granted individually to other roles.\n- **Cortex AI** access — your role needs the `SNOWFLAKE.CORTEX_USER` database role\n- A **warehouse** for the Streamlit app's query execution\n- **Snowflake CoCo CLI** (recommended, for AI-assisted development) — [getting started](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-cli)\n\n**Required privileges:**\n\n| Privilege | What it's for | Grant statement |\n|-----------|--------------|-----------------|\n| `CREATE POSTGRES INSTANCE ON ACCOUNT` | Create the Postgres instance | `GRANT CREATE POSTGRES INSTANCE ON ACCOUNT TO ROLE my_role;` |\n| `CREATE INTEGRATION ON ACCOUNT` | Create the External Access Integration | `GRANT CREATE INTEGRATION ON ACCOUNT TO ROLE my_role;` |\n| `CREATE NETWORK POLICY ON ACCOUNT` | Create network policies for Postgres ingress | `GRANT CREATE NETWORK POLICY ON ACCOUNT TO ROLE my_role;` |\n| `CREATE STREAMLIT ON SCHEMA` | Create the Streamlit app | `GRANT CREATE STREAMLIT ON SCHEMA my_db.my_schema TO ROLE my_role;` |\n| `SNOWFLAKE.CORTEX_USER` database role | Call Cortex AI LLM functions | `GRANT DATABASE ROLE SNOWFLAKE.CORTEX_USER TO ROLE my_role;` |\n\n\u003C!-- ------------------------ --\u003E\n## Create a Postgres Instance\n\nIn this step, you will create a managed PostgreSQL instance inside Snowflake and configure network access so your Streamlit app can connect to it.\n\n### Step 1.1: Configure network access\n\nRun the following in a **Snowsight SQL worksheet** with a role that has the required privileges (ACCOUNTADMIN works, or a custom role with the grants from the Prerequisites table):\n\n```sql\nCREATE NETWORK RULE IF NOT EXISTS iot_lab_ingress\n  TYPE = IPV4\n  VALUE_LIST = ('0.0.0.0/0')\n  MODE = POSTGRES_INGRESS;\n\nCREATE NETWORK POLICY IF NOT EXISTS iot_lab_policy\n  ALLOWED_NETWORK_RULE_LIST = ('iot_lab_ingress');\n```\n\n\u003E **Note:** The `0.0.0.0/0` rule allows connections from any IP address. This is fine for a lab environment. For production, restrict this to specific IP ranges or set up additional networking.\n\n\u003E **CoCo prompt:**\n\u003E\n\u003E ```\n\u003E Create a network rule and network policy for a Postgres instance.\n\u003E Allow all IPv4 ingress (0.0.0.0/0) for lab purposes.\n\u003E Name them iot_lab_ingress and iot_lab_policy.\n\u003E ```\n\n### Step 1.2: Create the Postgres instance\n\n```sql\nCREATE POSTGRES INSTANCE iot_sensors\n  COMPUTE_FAMILY = 'STANDARD_M'\n  STORAGE_SIZE_GB = 10\n  AUTHENTICATION_AUTHORITY = POSTGRES\n  NETWORK_POLICY = 'iot_lab_policy'\n  COMMENT = 'IoT sensor dashboard lab';\n```\n\n\u003E **Tip:** This creates two users: `snowflake_admin` and `application` with auto-generated passwords. **Copy the passwords immediately** — they are only shown once.\n\n### Step 1.3: Verify the instance is ready\n\n```sql\n-- Wait until state = READY (typically 3-5 minutes)\nDESCRIBE POSTGRES INSTANCE iot_sensors;\n```\n\n### Step 1.4: Connect and verify\n\nUsing psql (replace `\u003Chostname\u003E` and `\u003Cpassword\u003E` with your values):\n\n```bash\npsql \"postgresql://snowflake_admin:\u003Cpassword\u003E@\u003Chostname\u003E:5432/postgres?sslmode=require\"\n```\n\nRun a quick test:\n\n```sql\nSELECT version();\n```\n\nYou should see PostgreSQL 18 (or your selected version).\n\n\u003E **CoCo prompt:**\n\u003E\n\u003E ```\n\u003E Create a Snowflake Postgres instance called iot_sensors with STANDARD_M\n\u003E compute, 10GB storage, and the iot_lab_policy network policy. Then save\n\u003E the connection details when it is ready for building with this instance.\n\u003E ```\n\n\u003C!-- ------------------------ --\u003E\n## Generate IoT Sensor Data\n\nNow you will create the schema and generate realistic smart-building sensor data entirely with SQL — no external files or downloads needed.\n\n### Step 2.1: Create the schema\n\nConnect to your Postgres instance and run:\n\n```sql\n-- Buildings in the sensor network\nCREATE TABLE buildings (\n    building_id   SERIAL PRIMARY KEY,\n    name          TEXT NOT NULL,\n    location      TEXT NOT NULL,\n    floors        INT NOT NULL\n);\n\n-- Sensors installed in buildings\nCREATE TABLE sensors (\n    sensor_id        SERIAL PRIMARY KEY,\n    building_id      INT NOT NULL REFERENCES buildings(building_id),\n    sensor_type      TEXT NOT NULL,    -- 'temperature', 'humidity', 'energy'\n    unit             TEXT NOT NULL,    -- '°F', '%RH', 'kW'\n    install_location TEXT NOT NULL,    -- e.g. 'Floor 1 - Lobby'\n    is_active        BOOLEAN DEFAULT TRUE\n);\n\n-- Time-series sensor readings\nCREATE TABLE readings (\n    reading_id    BIGSERIAL PRIMARY KEY,\n    sensor_id     INT NOT NULL REFERENCES sensors(sensor_id),\n    reading_time  TIMESTAMP NOT NULL,\n    value         NUMERIC(10,2) NOT NULL\n);\n\n-- Index for efficient time-range queries\nCREATE INDEX idx_readings_sensor_time ON readings(sensor_id, reading_time);\nCREATE INDEX idx_readings_time ON readings(reading_time);\n```\n\n### Step 2.2: Seed buildings and sensors\n\n```sql\nINSERT INTO buildings (name, location, floors) VALUES\n    ('HQ Tower',        'Downtown Campus',  12),\n    ('Data Center',     'East Campus',       3),\n    ('Warehouse Alpha', 'West Campus',       1);\n\nINSERT INTO sensors (building_id, sensor_type, unit, install_location) VALUES\n    -- HQ Tower\n    (1, 'temperature', '°F',  'Floor 1 - Lobby'),\n    (1, 'temperature', '°F',  'Floor 6 - Office'),\n    (1, 'temperature', '°F',  'Floor 12 - Executive'),\n    (1, 'humidity',    '%RH', 'Floor 1 - Lobby'),\n    (1, 'humidity',    '%RH', 'Floor 6 - Office'),\n    (1, 'energy',      'kW',  'Main Panel'),\n    -- Data Center\n    (2, 'temperature', '°F',  'Server Room A'),\n    (2, 'temperature', '°F',  'Server Room B'),\n    (2, 'humidity',    '%RH', 'Server Room A'),\n    (2, 'energy',      'kW',  'UPS System'),\n    (2, 'energy',      'kW',  'Cooling Unit'),\n    -- Warehouse Alpha\n    (3, 'temperature', '°F',  'Loading Dock'),\n    (3, 'temperature', '°F',  'Cold Storage'),\n    (3, 'humidity',    '%RH', 'Cold Storage'),\n    (3, 'energy',      'kW',  'Main Panel');\n```\n\n### Step 2.3: Generate realistic sensor readings\n\nThis query generates 30 days of readings at 15-minute intervals for every sensor. The values follow realistic patterns — daily temperature cycles, stable data center temps, and energy usage that peaks during business hours:\n\n```sql\nINSERT INTO readings (sensor_id, reading_time, value)\nSELECT\n    s.sensor_id,\n    ts,\n    CASE s.sensor_type\n        -- Temperature: sine wave for daily cycle + random noise\n        WHEN 'temperature' THEN\n            CASE\n                -- Data center server rooms: tight 65-72°F range\n                WHEN s.install_location LIKE 'Server Room%' THEN\n                    round((68 + 3 * sin(extract(HOUR FROM ts) * pi() / 12)\n                           + (random() - 0.5) * 2)::numeric, 2)\n                -- Cold storage: 34-38°F\n                WHEN s.install_location = 'Cold Storage' THEN\n                    round((36 + 1.5 * sin(extract(HOUR FROM ts) * pi() / 12)\n                           + (random() - 0.5) * 1)::numeric, 2)\n                -- Office/lobby: 68-78°F daily cycle\n                ELSE\n                    round((72 + 4 * sin((extract(HOUR FROM ts) - 6) * pi() / 12)\n                           + (random() - 0.5) * 3)::numeric, 2)\n            END\n        -- Humidity: inverse of temperature pattern\n        WHEN 'humidity' THEN\n            CASE\n                WHEN s.install_location LIKE 'Server Room%' THEN\n                    round((45 + 3 * sin(extract(HOUR FROM ts) * pi() / 12)\n                           + (random() - 0.5) * 2)::numeric, 2)\n                WHEN s.install_location = 'Cold Storage' THEN\n                    round((80 + 5 * sin(extract(HOUR FROM ts) * pi() / 12)\n                           + (random() - 0.5) * 3)::numeric, 2)\n                ELSE\n                    round((50 + 8 * sin((extract(HOUR FROM ts) - 6) * pi() / 12)\n                           + (random() - 0.5) * 4)::numeric, 2)\n            END\n        -- Energy: peaks during business hours (8am-6pm)\n        WHEN 'energy' THEN\n            CASE\n                WHEN s.install_location = 'UPS System' THEN\n                    round((200 + 80 * GREATEST(0, sin((extract(HOUR FROM ts) - 6) * pi() / 12))\n                           + (random() - 0.5) * 20)::numeric, 2)\n                WHEN s.install_location = 'Cooling Unit' THEN\n                    round((150 + 100 * GREATEST(0, sin((extract(HOUR FROM ts) - 8) * pi() / 10))\n                           + (random() - 0.5) * 15)::numeric, 2)\n                ELSE\n                    round((120 + 60 * GREATEST(0, sin((extract(HOUR FROM ts) - 7) * pi() / 11))\n                           + (random() - 0.5) * 10)::numeric, 2)\n            END\n    END\nFROM sensors s\nCROSS JOIN generate_series(\n    now() - INTERVAL '30 days',\n    now(),\n    INTERVAL '15 minutes'\n) AS ts;\n```\n\n### Step 2.4: Verify the data\n\n```sql\nSELECT count(*) AS total_readings FROM readings;\n\nSELECT s.sensor_type, count(*) AS readings, round(avg(r.value), 1) AS avg_value\nFROM readings r\nJOIN sensors s ON r.sensor_id = s.sensor_id\nGROUP BY s.sensor_type\nORDER BY readings DESC;\n```\n\nYou should see approximately 43,000 total readings across temperature, humidity, and energy sensors.\n\n\u003E **CoCo prompt:**\n\u003E\n\u003E ```\n\u003E Connect to my iot_sensors Postgres instance and create a schema for\n\u003E IoT sensor data: buildings, sensors, and readings tables. Then generate\n\u003E 30 days of realistic smart-building sensor readings at 15-minute\n\u003E intervals using generate_series(). Include temperature (with daily\n\u003E cycles), humidity, and energy (with business-hour peaks) sensors\n\u003E across 3 buildings. Verify the data loaded correctly.\n\u003E ```\n\n\u003C!-- ------------------------ --\u003E\n## Set Up Networking and Dependencies\n\nThe container runtime needs an **External Access Integration (EAI)** to make outbound connections to PyPI (for package installs) and to your Postgres instance. Cortex AI calls go through the Snowpark session internally, so no outbound egress rule is needed for Cortex.\n\n### Step 3.1: Create the EAI\n\nRun this in a Snowsight SQL worksheet. Replace `\u003Cyour-postgres-hostname\u003E` with your actual Postgres host from Step 2.3:\n\n```sql\nCREATE OR REPLACE NETWORK RULE iot_app_egress\n  MODE = EGRESS\n  TYPE = HOST_PORT\n  VALUE_LIST = (\n    'pypi.org',\n    'files.pythonhosted.org',\n    '\u003Cyour-postgres-hostname\u003E:5432'\n  );\n\nCREATE OR REPLACE EXTERNAL ACCESS INTEGRATION iot_app_eai\n  ALLOWED_NETWORK_RULES = (iot_app_egress)\n  ENABLED = TRUE;\n```\n\n\u003E **CoCo prompt:**\n\u003E\n\u003E ```\n\u003E Create an egress network rule that allows outbound access to PyPI\n\u003E (pypi.org, files.pythonhosted.org) and my iot_sensors Postgres host\n\u003E on port 5432. Then create an External Access Integration called\n\u003E iot_app_eai. My Postgres host is \u003Cpaste-your-host-here\u003E.\n\u003E ```\n\n### Step 3.2: Create the database and schema\n\nIf you don't already have a database and schema for the app:\n\n```sql\nCREATE DATABASE IF NOT EXISTS iot_lab;\nCREATE SCHEMA IF NOT EXISTS iot_lab.sensors;\n```\n\n### Step 3.3: Project structure\n\nCreate a project directory with this structure:\n\n```\niot-streamlit-dashboard/\n  streamlit_app.py          # Main entry point\n  data.py                   # Shared Postgres connection + query layer\n  cortex_ai.py              # Cortex AI helper functions\n  pyproject.toml            # Dependencies\n  snowflake.yml             # SiS deployment manifest\n  .streamlit/\n    secrets.toml            # Postgres connection credentials\n  app_pages/\n    home.py                 # Dashboard page\n    charts.py               # Charts on Demand page\n    agent.py                # AI Agent Search page\n```\n\n### Step 3.4: Add dependencies — `pyproject.toml`\n\n```toml\n[project]\nname = \"iot-streamlit-dashboard\"\nversion = \"0.1.0\"\nrequires-python = \"\u003E=3.11\"\n    dependencies = [\n        \"streamlit\u003E=1.54.0\",\n        \"psycopg2-binary\u003E=2.9.10\",\n        \"snowflake-connector-python\u003E=3.3.0\",\n        \"snowflake-ml-python\u003E=1.7.0\",\n        \"altair\u003E=5.5.0\",\n        \"pandas\u003E=2.2.0\",\n        \"numpy\u003E=1.26.0\",\n    ]\n```\n\n### Step 3.5: Add connection secrets — `.streamlit/secrets.toml`\n\n```toml\n[postgres]\nhost = \"\u003Cyour-postgres-hostname\u003E\"\nport = 5432\ndbname = \"postgres\"\nuser = \"snowflake_admin\"\npassword = \"\u003Cyour-password\u003E\"\n\n[snowflake_cortex]\nconnection_name = \"\u003Cyour-snowflake-connection\u003E\"\n```\n\nReplace the placeholder values with your actual Postgres connection details (from Step 2.3) and your Snowflake connection name from `~/.snowflake/connections.toml`.\n\n\u003E **Important:** The `[postgres]` section is needed in both local development and SiS. The `[snowflake_cortex]` section is only needed for local development — in SiS, Cortex AI calls go through `st.connection(\"snowflake\")` automatically. When deploying to SiS, you **must include `.streamlit/secrets.toml` in your deployment artifacts** (see Step 9). Without it, psycopg2 will fail with `fe_sendauth: no password supplied`.\n\n### Step 3.6: Deployment manifest — `snowflake.yml`\n\n```yaml\ndefinition_version: 2\nentities:\n  iot_streamlit_dashboard:\n    type: streamlit\n    identifier:\n      name: IOT_STREAMLIT_DASHBOARD\n      database: IOT_LAB\n      schema: SENSORS\n    query_warehouse: \u003Cyour-warehouse\u003E\n    compute_pool: \u003Cyour-compute-pool\u003E\n    runtime_name: SYSTEM$ST_CONTAINER_RUNTIME_PY3_11\n    external_access_integrations:\n      - IOT_APP_EAI\n    main_file: streamlit_app.py\n    artifacts:\n      - streamlit_app.py\n      - pyproject.toml\n      - data.py\n      - cortex_ai.py\n      - app_pages/home.py\n      - app_pages/charts.py\n      - app_pages/agent.py\n      - .streamlit/secrets.toml\n```\n\n\u003E **Tip:** Notice that `.streamlit/secrets.toml` is listed in `artifacts`. This is the key to making Postgres credentials available in the container runtime. Without this, `st.secrets[\"postgres\"]` will not find the password and the app will fail to connect.\n\n\u003E **CoCo prompt:**\n\u003E\n\u003E ```\n\u003E I am building a Streamlit app in Snowflake (SiS) called IOT Streamlit Dashboard for Streamlit in Snowflake that connects\n\u003E to my iot_sensors Postgres instance. Create the project skeleton:\n\u003E pyproject.toml (with streamlit, psycopg2-binary, snowflake-connector-python,\n\u003E altair, pandas, numpy), .streamlit/secrets.toml with [postgres] section,\n\u003E and snowflake.yml for SiS container runtime deployment with the EAI\n\u003E iot_app_eai. Include .streamlit/secrets.toml in the deployment artifacts and use the saved Postgres credentials for the secrets file.\n\u003E ```\n\n\u003C!-- ------------------------ --\u003E\n## Build the Data Layer\n\nThe data layer provides a shared Postgres connection and cached query functions used by all three pages.\n\n### `data.py`\n\n```python\n\"\"\"Shared database connection and query functions for the IOT dashboard.\"\"\"\n\nimport psycopg2\nimport pandas as pd\nimport streamlit as st\n\n\n# ---------------------------------------------------------------------------\n# Postgres connection\n# ---------------------------------------------------------------------------\n\n@st.cache_resource\ndef _get_pg_connection():\n    \"\"\"Create a persistent Postgres connection.\n\n    Reads credentials from .streamlit/secrets.toml (works both locally and in\n    SiS when the secrets file is included in the deployment artifacts).\n    \"\"\"\n    pg = st.secrets[\"postgres\"]\n    conn = psycopg2.connect(\n        host=pg[\"host\"],\n        port=pg[\"port\"],\n        dbname=pg[\"dbname\"],\n        user=pg[\"user\"],\n        password=pg[\"password\"],\n        sslmode=\"require\",\n    )\n    conn.autocommit = True\n    return conn\n\n\ndef run_pg_query(sql: str) -\u003E pd.DataFrame:\n    \"\"\"Run a SQL query against the Postgres instance and return a DataFrame.\"\"\"\n    conn = _get_pg_connection()\n    if conn is None:\n        return pd.DataFrame()\n    try:\n        return pd.read_sql_query(sql, conn)\n    except Exception:\n        st.cache_resource.clear()\n        try:\n            conn = _get_pg_connection()\n            return pd.read_sql_query(sql, conn)\n        except Exception as retry_err:\n            st.error(f\"Query failed: {retry_err}\")\n            return pd.DataFrame()\n\n\n@st.cache_data(ttl=300)\ndef get_buildings() -\u003E pd.DataFrame:\n    return run_pg_query(\"SELECT * FROM buildings ORDER BY building_id\")\n\n\n@st.cache_data(ttl=300)\ndef get_sensors() -\u003E pd.DataFrame:\n    return run_pg_query(\"SELECT * FROM sensors ORDER BY sensor_id\")\n\n\n@st.cache_data(ttl=300)\ndef get_sensor_summary() -\u003E pd.DataFrame:\n    return run_pg_query(\"\"\"\n        SELECT\n            s.sensor_type,\n            s.unit,\n            COUNT(DISTINCT s.sensor_id) AS sensor_count,\n            COUNT(r.reading_id) AS total_readings,\n            ROUND(AVG(r.value)::numeric, 2) AS avg_value,\n            ROUND(MIN(r.value)::numeric, 2) AS min_value,\n            ROUND(MAX(r.value)::numeric, 2) AS max_value\n        FROM sensors s\n        JOIN readings r ON s.sensor_id = r.sensor_id\n        GROUP BY s.sensor_type, s.unit\n        ORDER BY s.sensor_type\n    \"\"\")\n\n\n@st.cache_data(ttl=300)\ndef get_building_summary() -\u003E pd.DataFrame:\n    return run_pg_query(\"\"\"\n        SELECT\n            b.name AS building,\n            b.location,\n            COUNT(DISTINCT s.sensor_id) AS sensors,\n            COUNT(r.reading_id) AS readings,\n            ROUND(AVG(r.value)::numeric, 2) AS avg_value\n        FROM buildings b\n        JOIN sensors s ON b.building_id = s.building_id\n        JOIN readings r ON s.sensor_id = r.sensor_id\n        GROUP BY b.name, b.location\n        ORDER BY b.name\n    \"\"\")\n\n\n@st.cache_data(ttl=300)\ndef get_daily_avg_by_type() -\u003E pd.DataFrame:\n    return run_pg_query(\"\"\"\n        SELECT\n            DATE(r.reading_time) AS day,\n            s.sensor_type,\n            ROUND(AVG(r.value)::numeric, 2) AS avg_value\n        FROM readings r\n        JOIN sensors s ON r.sensor_id = s.sensor_id\n        GROUP BY DATE(r.reading_time), s.sensor_type\n        ORDER BY day\n    \"\"\")\n\n\n@st.cache_data(ttl=300)\ndef get_hourly_avg(sensor_type: str, building: str | None = None) -\u003E pd.DataFrame:\n    where = \"AND s.sensor_type = %s\"\n    params = [sensor_type]\n    if building:\n        where += \" AND b.name = %s\"\n        params.append(building)\n\n    sql = f\"\"\"\n        SELECT\n            DATE(r.reading_time) AS day,\n            EXTRACT(HOUR FROM r.reading_time)::int AS hour,\n            ROUND(AVG(r.value)::numeric, 2) AS avg_value\n        FROM readings r\n        JOIN sensors s ON r.sensor_id = s.sensor_id\n        JOIN buildings b ON s.building_id = b.building_id\n        WHERE 1=1 {where}\n        GROUP BY DATE(r.reading_time), EXTRACT(HOUR FROM r.reading_time)\n        ORDER BY day, hour\n    \"\"\"\n    conn = _get_pg_connection()\n    try:\n        return pd.read_sql_query(sql, conn, params=params)\n    except Exception:\n        st.cache_resource.clear()\n        conn = _get_pg_connection()\n        return pd.read_sql_query(sql, conn, params=params)\n\n\n@st.cache_data(ttl=300)\ndef get_readings_by_sensor(sensor_type: str, building: str | None = None,\n                           days: int = 7) -\u003E pd.DataFrame:\n    where = \"AND s.sensor_type = %s\"\n    params: list = [sensor_type]\n    if building:\n        where += \" AND b.name = %s\"\n        params.append(building)\n\n    sql = f\"\"\"\n        SELECT\n            r.reading_time,\n            s.install_location AS location,\n            b.name AS building,\n            r.value,\n            s.unit\n        FROM readings r\n        JOIN sensors s ON r.sensor_id = s.sensor_id\n        JOIN buildings b ON s.building_id = b.building_id\n        WHERE r.reading_time \u003E= NOW() - INTERVAL '{days} days'\n        {where}\n        ORDER BY r.reading_time\n    \"\"\"\n    conn = _get_pg_connection()\n    try:\n        return pd.read_sql_query(sql, conn, params=params)\n    except Exception:\n        st.cache_resource.clear()\n        conn = _get_pg_connection()\n        return pd.read_sql_query(sql, conn, params=params)\n\n\n@st.cache_data(ttl=300)\ndef get_latest_readings() -\u003E pd.DataFrame:\n    return run_pg_query(\"\"\"\n        SELECT DISTINCT ON (s.sensor_id)\n            b.name AS building,\n            s.sensor_type,\n            s.install_location AS location,\n            r.value,\n            s.unit,\n            r.reading_time AS last_reading\n        FROM readings r\n        JOIN sensors s ON r.sensor_id = s.sensor_id\n        JOIN buildings b ON s.building_id = b.building_id\n        ORDER BY s.sensor_id, r.reading_time DESC\n    \"\"\")\n\n\n@st.cache_data(ttl=300)\ndef get_readings_for_chart(sensor_type: str, building: str | None = None,\n                           days: int = 30) -\u003E pd.DataFrame:\n    where = \"AND s.sensor_type = %s\"\n    params: list = [sensor_type]\n    if building:\n        where += \" AND b.name = %s\"\n        params.append(building)\n\n    sql = f\"\"\"\n        SELECT\n            r.reading_time,\n            b.name AS building,\n            s.install_location AS location,\n            r.value\n        FROM readings r\n        JOIN sensors s ON r.sensor_id = s.sensor_id\n        JOIN buildings b ON s.building_id = b.building_id\n        WHERE r.reading_time \u003E= NOW() - INTERVAL '{days} days'\n        {where}\n        ORDER BY r.reading_time\n    \"\"\"\n    conn = _get_pg_connection()\n    try:\n        return pd.read_sql_query(sql, conn, params=params)\n    except Exception:\n        st.cache_resource.clear()\n        conn = _get_pg_connection()\n        return pd.read_sql_query(sql, conn, params=params)\n```\n\nKey design decisions:\n- **`st.secrets[\"postgres\"]`** reads from `.streamlit/secrets.toml` — this works identically in local dev and in SiS when the file is deployed as an artifact\n- **`@st.cache_resource`** keeps a single persistent connection across reruns\n- **`@st.cache_data(ttl=300)`** caches query results for 5 minutes\n- **Retry logic** clears the cached connection and reconnects if a query fails (handles stale connections)\n\n\u003E **CoCo prompt:**\n\u003E\n\u003E ```\n\u003E Create a data.py module with a shared Postgres connection using\n\u003E st.secrets[\"postgres\"] and psycopg2. Add cached query functions for:\n\u003E get_buildings, get_sensors, get_sensor_summary, get_building_summary,\n\u003E get_daily_avg_by_type, get_hourly_avg (with sensor_type and optional\n\u003E building filters), get_readings_by_sensor, get_latest_readings, and\n\u003E get_readings_for_chart. Use @st.cache_data with 5-minute TTL and\n\u003E retry logic for stale connections.\n\u003E ```\n\n\u003C!-- ------------------------ --\u003E\n## Build the Cortex AI Layer\n\nThe Cortex AI helper provides a dual-mode interface — it detects whether the app is running in SiS or locally and calls Cortex accordingly.\n\n### `cortex_ai.py`\n\n```python\n\"\"\"Shared Snowflake Cortex AI helper functions.\"\"\"\n\nimport os\nimport streamlit as st\n\n\ndef _is_sis() -\u003E bool:\n    \"\"\"Return True if running inside Streamlit in Snowflake (container runtime).\"\"\"\n    return os.path.exists(\"/opt/streamlit-runtime\")\n\n\ndef cortex_complete(prompt: str) -\u003E str:\n    \"\"\"Call Snowflake Cortex COMPLETE and return the response text.\"\"\"\n    escaped = prompt.replace(\"\\\\\", \"\\\\\\\\\").replace(\"'\", \"\\\\'\")\n    sql = f\"SELECT SNOWFLAKE.CORTEX.COMPLETE('llama3.1-70b', '{escaped}')\"\n\n    if _is_sis():\n        conn = st.connection(\"snowflake\")\n        df = conn.query(sql, ttl=0)\n        if not df.empty:\n            return df.iloc[0, 0]\n    else:\n        import snowflake.connector\n\n        @st.cache_resource\n        def _get_sf_conn():\n            conn_name = st.secrets[\"snowflake_cortex\"][\"connection_name\"]\n            return snowflake.connector.connect(connection_name=conn_name)\n\n        conn = _get_sf_conn()\n        cursor = conn.cursor()\n        try:\n            cursor.execute(sql)\n            result = cursor.fetchone()\n        except Exception:\n            st.cache_resource.clear()\n            conn = _get_sf_conn()\n            cursor = conn.cursor()\n            cursor.execute(sql)\n            result = cursor.fetchone()\n        finally:\n            cursor.close()\n        if result and result[0]:\n            return result[0]\n\n    return \"Sorry, I couldn't generate a response.\"\n\n\ndef cortex_complete_stream(prompt: str):\n    \"\"\"Yield Cortex COMPLETE response in chunks for streaming UX.\"\"\"\n    response = cortex_complete(prompt)\n    words = response.split(\" \")\n    chunk = []\n    for word in words:\n        chunk.append(word)\n        if len(chunk) \u003E= 3:\n            yield \" \".join(chunk) + \" \"\n            chunk = []\n    if chunk:\n        yield \" \".join(chunk)\n```\n\nHow the dual-mode works:\n\n- **In SiS (container runtime):** Detects SiS via `/opt/streamlit-runtime` and calls Cortex through `st.connection(\"snowflake\")`, which uses the built-in Snowpark session. No extra credentials needed.\n- **In local dev:** Uses `snowflake-connector-python` with `connection_name` from `~/.snowflake/connections.toml` (supports OAuth, SSO, and other auth methods). The connection name is read from `.streamlit/secrets.toml`.\n\n\u003E **Why not `get_active_session()`?** The `snowflake.snowpark.context.get_active_session()` approach works in SiS but requires `snowflake-snowpark-python` as a dependency and doesn't work for local development. The dual-mode approach here works in both environments without Snowpark.\n\n\u003E **Why not `_snowflake`?** The `_snowflake` module is only available in classic SiS runtime, not container runtime. If you see `ModuleNotFoundError: No module named '_snowflake'`, you're on container runtime and should use `st.connection(\"snowflake\")` instead.\n\n\u003E **CoCo prompt:**\n\u003E\n\u003E ```\n    \u003E Create a cortex_ai.py module with dual-mode Cortex AI support.\n    \u003E In SiS (detect via /opt/streamlit-runtime), use st.connection(\"snowflake\").\n    \u003E Locally, use snowflake-connector-python with connection_name from\n    \u003E st.secrets[\"snowflake_cortex\"][\"connection_name\"]. Include a\n    \u003E cortex_complete() function and a cortex_complete_stream() function\n    \u003E that yields word chunks for streaming UX. Use llama3.1-70b model.\n    \u003E Add snowflake-ml-python to pyproject.toml (provides snowflake.cortex).\n\u003E ```\n\n\u003C!-- ------------------------ --\u003E\n## Build the Dashboard Pages\n\nNow build the three app pages. The main entry point uses `st.navigation` for top-bar navigation.\n\n### `streamlit_app.py` — Main entry point\n\n```python\n\"\"\"IOT Streamlit Dashboard - Main Entry Point\"\"\"\n\nimport streamlit as st\n\npage = st.navigation(\n    [\n        st.Page(\"app_pages/home.py\", title=\"Dashboard\", icon=\":material/dashboard:\"),\n        st.Page(\"app_pages/charts.py\", title=\"Charts on Demand\", icon=\":material/bar_chart:\"),\n        st.Page(\"app_pages/agent.py\", title=\"AI Agent Search\", icon=\":material/smart_toy:\"),\n    ],\n    position=\"top\",\n)\n\npage.run()\n```\n\n### `app_pages/home.py` — Dashboard landing page\n\n```python\n\"\"\"Landing page - KPI cards and overview charts.\"\"\"\n\nimport pandas as pd\nimport altair as alt\nimport streamlit as st\nfrom data import (\n    get_sensor_summary,\n    get_building_summary,\n    get_daily_avg_by_type,\n    get_latest_readings,\n)\n\n# ---------------------------------------------------------------------------\n# Load data\n# ---------------------------------------------------------------------------\nsensor_summary = get_sensor_summary()\nbuilding_summary = get_building_summary()\ndaily_avg = get_daily_avg_by_type()\nlatest = get_latest_readings()\n\n# Cast numeric columns\nfor df in [sensor_summary, building_summary, daily_avg, latest]:\n    for col in df.columns:\n        if col in (\"avg_value\", \"min_value\", \"max_value\", \"value\", \"sensor_count\",\n                    \"total_readings\", \"sensors\", \"readings\"):\n            df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n\n# ---------------------------------------------------------------------------\n# Header\n# ---------------------------------------------------------------------------\nst.markdown(\"# :material/sensors: IOT Streamlit Dashboard\")\n\n# ---------------------------------------------------------------------------\n# KPI row\n# ---------------------------------------------------------------------------\nif not sensor_summary.empty:\n    total_sensors = int(sensor_summary[\"sensor_count\"].sum())\n    total_readings = int(sensor_summary[\"total_readings\"].sum())\n    sensor_types = len(sensor_summary)\n\n    with st.container(horizontal=True):\n        st.metric(\"Total Sensors\", total_sensors, border=True)\n        st.metric(\"Total Readings\", f\"{total_readings:,}\", border=True)\n        st.metric(\"Sensor Types\", sensor_types, border=True)\n        if not building_summary.empty:\n            st.metric(\"Buildings\", len(building_summary), border=True)\n\n# ---------------------------------------------------------------------------\n# Sensor type summary cards\n# ---------------------------------------------------------------------------\nst.subheader(\"Sensor Overview\")\n\nif not sensor_summary.empty:\n    cols = st.columns(len(sensor_summary))\n    for i, (_, row) in enumerate(sensor_summary.iterrows()):\n        with cols[i]:\n            with st.container(border=True):\n                st.markdown(f\"**{row['sensor_type'].title()}** ({row['unit']})\")\n                st.metric(\"Avg\", f\"{row['avg_value']:.1f}\", border=True)\n                with st.container(horizontal=True):\n                    st.metric(\"Min\", f\"{row['min_value']:.1f}\", border=True)\n                    st.metric(\"Max\", f\"{row['max_value']:.1f}\", border=True)\n\n# ---------------------------------------------------------------------------\n# Daily trend chart\n# ---------------------------------------------------------------------------\nst.subheader(\"Daily Averages by Sensor Type\")\n\nif not daily_avg.empty:\n    daily_avg[\"day\"] = pd.to_datetime(daily_avg[\"day\"])\n\n    chart = (\n        alt.Chart(daily_avg)\n        .mark_line(point=False)\n        .encode(\n            x=alt.X(\"day:T\", title=None),\n            y=alt.Y(\"avg_value:Q\", title=\"Average Value\"),\n            color=alt.Color(\"sensor_type:N\", title=\"Sensor Type\"),\n            tooltip=[\n                alt.Tooltip(\"day:T\", title=\"Date\", format=\"%Y-%m-%d\"),\n                alt.Tooltip(\"sensor_type:N\", title=\"Type\"),\n                alt.Tooltip(\"avg_value:Q\", title=\"Avg Value\", format=\",.2f\"),\n            ],\n        )\n        .properties(height=350)\n    )\n    st.altair_chart(chart)\n\n# ---------------------------------------------------------------------------\n# Building summary and latest readings\n# ---------------------------------------------------------------------------\ncol1, col2 = st.columns(2)\n\nwith col1:\n    st.subheader(\"Readings by Building\")\n    if not building_summary.empty:\n        st.dataframe(\n            building_summary,\n            hide_index=True,\n            column_config={\n                \"building\": st.column_config.TextColumn(\"Building\"),\n                \"location\": st.column_config.TextColumn(\"Location\"),\n                \"sensors\": st.column_config.NumberColumn(\"Sensors\"),\n                \"readings\": st.column_config.NumberColumn(\"Readings\", format=\"%d\"),\n                \"avg_value\": st.column_config.NumberColumn(\"Avg Value\", format=\"%.2f\"),\n            },\n        )\n\nwith col2:\n    st.subheader(\"Latest Sensor Readings\")\n    if not latest.empty:\n        latest[\"last_reading\"] = pd.to_datetime(latest[\"last_reading\"])\n        st.dataframe(\n            latest,\n            hide_index=True,\n            column_config={\n                \"building\": st.column_config.TextColumn(\"Building\"),\n                \"sensor_type\": st.column_config.TextColumn(\"Type\"),\n                \"location\": st.column_config.TextColumn(\"Location\"),\n                \"value\": st.column_config.NumberColumn(\"Value\", format=\"%.2f\"),\n                \"unit\": st.column_config.TextColumn(\"Unit\"),\n                \"last_reading\": st.column_config.DatetimeColumn(\n                    \"Last Reading\", format=\"MMM DD, YYYY HH:mm\"\n                ),\n            },\n        )\n```\n\n\u003E **CoCo prompt:**\n\u003E\n\u003E ```\n\u003E Build the Dashboard landing page (app_pages/home.py) for my IOT\n\u003E Streamlit Dashboard. It should show:\n\u003E 1. KPI row with total sensors, total readings, sensor types, buildings\n\u003E 2. Sensor overview cards showing avg/min/max for each sensor type\n\u003E 3. An Altair line chart of daily averages by sensor type\n\u003E 4. A two-column layout with building summary table and latest readings table\n\u003E Import data functions from the data.py module.\n\u003E ```\n\n\u003C!-- ------------------------ --\u003E\n## Build the Charts on Demand Page\n\nThis page provides interactive filtering and a **natural language chart generator** where users describe the chart they want in plain English and Cortex AI builds it.\n\n### `app_pages/charts.py`\n\n```python\n\"\"\"Charts on Demand - Interactive sensor data exploration.\"\"\"\n\nimport pandas as pd\nimport altair as alt\nimport streamlit as st\nfrom data import get_buildings, get_readings_for_chart, get_hourly_avg\nfrom cortex_ai import cortex_complete_stream\n\n# ---------------------------------------------------------------------------\n# Header\n# ---------------------------------------------------------------------------\nst.markdown(\"# :material/bar_chart: Charts on Demand\")\nst.caption(\"Select a sensor type, building, and time range to generate charts.\")\n\n# ---------------------------------------------------------------------------\n# Sidebar filters\n# ---------------------------------------------------------------------------\nbuildings_df = get_buildings()\nbuilding_names = buildings_df[\"name\"].tolist() if not buildings_df.empty else []\n\nwith st.container(horizontal=True):\n    sensor_type = st.selectbox(\n        \"Sensor Type\",\n        [\"temperature\", \"humidity\", \"energy\"],\n        key=\"chart_sensor_type\",\n    )\n    building = st.selectbox(\n        \"Building\",\n        [\"All Buildings\"] + building_names,\n        key=\"chart_building\",\n    )\n    days = st.selectbox(\n        \"Time Range\",\n        [7, 14, 30],\n        format_func=lambda d: f\"Last {d} days\",\n        key=\"chart_days\",\n    )\n\nselected_building = None if building == \"All Buildings\" else building\n\nUNITS = {\"temperature\": \"°F\", \"humidity\": \"%RH\", \"energy\": \"kW\"}\nunit = UNITS.get(sensor_type, \"\")\n\n# ---------------------------------------------------------------------------\n# Time series chart\n# ---------------------------------------------------------------------------\nst.subheader(f\"{sensor_type.title()} Over Time\")\n\nreadings = get_readings_for_chart(sensor_type, selected_building, days)\n\nif readings.empty:\n    st.info(\"No data found for the selected filters.\")\nelse:\n    readings[\"reading_time\"] = pd.to_datetime(readings[\"reading_time\"])\n    readings[\"value\"] = pd.to_numeric(readings[\"value\"], errors=\"coerce\")\n\n    # Build label for each series: \"Building - Location\"\n    readings[\"series\"] = readings[\"building\"] + \" - \" + readings[\"location\"]\n\n    line_chart = (\n        alt.Chart(readings)\n        .mark_line(opacity=0.8)\n        .encode(\n            x=alt.X(\"reading_time:T\", title=None),\n            y=alt.Y(\"value:Q\", title=f\"{sensor_type.title()} ({unit})\"),\n            color=alt.Color(\"series:N\", title=\"Sensor\"),\n            tooltip=[\n                alt.Tooltip(\"reading_time:T\", title=\"Time\", format=\"%Y-%m-%d %H:%M\"),\n                alt.Tooltip(\"series:N\", title=\"Sensor\"),\n                alt.Tooltip(\"value:Q\", title=\"Value\", format=\",.2f\"),\n            ],\n        )\n        .properties(height=400)\n    )\n    st.altair_chart(line_chart)\n\n    # -------------------------------------------------------------------\n    # Summary statistics\n    # -------------------------------------------------------------------\n    st.subheader(\"Summary Statistics\")\n    stats = (\n        readings.groupby(\"series\")[\"value\"]\n        .agg([\"mean\", \"min\", \"max\", \"std\", \"count\"])\n        .round(2)\n        .reset_index()\n    )\n    stats.columns = [\"Sensor\", \"Mean\", \"Min\", \"Max\", \"Std Dev\", \"Readings\"]\n    st.dataframe(stats, hide_index=True)\n\n# ---------------------------------------------------------------------------\n# Heatmap: hourly average\n# ---------------------------------------------------------------------------\nst.subheader(f\"Hourly Average {sensor_type.title()} Heatmap\")\n\nhourly = get_hourly_avg(sensor_type, selected_building)\n\nif hourly.empty:\n    st.info(\"No hourly data available.\")\nelse:\n    hourly[\"day\"] = pd.to_datetime(hourly[\"day\"])\n    hourly[\"hour\"] = pd.to_numeric(hourly[\"hour\"], errors=\"coerce\").astype(int)\n    hourly[\"avg_value\"] = pd.to_numeric(hourly[\"avg_value\"], errors=\"coerce\")\n\n    heatmap = (\n        alt.Chart(hourly)\n        .mark_rect()\n        .encode(\n            x=alt.X(\"hour:O\", title=\"Hour of Day\"),\n            y=alt.Y(\"day:T\", title=None),\n            color=alt.Color(\n                \"avg_value:Q\",\n                title=f\"Avg {unit}\",\n                scale=alt.Scale(scheme=\"redyellowgreen\" if sensor_type == \"temperature\" else \"blues\"),\n            ),\n            tooltip=[\n                alt.Tooltip(\"day:T\", title=\"Date\", format=\"%Y-%m-%d\"),\n                alt.Tooltip(\"hour:O\", title=\"Hour\"),\n                alt.Tooltip(\"avg_value:Q\", title=\"Avg Value\", format=\",.2f\"),\n            ],\n        )\n        .properties(height=400)\n    )\n    st.altair_chart(heatmap)\n\n# ---------------------------------------------------------------------------\n# Distribution chart\n# ---------------------------------------------------------------------------\nst.subheader(f\"{sensor_type.title()} Value Distribution\")\n\nif not readings.empty:\n    histogram = (\n        alt.Chart(readings)\n        .mark_bar(opacity=0.7)\n        .encode(\n            x=alt.X(\"value:Q\", bin=alt.Bin(maxbins=40), title=f\"{sensor_type.title()} ({unit})\"),\n            y=alt.Y(\"count():Q\", title=\"Frequency\"),\n            color=alt.Color(\"building:N\", title=\"Building\"),\n            tooltip=[\n                alt.Tooltip(\"value:Q\", bin=alt.Bin(maxbins=40), title=\"Value Range\"),\n                alt.Tooltip(\"count():Q\", title=\"Count\"),\n            ],\n        )\n        .properties(height=300)\n    )\n    st.altair_chart(histogram)\n\n# ---------------------------------------------------------------------------\n# Natural Language Chart Generator\n# ---------------------------------------------------------------------------\nst.markdown(\"---\")\nst.subheader(\":material/smart_toy: Generate a Chart with AI\")\nst.caption(\"Describe the chart you want in plain English and AI will build it for you.\")\n\nNL_CHART_PROMPT = \"\"\"You are a PostgreSQL query generator for an IoT sensor database.\nGiven a user's natural language request, generate ONLY a JSON object with these fields:\n- \"sql\": a valid PostgreSQL SELECT query\n- \"chart_type\": one of \"line\", \"bar\", \"area\", \"scatter\"\n- \"x\": the column name for the x-axis\n- \"y\": the column name(s) for the y-axis (string or list of strings)\n- \"color\": optional column name for color grouping (or null)\n- \"title\": a short chart title\n\nDATABASE SCHEMA:\n- buildings (building_id INT PK, name TEXT, location TEXT, floors INT)\n  Data: HQ Tower (Downtown Campus, 12 floors), Data Center (East Campus, 3 floors), Warehouse Alpha (West Campus, 1 floor)\n- sensors (sensor_id INT PK, building_id INT FK, sensor_type TEXT, unit TEXT, install_location TEXT, is_active BOOLEAN)\n  Types: temperature (°F), humidity (%RH), energy (kW). 15 sensors total.\n- readings (reading_id BIGINT PK, sensor_id INT FK, reading_time TIMESTAMP, value NUMERIC(10,2))\n  ~43,000 readings over 30 days.\n\nRULES:\n- Return ONLY valid JSON. No markdown, no explanation, no code fences.\n- Always alias columns to short readable names (e.g. AS day, AS avg_temp).\n- Use ROUND() for averages. Use DATE() or date_trunc() for time grouping.\n- Always ORDER BY the x-axis column.\n- Limit to 1000 rows max.\n\nUser request: {request}\n\"\"\"\n\nSUGGESTIONS_NL = [\n    \"Average temperature per building over time\",\n    \"Daily energy usage comparison across buildings\",\n    \"Humidity distribution by building as a bar chart\",\n    \"Hourly temperature pattern for Data Center\",\n    \"Energy consumption trend last 7 days\",\n]\n\nif \"nl_chart_history\" not in st.session_state:\n    st.session_state.nl_chart_history = []\n\n# Suggestion pills\nselected_suggestion = st.pills(\n    \"Try these:\",\n    SUGGESTIONS_NL,\n    label_visibility=\"collapsed\",\n    key=\"nl_chart_suggestions\",\n)\n\nnl_input = st.text_input(\n    \"Describe the chart you want:\",\n    value=selected_suggestion or \"\",\n    placeholder=\"e.g. Show me average temperature by building over time as a line chart\",\n    key=\"nl_chart_input\",\n)\n\nif st.button(\"Generate Chart\", type=\"primary\", key=\"nl_chart_btn\") and nl_input:\n    import json\n    from data import run_pg_query\n    from cortex_ai import cortex_complete\n\n    with st.spinner(\"Generating chart...\"):\n        prompt = NL_CHART_PROMPT.format(request=nl_input)\n        raw = cortex_complete(prompt)\n\n        # Parse JSON from response (strip any accidental markdown fences)\n        cleaned = raw.strip()\n        if cleaned.startswith(\"```\"):\n            cleaned = cleaned.split(\"\\n\", 1)[-1]\n        if cleaned.endswith(\"```\"):\n            cleaned = cleaned.rsplit(\"```\", 1)[0]\n        cleaned = cleaned.strip()\n\n        try:\n            spec = json.loads(cleaned)\n        except json.JSONDecodeError:\n            st.error(\"AI returned invalid JSON. Try rephrasing your request.\")\n            st.code(raw, language=\"text\")\n            spec = None\n\n    if spec:\n        sql = spec.get(\"sql\", \"\")\n        chart_type = spec.get(\"chart_type\", \"line\")\n        x_col = spec.get(\"x\", \"\")\n        y_col = spec.get(\"y\", \"\")\n        color_col = spec.get(\"color\")\n        title = spec.get(\"title\", nl_input)\n\n        with st.expander(\"Generated SQL\", expanded=False):\n            st.code(sql, language=\"sql\")\n\n        df_result = run_pg_query(sql)\n\n        if df_result.empty:\n            st.warning(\"Query returned no data. Try a different request.\")\n        else:\n            # Cast numeric columns\n            if isinstance(y_col, list):\n                y_cols = y_col\n            else:\n                y_cols = [y_col]\n            for yc in y_cols:\n                if yc in df_result.columns:\n                    df_result[yc] = pd.to_numeric(df_result[yc], errors=\"coerce\")\n            if x_col in df_result.columns:\n                # Try to parse as datetime\n                try:\n                    df_result[x_col] = pd.to_datetime(df_result[x_col])\n                except (ValueError, TypeError):\n                    pass\n\n            st.markdown(f\"**{title}**\")\n\n            if len(y_cols) == 1:\n                y_arg = y_cols[0]\n            else:\n                y_arg = y_cols\n\n            chart_args = {\"x\": x_col, \"y\": y_arg}\n            if color_col and color_col in df_result.columns:\n                chart_args[\"color\"] = color_col\n\n            if chart_type == \"bar\":\n                st.bar_chart(df_result, **chart_args)\n            elif chart_type == \"area\":\n                st.area_chart(df_result, **chart_args)\n            elif chart_type == \"scatter\":\n                st.scatter_chart(df_result, **chart_args)\n            else:\n                st.line_chart(df_result, **chart_args)\n\n            with st.expander(\"View data\", expanded=False):\n                st.dataframe(df_result, hide_index=True)\n\n            # Save to history\n            st.session_state.nl_chart_history.append(\n                {\"request\": nl_input, \"title\": title}\n            )\n\n# Show history\nif st.session_state.nl_chart_history:\n    with st.expander(f\"Chart history ({len(st.session_state.nl_chart_history)})\"):\n        for i, item in enumerate(reversed(st.session_state.nl_chart_history), 1):\n            st.markdown(f\"{i}. **{item['title']}** -- _{item['request']}_\")\n```\n\n### How the NL chart generator works\n\n1. The user types a plain-English description (e.g. \"Average temperature per building over time\")\n2. Cortex AI generates a JSON spec with the SQL query, chart type, axis columns, and title\n3. The app runs the SQL against Postgres and renders the chart using Streamlit's built-in chart functions\n4. The generated SQL is shown in a collapsed expander for transparency\n\n\u003E **CoCo prompt:**\n\u003E\n\u003E ```\n\u003E Add a natural language chart generator to my Charts on Demand page.\n\u003E The user describes a chart in plain English, Cortex AI generates a\n\u003E JSON object with {sql, chart_type, x, y, color, title}, the app\n\u003E runs the SQL against Postgres and renders the chart. Include suggestion\n\u003E pills, a text input, and chart history. Use st.line_chart/bar_chart/\n\u003E area_chart/scatter_chart based on the chart_type.\n\u003E ```\n\n\u003C!-- ------------------------ --\u003E\n## Build the AI Agent Search Page\n\nThe AI Agent Search page is the key differentiator of this app. Unlike traditional text-to-SQL chatbots that return SQL queries for the user to run, this chatbot **pre-fetches live data from Postgres and injects it into the prompt** so the LLM can answer directly with real numbers.\n\n### `app_pages/agent.py`\n\n```python\n\"\"\"AI Agent Search - Ask questions about your IoT sensor data using Cortex AI.\"\"\"\n\nimport streamlit as st\nfrom data import run_pg_query\nfrom cortex_ai import cortex_complete_stream\n\n# ---------------------------------------------------------------------------\n# Pre-fetch live data summaries so the LLM can answer with real numbers\n# ---------------------------------------------------------------------------\n\n@st.cache_data(ttl=300)\ndef _build_data_context() -\u003E str:\n    \"\"\"Query the Postgres database and build a data context string for the LLM.\"\"\"\n    parts = []\n\n    # Sensor summary\n    df = run_pg_query(\"\"\"\n        SELECT s.sensor_type, s.unit,\n               COUNT(DISTINCT s.sensor_id) AS sensors,\n               COUNT(r.reading_id) AS readings,\n               ROUND(AVG(r.value)::numeric, 1) AS avg_val,\n               ROUND(MIN(r.value)::numeric, 1) AS min_val,\n               ROUND(MAX(r.value)::numeric, 1) AS max_val\n        FROM sensors s JOIN readings r ON s.sensor_id = r.sensor_id\n        GROUP BY s.sensor_type, s.unit ORDER BY s.sensor_type\n    \"\"\")\n    if not df.empty:\n        parts.append(\"SENSOR SUMMARY:\\n\" + df.to_string(index=False))\n\n    # Building summary\n    df2 = run_pg_query(\"\"\"\n        SELECT b.name AS building, b.location, b.floors,\n               COUNT(DISTINCT s.sensor_id) AS sensors,\n               COUNT(r.reading_id) AS readings\n        FROM buildings b\n        JOIN sensors s ON b.building_id = s.building_id\n        JOIN readings r ON s.sensor_id = r.sensor_id\n        GROUP BY b.name, b.location, b.floors ORDER BY b.name\n    \"\"\")\n    if not df2.empty:\n        parts.append(\"BUILDING SUMMARY:\\n\" + df2.to_string(index=False))\n\n    # Latest readings per sensor\n    df3 = run_pg_query(\"\"\"\n        SELECT DISTINCT ON (s.sensor_id)\n            b.name AS building, s.sensor_type, s.install_location,\n            r.value, s.unit, r.reading_time\n        FROM readings r\n        JOIN sensors s ON r.sensor_id = s.sensor_id\n        JOIN buildings b ON s.building_id = b.building_id\n        ORDER BY s.sensor_id, r.reading_time DESC\n    \"\"\")\n    if not df3.empty:\n        parts.append(\"LATEST READINGS (most recent per sensor):\\n\" + df3.to_string(index=False))\n\n    # Daily averages last 7 days\n    df4 = run_pg_query(\"\"\"\n        SELECT DATE(r.reading_time) AS day, s.sensor_type,\n               ROUND(AVG(r.value)::numeric, 1) AS avg_val\n        FROM readings r JOIN sensors s ON r.sensor_id = s.sensor_id\n        WHERE r.reading_time \u003E= NOW() - INTERVAL '7 days'\n        GROUP BY DATE(r.reading_time), s.sensor_type\n        ORDER BY day, s.sensor_type\n    \"\"\")\n    if not df4.empty:\n        parts.append(\"DAILY AVERAGES (last 7 days):\\n\" + df4.to_string(index=False))\n\n    # Building + type averages last 7 days\n    df5 = run_pg_query(\"\"\"\n        SELECT b.name AS building, s.sensor_type, s.unit,\n               ROUND(AVG(r.value)::numeric, 1) AS avg_val,\n               ROUND(MIN(r.value)::numeric, 1) AS min_val,\n               ROUND(MAX(r.value)::numeric, 1) AS max_val\n        FROM readings r\n        JOIN sensors s ON r.sensor_id = s.sensor_id\n        JOIN buildings b ON s.building_id = b.building_id\n        WHERE r.reading_time \u003E= NOW() - INTERVAL '7 days'\n        GROUP BY b.name, s.sensor_type, s.unit\n        ORDER BY b.name, s.sensor_type\n    \"\"\")\n    if not df5.empty:\n        parts.append(\"PER-BUILDING AVERAGES (last 7 days):\\n\" + df5.to_string(index=False))\n\n    return \"\\n\\n\".join(parts)\n\n\nSYSTEM_PROMPT = \"\"\"You are an IoT sensor data analyst assistant. You answer questions about\nbuilding sensor data using the LIVE DATA provided below. \n\nIMPORTANT RULES:\n- Answer directly with real numbers from the data. Do NOT write SQL queries.\n- Never suggest the user run a query. You have the data -- use it.\n- Give clear, conversational answers. Use specific values, buildings, and sensor locations.\n- Explain what the data means in practical building management terms.\n- If the user explicitly asks for a SQL query, then and only then provide PostgreSQL syntax.\n\nDATABASE SCHEMA (for reference only):\n- buildings: building_id, name, location, floors\n- sensors: sensor_id, building_id, sensor_type (temperature/humidity/energy), unit, install_location, is_active\n- readings: reading_id, sensor_id, reading_time, value\n\nLIVE DATA FROM THE DATABASE:\n{data_context}\n\"\"\"\n\n# ---------------------------------------------------------------------------\n# Suggestion chips\n# ---------------------------------------------------------------------------\nSUGGESTIONS = {\n    \":blue[:material/thermostat:] Temperature trends\": \"What are the temperature trends across buildings over the past week?\",\n    \":green[:material/bolt:] Energy usage\": \"Which building uses the most energy and what patterns do you see?\",\n    \":orange[:material/water_drop:] Humidity analysis\": \"Are there any concerning humidity readings in the Data Center server rooms?\",\n    \":red[:material/query_stats:] Write a query\": \"Write a SQL query to find the top 5 highest temperature readings with their building and location.\",\n}\n\n# ---------------------------------------------------------------------------\n# Page UI\n# ---------------------------------------------------------------------------\nst.markdown(\"# :material/smart_toy: AI Agent Search\")\nst.caption(\"Ask questions about your IoT sensor data using Snowflake Cortex AI.\")\n\n# Initialize chat history\nif \"agent_messages\" not in st.session_state:\n    st.session_state.agent_messages = []\n\n# Show suggestion chips before first message\nif not st.session_state.agent_messages:\n    selected = st.pills(\n        \"Try asking:\",\n        list(SUGGESTIONS.keys()),\n        label_visibility=\"collapsed\",\n    )\n    if selected:\n        st.session_state.agent_messages.append(\n            {\"role\": \"user\", \"content\": SUGGESTIONS[selected]}\n        )\n        st.rerun()\n\n# Display chat history\nfor msg in st.session_state.agent_messages:\n    with st.chat_message(msg[\"role\"]):\n        st.write(msg[\"content\"])\n\n# Handle new input\nif prompt := st.chat_input(\"Ask about your IoT sensor data...\"):\n    st.session_state.agent_messages.append({\"role\": \"user\", \"content\": prompt})\n\n    with st.chat_message(\"user\"):\n        st.write(prompt)\n\n    with st.chat_message(\"assistant\"):\n        data_context = _build_data_context()\n        system = SYSTEM_PROMPT.format(data_context=data_context)\n        conversation = system + \"\\n\\n\"\n        for msg in st.session_state.agent_messages:\n            role = \"User\" if msg[\"role\"] == \"user\" else \"Assistant\"\n            conversation += f\"{role}: {msg['content']}\\n\\n\"\n        conversation += \"Assistant:\"\n\n        response = st.write_stream(cortex_complete_stream(conversation))\n\n    st.session_state.agent_messages.append(\n        {\"role\": \"assistant\", \"content\": response}\n    )\n\n# Sidebar info\nwith st.sidebar:\n    st.markdown(\"### About\")\n    st.markdown(\n        \"This AI agent uses **Snowflake Cortex** (Llama 3.1 70B) \"\n        \"to answer questions about the IoT sensor data in your \"\n        \"Postgres instance.\"\n    )\n    st.markdown(\"---\")\n    st.markdown(\"**Data available:**\")\n    st.markdown(\"- 3 buildings\")\n    st.markdown(\"- 15 sensors (temp, humidity, energy)\")\n    st.markdown(\"- ~43K readings over 30 days\")\n    st.markdown(\"---\")\n    if st.button(\":material/restart_alt: Clear Chat\", type=\"tertiary\"):\n        st.session_state.agent_messages = []\n        st.rerun()\n```\n\n### How the \"answers first\" approach works\n\nThe key insight is that instead of asking the LLM to generate SQL (which the user then has to run), we:\n\n1. **Pre-fetch live data** using `_build_data_context()` — this runs 5 summary queries against Postgres and formats the results as plain text\n2. **Inject the data into the system prompt** — the LLM receives real numbers alongside the schema\n3. **Instruct the LLM to answer directly** — the system prompt explicitly says \"Answer directly with real numbers. Do NOT write SQL queries.\"\n4. The LLM responds with conversational answers like \"The average temperature in HQ Tower is 72.3°F\" instead of `SELECT AVG(value) FROM readings...`\n\nThis approach gives users immediate answers without needing to understand SQL, while still allowing them to request SQL queries when explicitly asked.\n\n\u003E **CoCo prompt:**\n\u003E\n\u003E ```\n\u003E Build an AI Agent Search page for my IoT dashboard. I want the AI to\n\u003E give me direct answers with real numbers from the database, NOT SQL\n\u003E queries. Pre-fetch live data from Postgres (sensor summary, building\n\u003E summary, latest readings, daily averages, per-building averages) and\n\u003E inject it into the Cortex AI system prompt. Include suggestion chips,\n\u003E chat history, streaming responses, and a clear chat button in the sidebar.\n\u003E Use llama3.1-70b via the cortex_ai.py module.\n\u003E ```\n\n\u003C!-- ------------------------ --\u003E\n## Deploy to Streamlit in Snowflake\n\nNow deploy your app to SiS using the Snow CLI.\n\n### Step 9.1: Deploy\n\nFrom your project directory, run:\n\n```bash\nsnow streamlit deploy --replace --connection \u003Cyour-connection-name\u003E\n```\n\nThe first deployment creates the Streamlit app. Subsequent `--replace` deployments update it in-place.\n\n\u003E **Tip:** The `--connection` flag specifies which Snowflake connection from `~/.snowflake/connections.toml` to use for the deployment. This is the same connection name you use for local development.\n\n### Step 9.2: Verify\n\nAfter deployment, Snow CLI prints the app URL. Open it in your browser. The app should load with the Dashboard page showing KPIs and charts.\n\nIf the app fails to start, check the compute pool:\n\n```sql\nDESCRIBE COMPUTE POOL \u003Cyour-compute-pool\u003E;\n```\n\nIf the pool is at capacity (`num_services` equals `max_nodes`), increase it:\n\n```sql\nALTER COMPUTE POOL \u003Cyour-compute-pool\u003E SET MAX_NODES = 3;\n```\n\n\u003E **CoCo prompt:**\n\u003E\n\u003E ```\n\u003E Deploy my IOT Streamlit Dashboard to SiS using snow streamlit deploy.\n\u003E Use the snowflake.yml manifest I already have. If the compute pool\n\u003E is full, increase max_nodes.\n\u003E ```\n\n\u003C!-- ------------------------ --\u003E\n## Networking\n\nTo limit network traffic to the Postgres instance, you might consider using a `HOST_PORT` (public) egress rule on the EAI side and scope the `POSTGRES_INGRESS` rule to Snowflake's egress CIDRs from `SYSTEM$GET_SNOWFLAKE_EGRESS_IP_RANGES()`. However, egress IPs for SPCS are shared across all accounts in the region and are not account-isolated. This approach is **not recommended for production Postgres instances** — use PrivateLink instead.\n\n### PrivateLink\n\nStreamlit in Snowflake (SiS) can connect to a Snowflake Postgres instance using an External Access Integration (EAI) with PrivateLink, keeping all traffic off the public internet. This approach uses `PRIVATE_HOST_PORT` network rules instead of `HOST_PORT`, routing egress traffic from SiS through an AWS PrivateLink tunnel directly to the Postgres instance's VPC endpoint service.\n\n```sql\n-- Step 1: Enable PrivateLink on the Postgres instance (takes ~3-10 min)\nALTER POSTGRES INSTANCE \"my_instance\" ENABLE PRIVATELINK;\n\n-- Monitor until privatelink_service_identifier is non-NULL\nDESCRIBE POSTGRES INSTANCE \"my_instance\";\n\n-- Step 2: Provision outbound PrivateLink endpoint from Snowflake compute\nSELECT SYSTEM$PROVISION_PRIVATELINK_ENDPOINT(\n  '\u003Cprivatelink_service_identifier\u003E',   -- e.g. 'com.amazonaws.vpce.us-west-2.vpce-svc-...'\n  '\u003Cpostgres_host\u003E'                      -- e.g. 'abc123.account.us-west-2.aws.postgres.snowflake.app'\n);\n\n-- Step 3: Find and authorize the pending connection\nSHOW PRIVATELINK CONNECTIONS IN POSTGRES INSTANCE \"my_instance\";\n\nALTER POSTGRES INSTANCE \"my_instance\"\n  AUTHORIZE PRIVATELINK CONNECTIONS = ('\u003Cconnection_id\u003E');\n\n-- Step 4: Create PRIVATE_HOST_PORT network rule\nCREATE OR REPLACE NETWORK RULE my_db.my_schema.pg_privatelink_rule\n  MODE = EGRESS\n  TYPE = PRIVATE_HOST_PORT\n  VALUE_LIST = ('\u003Cpostgres_host\u003E:5432');\n\n-- Step 5: Create secret with Postgres credentials\nCREATE OR REPLACE SECRET my_db.my_schema.pg_secret\n  TYPE = GENERIC_STRING\n  SECRET_STRING = '\u003Cpassword\u003E';\n\n-- Step 6: Create External Access Integration\nCREATE OR REPLACE EXTERNAL ACCESS INTEGRATION pg_privatelink_eai\n  ALLOWED_NETWORK_RULES = (my_db.my_schema.pg_privatelink_rule)\n  ALLOWED_AUTHENTICATION_SECRETS = (my_db.my_schema.pg_secret)\n  ENABLED = TRUE;\n\n-- Step 7: Create Streamlit app with the EAI\nCREATE OR REPLACE STREAMLIT my_db.my_schema.my_app\n  ROOT_LOCATION = '@my_db.my_schema.my_stage'\n  MAIN_FILE = 'app.py'\n  QUERY_WAREHOUSE = 'my_wh'\n  EXTERNAL_ACCESS_INTEGRATIONS = (pg_privatelink_eai)\n  SECRETS = ('pg_pass' = my_db.my_schema.pg_secret);\n\n-- Optional: Remove public network policy for PrivateLink-only access\nALTER POSTGRES INSTANCE \"my_instance\" UNSET NETWORK_POLICY;\n```\n\n\u003E **CoCo prompt:**\n\u003E\n\u003E ```\n\u003E Set up a PrivateLink connection between my Snowflake Postgres instance\n\u003E \"\u003Cinstance_name\u003E\" and a Streamlit in Snowflake app. Use the snowflake-postgres\n\u003E skill. The steps are:\n\u003E\n\u003E 1. Enable PrivateLink on the instance with ALTER POSTGRES INSTANCE ENABLE PRIVATELINK\n\u003E 2. Wait for privatelink_service_identifier to populate via DESCRIBE\n\u003E 3. Use SYSTEM$PROVISION_PRIVATELINK_ENDPOINT with the service identifier and\n\u003E    the PG host to create an outbound endpoint\n\u003E 4. Authorize the pending connection with ALTER POSTGRES INSTANCE AUTHORIZE\n\u003E    PRIVATELINK CONNECTIONS\n\u003E 5. Create a secret for the Postgres connection\n\u003E 6. Create a PRIVATE_HOST_PORT (not HOST_PORT) egress network rule for the PG\n\u003E    host on port 5432\n\u003E 7. Create an External Access Integration with the rule and secret\n\u003E 8. Create or update the Streamlit app with the EAI attached\n\u003E 9. (Optional) Remove any existing POSTGRES_INGRESS network policies so only PrivateLink\n\u003E    can connect\n\u003E ```\n\n\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion and Resources\n\n### What You Built\n\nIn this quickstart, you built a complete IoT sensor monitoring application:\n\n1. A **Snowflake Postgres instance** with a smart-building sensor schema and 30 days of realistic generated data\n2. A **multi-page Streamlit in Snowflake dashboard** with KPI metrics, interactive Altair charts, and building summary tables\n3. A **Charts on Demand** page with interactive filters, heatmaps, distribution histograms, and a **natural language chart generator** powered by Cortex AI\n4. An **AI Agent Search** chatbot using Snowflake Cortex AI that answers questions about your sensor data in plain English using real numbers — not SQL queries\n5. A **Snow CLI deployment** using `snowflake.yml` with container runtime, EAI, and secrets management\n\n### What You Learned\n\n- How to create and configure Snowflake Postgres instances with network policies\n- How to generate realistic time-series data with PostgreSQL's `generate_series()` function\n- How to build a multi-page Streamlit in Snowflake app with `st.navigation` and container runtime\n- How to connect to Postgres from container runtime using `psycopg2` and `.streamlit/secrets.toml` as a deployment artifact\n- How to call Snowflake Cortex AI from both SiS and local development using dual-mode detection\n- How to build a natural language chart generator (NL to SQL to chart) with JSON spec output\n- How to build an AI chatbot that gives direct answers with real data by injecting live data context into the prompt\n- How to deploy using `snow streamlit deploy` with `snowflake.yml`\n- How to troubleshoot common container runtime issues (`_snowflake` module, secrets, compute pool capacity)\n\n### Full CoCo Prompt (All-in-One)\n\nIf you prefer to give CoCo the entire app spec as a single prompt, here it is. This combines all the individual prompts above into one request:\n\n```\nI want to build a complete IoT sensor monitoring app using Snowflake Postgres,\nStreamlit in Snowflake (container runtime), and Snowflake Cortex AI.\n\nINFRASTRUCTURE:\n- Create a network rule (iot_lab_ingress) and network policy (iot_lab_policy)\n  allowing all IPv4 ingress (0.0.0.0/0) for lab purposes.\n- Create a Snowflake Postgres instance called iot_sensors with STANDARD_M\n  compute, 10GB storage, AUTHENTICATION_AUTHORITY = POSTGRES, and the\n  iot_lab_policy network policy.\n- Create an egress network rule (iot_app_egress) allowing outbound access to\n  PyPI (pypi.org, files.pythonhosted.org) and my Postgres host on port 5432.\n  Then create an External Access Integration called iot_app_eai.\n\nDATA:\n- Connect to the iot_sensors Postgres instance and create tables: buildings\n  (building_id, name, location, floors), sensors (sensor_id, building_id,\n  sensor_type, unit, install_location, is_active), and readings (reading_id,\n  sensor_id, reading_time, value) with indexes on (sensor_id, reading_time)\n  and (reading_time).\n- Seed 3 buildings: HQ Tower (Downtown Campus, 12 floors), Data Center\n  (East Campus, 3 floors), Warehouse Alpha (West Campus, 1 floor).\n- Seed 15 sensors across all buildings covering temperature (°F), humidity\n  (%RH), and energy (kW).\n- Generate 30 days of realistic sensor readings at 15-minute intervals using\n  generate_series(). Temperature should follow daily sine-wave cycles (with\n  tight ranges for server rooms and cold storage), humidity should be inverse\n  of temperature, and energy should peak during business hours (8am-6pm).\n\nAPP STRUCTURE:\n- Multi-page Streamlit app using st.navigation with top-bar nav.\n- Project files: streamlit_app.py (entry point), data.py (shared Postgres\n  connection + cached query layer), cortex_ai.py (dual-mode Cortex AI helper),\n  pyproject.toml (dependencies), snowflake.yml (SiS deployment manifest),\n  .streamlit/secrets.toml (Postgres credentials), and app_pages/ directory\n  with home.py, charts.py, and agent.py.\n\nDATA LAYER (data.py):\n- Shared Postgres connection using psycopg2 and st.secrets[\"postgres\"].\n- @st.cache_resource for the persistent connection, @st.cache_data(ttl=300)\n  for query results.\n- Retry logic that clears the cached connection and reconnects on failure.\n- Query functions: get_buildings, get_sensors, get_sensor_summary,\n  get_building_summary, get_daily_avg_by_type, get_hourly_avg (with\n  sensor_type and optional building filter), get_readings_by_sensor,\n  get_latest_readings, get_readings_for_chart, and run_pg_query.\n\nCORTEX AI LAYER (cortex_ai.py):\n- Dual-mode detection: in SiS (detect via /opt/streamlit-runtime) use\n  st.connection(\"snowflake\"); locally use snowflake-connector-python with\n  connection_name from st.secrets[\"snowflake_cortex\"][\"connection_name\"].\n- cortex_complete(prompt) function calling SNOWFLAKE.CORTEX.COMPLETE with\n  llama3.1-70b.\n- cortex_complete_stream(prompt) function that yields word chunks for\n  streaming UX.\n\nPAGE 1 — DASHBOARD (app_pages/home.py):\n- KPI row: total sensors, total readings, sensor types, buildings.\n- Sensor overview cards showing avg/min/max for each sensor type.\n- Altair line chart of daily averages by sensor type.\n- Two-column layout: building summary table and latest readings table.\n\nPAGE 2 — CHARTS ON DEMAND (app_pages/charts.py):\n- Filter bar: sensor type, building, time range (7/14/30 days).\n- Altair time-series line chart colored by sensor location.\n- Summary statistics table per sensor.\n- Hourly average heatmap (day × hour).\n- Value distribution histogram by building.\n- Natural language chart generator: user describes a chart in plain English,\n  Cortex AI returns a JSON spec {sql, chart_type, x, y, color, title}, the\n  app runs the SQL against Postgres and renders with st.line_chart/bar_chart/\n  area_chart/scatter_chart. Include suggestion pills and chart history.\n\nPAGE 3 — AI AGENT SEARCH (app_pages/agent.py):\n- \"Answers first\" approach: pre-fetch live data from Postgres (sensor summary,\n  building summary, latest readings, daily averages, per-building averages)\n  and inject it into the Cortex AI system prompt.\n- The LLM answers directly with real numbers. It does NOT write SQL queries\n  unless the user explicitly asks for one.\n- Chat UI with suggestion chips, streaming responses via st.write_stream,\n  chat history in session state, and a clear chat button in the sidebar.\n\nDEPLOYMENT:\n- snowflake.yml for SiS container runtime deployment with the iot_app_eai\n  External Access Integration.\n- Include .streamlit/secrets.toml in the deployment artifacts so the Postgres\n  password is available in the container runtime.\n- Deploy with: snow streamlit deploy --replace --connection \u003Cconnection-name\u003E\n```\n\n### Cleanup\n\nTo remove the resources created in this guide:\n\n```sql\nDROP POSTGRES INSTANCE IF EXISTS iot_sensors;\nDROP NETWORK POLICY IF EXISTS iot_lab_policy;\nDROP NETWORK RULE IF EXISTS iot_lab_ingress;\nDROP EXTERNAL ACCESS INTEGRATION IF EXISTS iot_app_eai;\nDROP NETWORK RULE IF EXISTS iot_app_egress;\n-- Drop the Streamlit app:\nDROP STREAMLIT IF EXISTS iot_lab.sensors.iot_streamlit_dashboard;\n```\n\n### Related Resources\n\n- [Snowflake Postgres Documentation](https://docs.snowflake.com/en/LIMITEDACCESS/postgres-overview)\n- [Streamlit in Snowflake](https://docs.snowflake.com/en/developer-guide/streamlit/about-streamlit)\n- [Snowflake Cortex AI Functions](https://docs.snowflake.com/en/user-guide/snowflake-cortex/llm-functions)\n- [Snow CLI Installation](https://docs.snowflake.com/en/developer-guide/snowflake-cli/installation/installation)\n- [Snowflake CoCo](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-04d3490983","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-1302db6b75",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-73ba4993a5","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-05-19",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-1cb4d464c5","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake 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