added AI AQI analysis agent2
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Madhu
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ai_agent_tutorials/ai_aqi_analysis_agent/README.md
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ai_agent_tutorials/ai_aqi_analysis_agent/README.md
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"""
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AQI Analysis Assistant
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---------------------
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A Streamlit application that provides health recommendations based on air quality conditions.
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Uses Firecrawl for AQI data and OpenAI for health recommendations.
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"""
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from typing import Dict, Optional, TypedDict
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from dataclasses import dataclass
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from pydantic import BaseModel, Field
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from agno.agent import Agent
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from agno.models.openai import OpenAIChat
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from firecrawl import FirecrawlApp
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import streamlit as st
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import asyncio
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# Data Models
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class AQIExtractSchema(BaseModel):
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"""Schema for AQI data extraction"""
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aqi: int = Field(description="Current AQI value")
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temperature: float = Field(description="Temperature in Celsius")
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humidity: float = Field(description="Humidity percentage")
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wind_speed: float = Field(description="Wind speed in km/h")
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pm25: float = Field(description="PM2.5 level in µg/m³")
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pm10: float = Field(description="PM10 level in µg/m³")
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co: float = Field(description="CO level in ppb")
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@dataclass
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class UserInput:
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"""Structure for user input data"""
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city: str
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state: str
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country: str
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medical_conditions: Optional[str]
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planned_activity: str
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# Agent Classes
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class AQIDataAgent:
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"""Agent responsible for fetching AQI and weather data"""
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def __init__(self, firecrawl_key: str, openai_key: str) -> None:
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"""Initialize with API keys"""
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self.firecrawl = FirecrawlApp(api_key=firecrawl_key)
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self.agent = Agent(
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model=OpenAIChat(
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id="gpt-4o",
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api_key=openai_key
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),
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description="Expert in analyzing air quality data and weather conditions"
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)
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def _format_url(self, country: str, state: str, city: str) -> str:
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"""Format location URL with proper formatting for multi-word locations"""
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return f"https://www.aqi.in/dashboard/{country.lower().replace(' ', '-')}/{state.lower().replace(' ', '-')}/{city.lower().replace(' ', '-')}"
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async def fetch_data(self, city: str, state: str, country: str) -> AQIExtractSchema:
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"""Fetch weather and AQI data for given location"""
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try:
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base_url = self._format_url(country, state, city)
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urls = [base_url, f"{base_url}/pm", f"{base_url}/co", f"{base_url}/pm10"]
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extract_prompt = """
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Extract the following air quality and weather metrics from the page:
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- Current AQI value as an integer
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- Temperature in Celsius as a float
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- Humidity percentage as a float
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- Wind speed in km/h as a float
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- PM2.5 level in µg/m³ as a float
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- PM10 level in µg/m³ as a float
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- CO level in ppb as a float
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Return these exact metrics with their specified types.
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"""
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response = self.firecrawl.extract(
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urls=urls,
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params={
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'prompt': extract_prompt,
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'schema': AQIExtractSchema.model_json_schema()
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}
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)
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if isinstance(response, dict) and 'error' in response:
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raise ValueError(f"Firecrawl error: {response['error']}")
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return AQIExtractSchema(**response)
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except Exception as e:
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st.error(f"Error fetching AQI data: {str(e)}")
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# Return default values if fetch fails
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return AQIExtractSchema(
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aqi=0,
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temperature=0.0,
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humidity=0.0,
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wind_speed=0.0,
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pm25=0.0,
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pm10=0.0,
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co=0.0
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)
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class HealthRecommendationAgent:
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"""Agent responsible for providing health recommendations"""
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def __init__(self, openai_key: str) -> None:
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"""Initialize with OpenAI API key"""
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self.agent = Agent(
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model=OpenAIChat(
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id="gpt-4o",
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api_key=openai_key
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),
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description="Health recommendation expert for air quality conditions"
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)
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async def get_recommendations(
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self,
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aqi_data: AQIExtractSchema,
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user_input: UserInput
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) -> str:
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"""Generate health recommendations based on conditions"""
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prompt = self._create_prompt(aqi_data, user_input)
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response = await self.agent.run(prompt)
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return response.content
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def _create_prompt(
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self,
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aqi_data: AQIExtractSchema,
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user_input: UserInput
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) -> str:
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"""Create detailed prompt for health recommendations"""
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return f"""
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Based on the following air quality conditions in {user_input.city}, {user_input.state}, {user_input.country}:
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- Overall AQI: {aqi_data.aqi}
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- PM2.5 Level: {aqi_data.pm25} µg/m³
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- PM10 Level: {aqi_data.pm10} µg/m³
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- CO Level: {aqi_data.co} ppb
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Weather conditions:
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- Temperature: {aqi_data.temperature}°C
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- Humidity: {aqi_data.humidity}%
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- Wind Speed: {aqi_data.wind_speed} km/h
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User's Context:
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- Medical Conditions: {user_input.medical_conditions or 'None'}
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- Planned Activity: {user_input.planned_activity}
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Provide detailed health recommendations considering:
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1. Current air quality impacts on health
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2. Safety precautions needed
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3. Whether the planned activity is advisable
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4. Alternative activity suggestions if needed
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5. Best time to conduct the activity if applicable
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"""
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# Main Analysis Function
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async def analyze_conditions(
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user_input: UserInput,
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api_keys: Dict[str, str]
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) -> str:
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"""Main function to analyze conditions and provide recommendations"""
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# Initialize agents
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aqi_agent = AQIDataAgent(
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firecrawl_key=api_keys['firecrawl'],
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openai_key=api_keys['openai']
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)
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health_agent = HealthRecommendationAgent(
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openai_key=api_keys['openai']
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)
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# Get data and recommendations
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aqi_data = await aqi_agent.fetch_data(
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city=user_input.city,
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state=user_input.state,
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country=user_input.country
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)
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return await health_agent.get_recommendations(aqi_data, user_input)
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# Streamlit UI Components
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def initialize_session_state():
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"""Initialize Streamlit session state"""
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if 'api_keys' not in st.session_state:
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st.session_state.api_keys = {
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'firecrawl': '',
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'openai': ''
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}
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def setup_page():
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"""Configure page settings and styles"""
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st.set_page_config(
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page_title="AQI Analysis Assistant",
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page_icon="🌍",
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layout="wide"
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)
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st.markdown("""
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<style>
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.main { padding: 2rem; }
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.stButton > button { width: 100%; }
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.success-message {
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padding: 1rem;
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border-radius: 0.5rem;
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background-color: #dcfce7;
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color: #166534;
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}
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.error-message {
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padding: 1rem;
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border-radius: 0.5rem;
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background-color: #fee2e2;
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color: #991b1b;
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}
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</style>
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""", unsafe_allow_html=True)
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def render_sidebar():
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"""Render sidebar with API configuration"""
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with st.sidebar:
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st.header("🔑 API Configuration")
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new_firecrawl_key = st.text_input(
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"Firecrawl API Key",
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type="password",
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value=st.session_state.api_keys['firecrawl'],
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help="Enter your Firecrawl API key"
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)
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new_openai_key = st.text_input(
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"OpenAI API Key",
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type="password",
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value=st.session_state.api_keys['openai'],
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help="Enter your OpenAI API key"
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)
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if (new_firecrawl_key != st.session_state.api_keys['firecrawl'] or
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new_openai_key != st.session_state.api_keys['openai']):
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st.session_state.api_keys.update({
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'firecrawl': new_firecrawl_key,
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'openai': new_openai_key
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})
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st.success("✅ API keys updated!")
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def render_main_content():
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"""Render main content area"""
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st.title("🌍 AQI Analysis Assistant")
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st.markdown("Get personalized health recommendations based on air quality conditions.")
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col1, col2 = st.columns([2, 1])
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with col1:
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st.header("📍 Location Details")
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city = st.text_input("City", placeholder="e.g., Mumbai")
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state = st.text_input("State", placeholder="e.g., Maharashtra")
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country = st.text_input("Country", value="India")
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st.header("👤 Personal Details")
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medical_conditions = st.text_area(
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"Medical Conditions (optional)",
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placeholder="e.g., asthma, allergies"
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)
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planned_activity = st.text_area(
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"Planned Activity",
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placeholder="e.g., morning jog for 2 hours"
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)
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return UserInput(
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city=city,
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state=state,
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country=country,
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medical_conditions=medical_conditions,
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planned_activity=planned_activity
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)
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def main():
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"""Main application entry point"""
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initialize_session_state()
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setup_page()
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render_sidebar()
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user_input = render_main_content()
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if st.button("🔍 Analyze & Get Recommendations"):
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if not all([user_input.city, user_input.state, user_input.planned_activity]):
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st.error("Please fill in all required fields (medical conditions are optional)")
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elif not all(st.session_state.api_keys.values()):
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st.error("Please provide both API keys in the sidebar")
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else:
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try:
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with st.spinner("🔄 Analyzing conditions..."):
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result = asyncio.run(
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analyze_conditions(
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user_input=user_input,
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api_keys=st.session_state.api_keys
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)
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)
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st.success("✅ Analysis completed!")
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st.markdown("### 📊 Recommendations")
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st.markdown(result)
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st.download_button(
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"💾 Download Recommendations",
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data=result,
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file_name=f"aqi_recommendations_{user_input.city}_{user_input.state}.txt",
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mime="text/plain"
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)
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except Exception as e:
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st.error(f"❌ Error: {str(e)}")
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if __name__ == "__main__":
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main()
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