added AI AQI analysis agent3
This commit is contained in:
Madhu
parent
8411c90d69
commit
37b9058dff
2 changed files with 85 additions and 136 deletions
|
|
@ -1,11 +1,4 @@
|
|||
"""
|
||||
AQI Analysis Assistant
|
||||
---------------------
|
||||
A Streamlit application that provides health recommendations based on air quality conditions.
|
||||
Uses Firecrawl for AQI data and OpenAI for health recommendations.
|
||||
"""
|
||||
|
||||
from typing import Dict, Optional, TypedDict
|
||||
from typing import Dict, Optional
|
||||
from dataclasses import dataclass
|
||||
from pydantic import BaseModel, Field
|
||||
from agno.agent import Agent
|
||||
|
|
@ -14,130 +7,99 @@ from firecrawl import FirecrawlApp
|
|||
import streamlit as st
|
||||
import asyncio
|
||||
|
||||
# Data Models
|
||||
class AQIExtractSchema(BaseModel):
|
||||
"""Schema for AQI data extraction"""
|
||||
aqi: int = Field(description="Current AQI value")
|
||||
temperature: float = Field(description="Temperature in Celsius")
|
||||
class AQIResponse(BaseModel):
|
||||
success: bool
|
||||
data: Dict[str, float]
|
||||
status: str
|
||||
expiresAt: str
|
||||
|
||||
class ExtractSchema(BaseModel):
|
||||
aqi: float = Field(description="Air Quality Index")
|
||||
temperature: float = Field(description="Temperature in degrees Celsius")
|
||||
humidity: float = Field(description="Humidity percentage")
|
||||
wind_speed: float = Field(description="Wind speed in km/h")
|
||||
pm25: float = Field(description="PM2.5 level in µg/m³")
|
||||
pm10: float = Field(description="PM10 level in µg/m³")
|
||||
co: float = Field(description="CO level in ppb")
|
||||
wind_speed: float = Field(description="Wind speed in kilometers per hour")
|
||||
pm25: float = Field(description="Particulate Matter 2.5 micrometers")
|
||||
pm10: float = Field(description="Particulate Matter 10 micrometers")
|
||||
co: float = Field(description="Carbon Monoxide level")
|
||||
|
||||
@dataclass
|
||||
class UserInput:
|
||||
"""Structure for user input data"""
|
||||
city: str
|
||||
state: str
|
||||
country: str
|
||||
medical_conditions: Optional[str]
|
||||
planned_activity: str
|
||||
|
||||
# Agent Classes
|
||||
class AQIDataAgent:
|
||||
"""Agent responsible for fetching AQI and weather data"""
|
||||
class AQIAnalyzer:
|
||||
|
||||
def __init__(self, firecrawl_key: str, openai_key: str) -> None:
|
||||
"""Initialize with API keys"""
|
||||
def __init__(self, firecrawl_key: str) -> None:
|
||||
self.firecrawl = FirecrawlApp(api_key=firecrawl_key)
|
||||
self.agent = Agent(
|
||||
model=OpenAIChat(
|
||||
id="gpt-4o",
|
||||
api_key=openai_key
|
||||
),
|
||||
description="Expert in analyzing air quality data and weather conditions"
|
||||
)
|
||||
|
||||
def _format_url(self, country: str, state: str, city: str) -> str:
|
||||
"""Format location URL with proper formatting for multi-word locations"""
|
||||
return f"https://www.aqi.in/dashboard/{country.lower().replace(' ', '-')}/{state.lower().replace(' ', '-')}/{city.lower().replace(' ', '-')}"
|
||||
|
||||
async def fetch_data(self, city: str, state: str, country: str) -> AQIExtractSchema:
|
||||
"""Fetch weather and AQI data for given location"""
|
||||
def fetch_aqi_data(self, city: str, state: str, country: str) -> Dict[str, float]:
|
||||
try:
|
||||
base_url = self._format_url(country, state, city)
|
||||
urls = [base_url, f"{base_url}/pm", f"{base_url}/co", f"{base_url}/pm10"]
|
||||
|
||||
extract_prompt = """
|
||||
Extract the following air quality and weather metrics from the page:
|
||||
- Current AQI value as an integer
|
||||
- Temperature in Celsius as a float
|
||||
- Humidity percentage as a float
|
||||
- Wind speed in km/h as a float
|
||||
- PM2.5 level in µg/m³ as a float
|
||||
- PM10 level in µg/m³ as a float
|
||||
- CO level in ppb as a float
|
||||
|
||||
Return these exact metrics with their specified types.
|
||||
"""
|
||||
url = self._format_url(country, state, city)
|
||||
|
||||
response = self.firecrawl.extract(
|
||||
urls=urls,
|
||||
urls=[f"{url}/*"],
|
||||
params={
|
||||
'prompt': extract_prompt,
|
||||
'schema': AQIExtractSchema.model_json_schema()
|
||||
'prompt': 'Extract the AQI, temperature, humidity, wind speed, PM2.5, PM10, and CO levels from the page.',
|
||||
'schema': ExtractSchema.model_json_schema()
|
||||
}
|
||||
)
|
||||
|
||||
if isinstance(response, dict) and 'error' in response:
|
||||
raise ValueError(f"Firecrawl error: {response['error']}")
|
||||
aqi_response = AQIResponse(**response)
|
||||
if not aqi_response.success:
|
||||
raise ValueError(f"Failed to fetch AQI data: {aqi_response.status}")
|
||||
|
||||
return AQIExtractSchema(**response)
|
||||
return aqi_response.data
|
||||
|
||||
except Exception as e:
|
||||
st.error(f"Error fetching AQI data: {str(e)}")
|
||||
# Return default values if fetch fails
|
||||
return AQIExtractSchema(
|
||||
aqi=0,
|
||||
temperature=0.0,
|
||||
humidity=0.0,
|
||||
wind_speed=0.0,
|
||||
pm25=0.0,
|
||||
pm10=0.0,
|
||||
co=0.0
|
||||
)
|
||||
return {
|
||||
'aqi': 0,
|
||||
'temperature': 0,
|
||||
'humidity': 0,
|
||||
'wind_speed': 0,
|
||||
'pm25': 0,
|
||||
'pm10': 0,
|
||||
'co': 0
|
||||
}
|
||||
|
||||
class HealthRecommendationAgent:
|
||||
"""Agent responsible for providing health recommendations"""
|
||||
|
||||
def __init__(self, openai_key: str) -> None:
|
||||
"""Initialize with OpenAI API key"""
|
||||
self.agent = Agent(
|
||||
model=OpenAIChat(
|
||||
id="gpt-4o",
|
||||
name="Health Recommendation Agent",
|
||||
api_key=openai_key
|
||||
),
|
||||
description="Health recommendation expert for air quality conditions"
|
||||
)
|
||||
)
|
||||
|
||||
async def get_recommendations(
|
||||
self,
|
||||
aqi_data: AQIExtractSchema,
|
||||
def get_recommendations(
|
||||
self,
|
||||
aqi_data: Dict[str, float],
|
||||
user_input: UserInput
|
||||
) -> str:
|
||||
"""Generate health recommendations based on conditions"""
|
||||
prompt = self._create_prompt(aqi_data, user_input)
|
||||
response = await self.agent.run(prompt)
|
||||
response = self.agent.run(prompt)
|
||||
return response.content
|
||||
|
||||
def _create_prompt(
|
||||
self,
|
||||
aqi_data: AQIExtractSchema,
|
||||
user_input: UserInput
|
||||
) -> str:
|
||||
"""Create detailed prompt for health recommendations"""
|
||||
def _create_prompt(self, aqi_data: Dict[str, float], user_input: UserInput) -> str:
|
||||
return f"""
|
||||
Based on the following air quality conditions in {user_input.city}, {user_input.state}, {user_input.country}:
|
||||
- Overall AQI: {aqi_data.aqi}
|
||||
- PM2.5 Level: {aqi_data.pm25} µg/m³
|
||||
- PM10 Level: {aqi_data.pm10} µg/m³
|
||||
- CO Level: {aqi_data.co} ppb
|
||||
- Overall AQI: {aqi_data['aqi']}
|
||||
- PM2.5 Level: {aqi_data['pm25']} µg/m³
|
||||
- PM10 Level: {aqi_data['pm10']} µg/m³
|
||||
- CO Level: {aqi_data['co']} ppb
|
||||
|
||||
Weather conditions:
|
||||
- Temperature: {aqi_data.temperature}°C
|
||||
- Humidity: {aqi_data.humidity}%
|
||||
- Wind Speed: {aqi_data.wind_speed} km/h
|
||||
- Temperature: {aqi_data['temperature']}°C
|
||||
- Humidity: {aqi_data['humidity']}%
|
||||
- Wind Speed: {aqi_data['wind_speed']} km/h
|
||||
|
||||
User's Context:
|
||||
- Medical Conditions: {user_input.medical_conditions or 'None'}
|
||||
|
|
@ -151,33 +113,22 @@ class HealthRecommendationAgent:
|
|||
5. Best time to conduct the activity if applicable
|
||||
"""
|
||||
|
||||
# Main Analysis Function
|
||||
async def analyze_conditions(
|
||||
def analyze_conditions(
|
||||
user_input: UserInput,
|
||||
api_keys: Dict[str, str]
|
||||
) -> str:
|
||||
"""Main function to analyze conditions and provide recommendations"""
|
||||
# Initialize agents
|
||||
aqi_agent = AQIDataAgent(
|
||||
firecrawl_key=api_keys['firecrawl'],
|
||||
openai_key=api_keys['openai']
|
||||
)
|
||||
health_agent = HealthRecommendationAgent(
|
||||
openai_key=api_keys['openai']
|
||||
)
|
||||
aqi_analyzer = AQIAnalyzer(firecrawl_key=api_keys['firecrawl'])
|
||||
health_agent = HealthRecommendationAgent(openai_key=api_keys['openai'])
|
||||
|
||||
# Get data and recommendations
|
||||
aqi_data = await aqi_agent.fetch_data(
|
||||
aqi_data = aqi_analyzer.fetch_aqi_data(
|
||||
city=user_input.city,
|
||||
state=user_input.state,
|
||||
country=user_input.country
|
||||
)
|
||||
|
||||
return await health_agent.get_recommendations(aqi_data, user_input)
|
||||
return health_agent.get_recommendations(aqi_data, user_input)
|
||||
|
||||
# Streamlit UI Components
|
||||
def initialize_session_state():
|
||||
"""Initialize Streamlit session state"""
|
||||
if 'api_keys' not in st.session_state:
|
||||
st.session_state.api_keys = {
|
||||
'firecrawl': '',
|
||||
|
|
@ -185,34 +136,16 @@ def initialize_session_state():
|
|||
}
|
||||
|
||||
def setup_page():
|
||||
"""Configure page settings and styles"""
|
||||
st.set_page_config(
|
||||
page_title="AQI Analysis Assistant",
|
||||
page_icon="🌍",
|
||||
layout="wide"
|
||||
)
|
||||
|
||||
st.markdown("""
|
||||
<style>
|
||||
.main { padding: 2rem; }
|
||||
.stButton > button { width: 100%; }
|
||||
.success-message {
|
||||
padding: 1rem;
|
||||
border-radius: 0.5rem;
|
||||
background-color: #dcfce7;
|
||||
color: #166534;
|
||||
}
|
||||
.error-message {
|
||||
padding: 1rem;
|
||||
border-radius: 0.5rem;
|
||||
background-color: #fee2e2;
|
||||
color: #991b1b;
|
||||
}
|
||||
</style>
|
||||
""", unsafe_allow_html=True)
|
||||
st.title("🌍 AQI Analysis Assistant")
|
||||
st.info("Get personalized health recommendations based on air quality conditions.")
|
||||
|
||||
def render_sidebar():
|
||||
"""Render sidebar with API configuration"""
|
||||
with st.sidebar:
|
||||
st.header("🔑 API Configuration")
|
||||
|
||||
|
|
@ -238,18 +171,15 @@ def render_sidebar():
|
|||
st.success("✅ API keys updated!")
|
||||
|
||||
def render_main_content():
|
||||
"""Render main content area"""
|
||||
st.title("🌍 AQI Analysis Assistant")
|
||||
st.markdown("Get personalized health recommendations based on air quality conditions.")
|
||||
|
||||
col1, col2 = st.columns([2, 1])
|
||||
st.header("📍 Location Details")
|
||||
col1, col2 = st.columns(2)
|
||||
|
||||
with col1:
|
||||
st.header("📍 Location Details")
|
||||
city = st.text_input("City", placeholder="e.g., Mumbai")
|
||||
state = st.text_input("State", placeholder="e.g., Maharashtra")
|
||||
country = st.text_input("Country", value="India")
|
||||
|
||||
|
||||
with col2:
|
||||
st.header("👤 Personal Details")
|
||||
medical_conditions = st.text_area(
|
||||
"Medical Conditions (optional)",
|
||||
|
|
@ -269,7 +199,6 @@ def render_main_content():
|
|||
)
|
||||
|
||||
def main():
|
||||
"""Main application entry point"""
|
||||
initialize_session_state()
|
||||
setup_page()
|
||||
render_sidebar()
|
||||
|
|
@ -283,16 +212,14 @@ def main():
|
|||
else:
|
||||
try:
|
||||
with st.spinner("🔄 Analyzing conditions..."):
|
||||
result = asyncio.run(
|
||||
analyze_conditions(
|
||||
user_input=user_input,
|
||||
api_keys=st.session_state.api_keys
|
||||
)
|
||||
result = analyze_conditions(
|
||||
user_input=user_input,
|
||||
api_keys=st.session_state.api_keys
|
||||
)
|
||||
|
||||
st.success("✅ Analysis completed!")
|
||||
st.markdown("### 📊 Recommendations")
|
||||
st.markdown(result)
|
||||
st.info(result)
|
||||
|
||||
st.download_button(
|
||||
"💾 Download Recommendations",
|
||||
|
|
|
|||
22
ai_agent_tutorials/ai_aqi_analysis_agent/test_firecrawl.py
Normal file
22
ai_agent_tutorials/ai_aqi_analysis_agent/test_firecrawl.py
Normal file
|
|
@ -0,0 +1,22 @@
|
|||
from firecrawl import FirecrawlApp
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
# Initialize the FirecrawlApp with your API key
|
||||
app = FirecrawlApp(api_key='')
|
||||
|
||||
class ExtractSchema(BaseModel):
|
||||
aqi: float = Field(description="Air Quality Index")
|
||||
temperature: float = Field(description="Temperature in degrees Celsius")
|
||||
humidity: float = Field(description="Humidity percentage")
|
||||
wind_speed: float = Field(description="Wind speed in kilometers per hour")
|
||||
pm25: float = Field(description="Particulate Matter 2.5 micrometers")
|
||||
pm10: float = Field(description="Particulate Matter 10 micrometers")
|
||||
co: float = Field(description="Carbon Monoxide level")
|
||||
|
||||
data = app.extract([
|
||||
'https://www.aqi.in/dashboard/india/andhra-pradesh/kakinada/*'
|
||||
], {
|
||||
'prompt': 'Extract the AQI, temperature, humidity, wind speed, PM2.5, PM10, and CO levels from the page.',
|
||||
'schema': ExtractSchema.model_json_schema(),
|
||||
})
|
||||
print(data)
|
||||
Loading…
Reference in a new issue