added readme requirements.txt complete file

ai_agent_tutorials/ai_real_estate_agent/README.md

@@ -0,0 +1,61 @@
+## 🏠 AI Real Estate Agent - Powered by Firecrawl's Extract Endpoint
+
+The AI Real Estate Agent automates property search and market analysis using Firecrawl's Extract endpoint and Agno AI Agent's insights. It helps users find properties matching their criteria while providing detailed location trends and investment recommendations. This agent streamlines the property search process by combining data from multiple real estate websites and offering intelligent analysis.
+
+### Features
+- **Smart Property Search**: Uses Firecrawl's Extract endpoint to find properties across multiple real estate websites
+- **Multi-Source Integration**: Aggregates data from 99acres, Housing.com, Square Yards, Nobroker, and MagicBricks
+- **Location Analysis**: Provides detailed price trends and investment insights for different localities
+- **AI-Powered Recommendations**: Uses GPT models to analyze properties and provide structured recommendations
+- **User-Friendly Interface**: Clean Streamlit UI for easy property search and results viewing
+- **Customizable Search**: Filter by city, property type, category, and budget
+
+### How to Get Started
+1. **Clone the repository**:
+   ```bash
+   git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
+   cd ai_agent_tutorials/ai_real_estate_agent
+   ```
+
+2. **Install the required packages**:
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. **Set up your API keys**:
+   - Get your Firecrawl API key from [Firecrawl's website](https://www.firecrawl.dev/app/api-keys)
+   - Get your OpenAI API key from [OpenAI's website](https://platform.openai.com/api-keys)
+
+4. **Run the application**:
+   ```bash
+   streamlit run ai_real_estate_agent.py
+   ```
+
+### Using the Agent
+1. **Enter API Keys**:
+   - Input your Firecrawl and OpenAI API keys in the sidebar
+   - Keys are securely stored in the session state
+
+2. **Set Search Criteria**:
+   - Enter the city name
+   - Select property category (Residential/Commercial)
+   - Choose property type (Flat/Individual House)
+   - Set maximum budget in Crores
+
+3. **View Results**:
+   - Property recommendations with detailed analysis
+   - Location trends with investment insights
+   - Expandable sections for easy reading
+
+### Features in Detail
+- **Property Finding**:
+  - Searches across multiple real estate websites
+  - Returns 3-6 properties matching criteria
+  - Provides detailed property information and analysis
+
+- **Location Analysis**:
+  - Price trends for different localities
+  - Rental yield analysis
+  - Investment potential assessment
+  - Top performing areas identification
+

ai_agent_tutorials/ai_real_estate_agent/ai_real_estate_agent.py

@@ -3,6 +3,7 @@ from pydantic import BaseModel, Field
 from agno.agent import Agent
 from agno.models.openai import OpenAIChat
 from firecrawl import FirecrawlApp
+import streamlit as st
 
 class PropertyData(BaseModel):
     """Schema for property data extraction"""
@@ -27,13 +28,6 @@ class LocationsResponse(BaseModel):
     """Schema for multiple locations response"""
     locations: List[LocationData] = Field(description="List of location data points")
 
-class MarketNewsData(BaseModel):
-    """Schema for market news extraction"""
-    title: str = Field(description="Title of the article/news")
-    content: str = Field(description="Main content of the article")
-    date: str = Field(description="Publication date")
-    source: str = Field(description="Source of the article")
-
 class FirecrawlResponse(BaseModel):
     """Schema for Firecrawl API response"""
     success: bool
@@ -62,13 +56,11 @@ class PropertyFindingAgent:
         """Find and analyze properties based on user preferences"""
         formatted_location = city.lower()
         
-        # First, extract properties using Firecrawl
         urls = [
             f"https://www.squareyards.com/sale/property-for-sale-in-{formatted_location}/*",
             f"https://www.99acres.com/property-in-{formatted_location}-ffid/*",
             f"https://housing.com/in/buy/{formatted_location}/{formatted_location}",
             f"https://www.nobroker.in/property/sale/{city}/{formatted_location}",
-            f"https://www.magicbricks.com/*"
         ]
         
         property_type_prompt = "Flats" if property_type == "Flat" else "Individual Houses"
@@ -76,7 +68,7 @@ class PropertyFindingAgent:
         raw_response = self.firecrawl.extract(
             urls=urls,
             params={
-                'prompt': f"""Extract at least 3-6 different {property_category} {property_type_prompt} from {city} that cost less than {max_price} crores.
+                'prompt': f"""Extract at least 3-10 different {property_category} {property_type_prompt} from {city} that cost less than {max_price} crores.
                 
                 Requirements:
                 - Property Category: {property_category} properties only
@@ -84,7 +76,7 @@ class PropertyFindingAgent:
                 - Location: {city}
                 - Maximum Price: {max_price} crores
                 - Include complete property details with exact location
-                - IMPORTANT: Return data for at least 3 different properties
+                - IMPORTANT: Return data for at least 3 different properties. MAXIMUM 10.
                 - Format as a list of properties with their respective details
                 """,
                 'schema': PropertiesResponse.model_json_schema()
@@ -93,7 +85,6 @@ class PropertyFindingAgent:
         
         print("Raw Property Response:", raw_response)
         
-        # Process the properties data
         if isinstance(raw_response, dict) and raw_response.get('success'):
             properties = raw_response['data'].get('properties', [])
         else:
@@ -101,13 +92,11 @@ class PropertyFindingAgent:
             
         print("Processed Properties:", properties)
 
-        # Now use the agent to analyze and provide recommendations
         properties_context = "\n".join([
             f"Property: {p['Building_name']}\nPrice: {p['Price']}\nLocation: {p['location_address']}\nType: {p['Property_type']}\nDescription: {p['Description']}"
             for p in properties
         ])
         
-        # Get location price trends
         price_trends = self.get_location_trends(city)
         
         analysis = self.agent.run(
@@ -132,7 +121,7 @@ class PropertyFindingAgent:
             """
         )
         
-        return analysis
+        return analysis.content
 
     def get_location_trends(self, city: str) -> str:
         """Get price trends for different localities in the city"""
@@ -151,9 +140,7 @@ class PropertyFindingAgent:
         
         if isinstance(raw_response, dict) and raw_response.get('success'):
             locations = raw_response['data'].get('locations', [])
-            
     
-            # Use agent to analyze the trends
             analysis = self.agent.run(
                 f"""As a real estate expert, analyze these location price trends for {city}:
 
@@ -191,105 +178,115 @@ class PropertyFindingAgent:
             
         return "No price trends data available"
 
-class MarketAnalysisAgent:
-    """Agent responsible for analyzing market trends and conditions"""
-    
-    def __init__(self, firecrawl_api_key: str, openai_api_key: str):
-        self.agent = Agent(
-            model=OpenAIChat(id="o3-mini", api_key=openai_api_key),
-            markdown=True,
-            description="I am a real estate market analyst who provides insights on market trends and conditions."
+def create_property_agent():
+    """Create PropertyFindingAgent with API keys from session state"""
+    if 'property_agent' not in st.session_state:
+        st.session_state.property_agent = PropertyFindingAgent(
+            firecrawl_api_key=st.session_state.firecrawl_key,
+            openai_api_key=st.session_state.openai_key
         )
-        self.firecrawl = FirecrawlApp(api_key=firecrawl_api_key)
-
-    def analyze_market(self, city: str) -> str:
-        """Analyze market conditions using news and market reports"""
-        # Extract market information from news and analysis sites
-        urls = [
-            "https://www.moneycontrol.com/real-estate-property/*",
-            f"https://www.99acres.com/property-rates-and-price-trends-in-{city.lower()}/*",
-            "https://housing.com/news/*",
-            f"https://www.99acres.com/articles/real-estate-market-{city.lower()}*"
-        ]
-        
-        raw_response = self.firecrawl.extract(
-            urls=urls,
-            params={
-                'prompt': f"""Extract recent real estate market information and trends for {city}.
-                Focus on:
-                - Market trends
-                - Price movements
-                - Development projects
-                - Infrastructure updates
-                - Investment potential
-                Only extract articles from the last 6 months.
-                """,
-                'schema': MarketNewsData.model_json_schema()
-            }
-        )
-        
-        # Process the market data
-        market_data = []
-        if isinstance(raw_response, dict):
-            response = FirecrawlResponse(**raw_response)
-            market_data = [response.data]
-        elif isinstance(raw_response, list):
-            responses = [FirecrawlResponse(**item) for item in raw_response]
-            market_data = [resp.data for resp in responses]
-
-        # Analyze the market data
-        market_context = "\n".join([
-            f"Title: {article['title']}\nDate: {article['date']}\nContent: {article['content']}\nSource: {article['source']}"
-            for article in market_data
-        ])
-        
-        analysis = self.agent.run(
-            f"""As a real estate market analyst, provide a comprehensive market analysis for {city}:
-
-            Market Data:
-            {market_context}
-
-            Please provide:
-            1. Current market overview
-            2. Price trends and predictions
-            3. Development and infrastructure updates
-            4. Investment opportunities and risks
-            5. Regulatory changes affecting the market
-            6. Future outlook
-
-            Format your response as a detailed market report with clear sections and actionable insights.
-            """
-        )
-        
-        return analysis
 
 def main():
-    """Main function to demonstrate the agents"""
-    firecrawl_api_key = "YOUR_FIRECRAWL_API_KEY"
-    openai_api_key = "OPENAI_API_KEY"
-    
-    try:
-        # Initialize agents
-        property_agent = PropertyFindingAgent(firecrawl_api_key, openai_api_key)
-        market_agent = MarketAnalysisAgent(firecrawl_api_key)
+    st.set_page_config(
+        page_title="AI Real Estate Agent",
+        page_icon="🏠",
+        layout="wide"
+    )
+
+    with st.sidebar:
+        st.title("🔑 API Configuration")
         
-        # Get property recommendations
-        print("=== Property Analysis ===")
-        property_analysis = property_agent.find_properties(
-            city="Hyderabad",
-            max_price=5.0,
-            property_category="Residential",
-            property_type="Flat"
+        firecrawl_key = st.text_input(
+            "Firecrawl API Key",
+            type="password",
+            help="Enter your Firecrawl API key"
+        )
+        openai_key = st.text_input(
+            "OpenAI API Key",
+            type="password",
+            help="Enter your OpenAI API key"
         )
-        print(property_analysis)
         
-        # Get market analysis
-        print("\n=== Market Analysis ===")
-        market_analysis = market_agent.analyze_market("Hyderabad")
-        print(market_analysis)
+        if firecrawl_key and openai_key:
+            st.session_state.firecrawl_key = firecrawl_key
+            st.session_state.openai_key = openai_key
+            create_property_agent()
+
+    st.title("🏠 AI Real Estate Agent")
+    st.info(
+        """
+        Welcome to the AI Real Estate Agent! 
+        Enter your search criteria below to get property recommendations 
+        and location insights.
+        """
+    )
+
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        city = st.text_input(
+            "City",
+            placeholder="Enter city name (e.g., Bangalore)",
+            help="Enter the city where you want to search for properties"
+        )
+        
+        property_category = st.selectbox(
+            "Property Category",
+            options=["Residential", "Commercial"],
+            help="Select the type of property you're interested in"
+        )
+
+    with col2:
+        max_price = st.number_input(
+            "Maximum Price (in Crores)",
+            min_value=0.1,
+            max_value=100.0,
+            value=5.0,
+            step=0.1,
+            help="Enter your maximum budget in Crores"
+        )
+        
+        property_type = st.selectbox(
+            "Property Type",
+            options=["Flat", "Individual House"],
+            help="Select the specific type of property"
+        )
+
+    if st.button("🔍 Start Search", use_container_width=True):
+        if 'property_agent' not in st.session_state:
+            st.error("⚠️ Please enter your API keys in the sidebar first!")
+            return
             
-    except Exception as e:
-        print(f"An error occurred: {str(e)}")
+        if not city:
+            st.error("⚠️ Please enter a city name!")
+            return
+            
+        try:
+            with st.spinner("🔍 Searching for properties..."):
+                property_results = st.session_state.property_agent.find_properties(
+                    city=city,
+                    max_price=max_price,
+                    property_category=property_category,
+                    property_type=property_type
+                )
+                
+                st.success("✅ Property search completed!")
+                
+                st.subheader("🏘️ Property Recommendations")
+                st.markdown(property_results)
+                
+                st.divider()
+                
+                with st.spinner("📊 Analyzing location trends..."):
+                    location_trends = st.session_state.property_agent.get_location_trends(city)
+                    
+                    st.success("✅ Location analysis completed!")
+                    
+                    with st.expander("📈 Location Trends Analysis"):
+                        st.markdown(location_trends)
+                
+        except Exception as e:
+            st.error(f"❌ An error occurred: {str(e)}")
 
 if __name__ == "__main__":
     main()

ai_agent_tutorials/ai_real_estate_agent/requirements.txt

@@ -1,3 +1,4 @@
 agno
-firecrawl
-pydantic
+firecrawl-py==1.9.0
+pydantic
+streamlit