Enhance property handling and update server configuration

- Updated the `extract_and_search_properties` function to utilize existing properties in the state for specific classification requests, improving efficiency in handling user inquiries.
- Removed outdated property selection logic from the `handle_image_request` function to streamline the code and enhance clarity.
- Modified the server port in `main.py` from 7860 to 8000 to align with new configuration settings.
- Updated the README to reflect the new server port and ensure accurate setup instructions for users.
- These changes aim to improve the responsiveness and accuracy of property selections while providing clear guidance for application setup.

README.md

@@ -1,156 +1,156 @@
----
-title: PropAgent
-emoji: 🤖
-colorFrom: blue
-colorTo: purple
-sdk: docker
-pinned: false
-short_description: WhatsApp AI Agent with Supabase integration
----
-
-# PropAgent - WhatsApp AI Agent
-
-A WhatsApp chatbot powered by OpenAI GPT-4 and integrated with Supabase for user management.
-
-## Features
-
-- 🤖 AI-powered chat responses using OpenAI GPT-4
-- 📱 WhatsApp Business API integration
-- 👥 User management with Supabase database
-- 🔄 Conversation memory and context
-- 📊 User activity tracking
-- 🚀 RESTful API endpoints for user management
-
-## Setup
-
-### 1. Environment Variables
-
-Create a `.env` file with the following variables:
-
-```bash
-# WhatsApp Business API
-VERIFY_TOKEN=your_webhook_verify_token
-PHONE_NUMBER_ID=your_phone_number_id
-WHATSAPP_API_TOKEN=your_whatsapp_api_token
-
-# OpenAI
-OPENAI_API_KEY=your_openai_api_key
-
-# Supabase
-SUPABASE_URL=your_supabase_project_url
-SUPABASE_KEY=your_supabase_anon_key
-```
-
-### 2. Supabase Setup
-
-1. Create a new Supabase project at [supabase.com](https://supabase.com)
-2. Go to the SQL Editor in your Supabase dashboard
-3. Run the SQL script from `supabase_setup.sql` to create the users table
-4. Copy your project URL and anon key from Settings > API
-
-### 3. WhatsApp Business API Setup
-
-1. Create a Meta Developer account
-2. Set up a WhatsApp Business app
-3. Configure webhook URL: `https://your-domain.com/webhook`
-4. Set the verify token in your environment variables
-
-### 4. Installation
-
-```bash
-pip install -r requirements.txt
-```
-
-### 5. Running the Application
-
-```bash
-uvicorn main:app --host 0.0.0.0 --port 7860
-```
-
-## API Endpoints
-
-### Webhook Endpoints
-- `GET /webhook` - WhatsApp webhook verification
-- `POST /webhook` - Receive WhatsApp messages
-
-### User Management
-- `GET /users/{wa_id}` - Get user by WhatsApp ID
-- `GET /users` - List all users (with pagination)
-- `PUT /users/{wa_id}` - Update user name
-
-### Health & Testing
-- `GET /ping` - Simple health check
-- `GET /health` - Detailed service status
-- `POST /chat` - Direct AI chat endpoint
-
-## Database Schema
-
-### Users Table
-```sql
-users
------
-- wa_id (TEXT, PRIMARY KEY) - WhatsApp user ID
-- name (TEXT) - User's display name
-- created_at (TIMESTAMP) - Account creation time
-- updated_at (TIMESTAMP) - Last activity time
-```
-
-## Features
-
-### User Management
-- Automatic user creation when first message is received
-- User name updates from WhatsApp profile
-- Activity tracking with automatic timestamp updates
-
-### AI Chat
-- Context-aware conversations using user names
-- Conversation memory (last 6 messages)
-- Error handling and fallback responses
-
-### Security
-- Environment variable configuration
-- Optional Row Level Security (RLS) in Supabase
-- Input validation and error handling
-
-## Project Structure
-
-```
-PropAgent/
-├── main.py              # Main application entry point
-├── config.py            # Configuration and client setup
-├── database.py          # Supabase database operations
-├── whatsapp.py          # WhatsApp API integration
-├── ai_chat.py           # LangGraph AI conversation logic
-├── api_routes.py        # FastAPI route definitions
-├── supabase_setup.sql   # Database schema setup
-└── requirements.txt     # Python dependencies
-```
-
-## Development
-
-The application uses a modular architecture:
-- **main.py** - Clean entry point focusing on core webhook logic
-- **config.py** - Environment variables and client initialization
-- **database.py** - All Supabase database operations
-- **whatsapp.py** - WhatsApp Business API integration
-- **ai_chat.py** - LangGraph conversation flow and AI processing
-- **api_routes.py** - REST API endpoints and webhook verification
-
-**Technologies:**
-- **FastAPI** for the web framework
-- **LangGraph** for conversation flow management
-- **OpenAI GPT-4** for AI responses
-- **Supabase** for user data storage
-- **WhatsApp Business API** for messaging
-
-## Deployment
-
-The application includes a Dockerfile for containerized deployment. You can deploy it to:
-- Hugging Face Spaces
-- Railway
-- Heroku
-- Any container platform
-
-## License
-
-MIT License
+---
+title: PropAgent
+emoji: 🤖
+colorFrom: blue
+colorTo: purple
+sdk: docker
+pinned: false
+short_description: WhatsApp AI Agent with Supabase integration
+---
+
+# PropAgent - WhatsApp AI Agent
+
+A WhatsApp chatbot powered by OpenAI GPT-4 and integrated with Supabase for user management.
+
+## Features
+
+- 🤖 AI-powered chat responses using OpenAI GPT-4
+- 📱 WhatsApp Business API integration
+- 👥 User management with Supabase database
+- 🔄 Conversation memory and context
+- 📊 User activity tracking
+- 🚀 RESTful API endpoints for user management
+
+## Setup
+
+### 1. Environment Variables
+
+Create a `.env` file with the following variables:
+
+```bash
+# WhatsApp Business API
+VERIFY_TOKEN=your_webhook_verify_token
+PHONE_NUMBER_ID=your_phone_number_id
+WHATSAPP_API_TOKEN=your_whatsapp_api_token
+
+# OpenAI
+OPENAI_API_KEY=your_openai_api_key
+
+# Supabase
+SUPABASE_URL=your_supabase_project_url
+SUPABASE_KEY=your_supabase_anon_key
+```
+
+### 2. Supabase Setup
+
+1. Create a new Supabase project at [supabase.com](https://supabase.com)
+2. Go to the SQL Editor in your Supabase dashboard
+3. Run the SQL script from `supabase_setup.sql` to create the users table
+4. Copy your project URL and anon key from Settings > API
+
+### 3. WhatsApp Business API Setup
+
+1. Create a Meta Developer account
+2. Set up a WhatsApp Business app
+3. Configure webhook URL: `https://your-domain.com/webhook`
+4. Set the verify token in your environment variables
+
+### 4. Installation
+
+```bash
+pip install -r requirements.txt
+```
+
+### 5. Running the Application
+
+```bash
+uvicorn main:app --host 0.0.0.0 --port 8000
+```
+
+## API Endpoints
+
+### Webhook Endpoints
+- `GET /webhook` - WhatsApp webhook verification
+- `POST /webhook` - Receive WhatsApp messages
+
+### User Management
+- `GET /users/{wa_id}` - Get user by WhatsApp ID
+- `GET /users` - List all users (with pagination)
+- `PUT /users/{wa_id}` - Update user name
+
+### Health & Testing
+- `GET /ping` - Simple health check
+- `GET /health` - Detailed service status
+- `POST /chat` - Direct AI chat endpoint
+
+## Database Schema
+
+### Users Table
+```sql
+users
+-----
+- wa_id (TEXT, PRIMARY KEY) - WhatsApp user ID
+- name (TEXT) - User's display name
+- created_at (TIMESTAMP) - Account creation time
+- updated_at (TIMESTAMP) - Last activity time
+```
+
+## Features
+
+### User Management
+- Automatic user creation when first message is received
+- User name updates from WhatsApp profile
+- Activity tracking with automatic timestamp updates
+
+### AI Chat
+- Context-aware conversations using user names
+- Conversation memory (last 6 messages)
+- Error handling and fallback responses
+
+### Security
+- Environment variable configuration
+- Optional Row Level Security (RLS) in Supabase
+- Input validation and error handling
+
+## Project Structure
+
+```
+PropAgent/
+├── main.py              # Main application entry point
+├── config.py            # Configuration and client setup
+├── database.py          # Supabase database operations
+├── whatsapp.py          # WhatsApp API integration
+├── ai_chat.py           # LangGraph AI conversation logic
+├── api_routes.py        # FastAPI route definitions
+├── supabase_setup.sql   # Database schema setup
+└── requirements.txt     # Python dependencies
+```
+
+## Development
+
+The application uses a modular architecture:
+- **main.py** - Clean entry point focusing on core webhook logic
+- **config.py** - Environment variables and client initialization
+- **database.py** - All Supabase database operations
+- **whatsapp.py** - WhatsApp Business API integration
+- **ai_chat.py** - LangGraph conversation flow and AI processing
+- **api_routes.py** - REST API endpoints and webhook verification
+
+**Technologies:**
+- **FastAPI** for the web framework
+- **LangGraph** for conversation flow management
+- **OpenAI GPT-4** for AI responses
+- **Supabase** for user data storage
+- **WhatsApp Business API** for messaging
+
+## Deployment
+
+The application includes a Dockerfile for containerized deployment. You can deploy it to:
+- Hugging Face Spaces
+- Railway
+- Heroku
+- Any container platform
+
+## License
+
+MIT License

ai_chat.py

@@ -371,6 +371,17 @@ async def extract_and_search_properties(state):
     classification = state.get("classification")
     print(f"DEBUG - Property search classification check: '{classification}'")
     
+    # Check if this is a detail request for existing properties in state
+    existing_properties = state.get("properties", [])
+    if (classification.startswith("request_images") or 
+        classification == "request_address" or 
+        classification == "request_details") and existing_properties:
+        print(f"DEBUG - Using existing properties from state, count: {len(existing_properties)}")
+        return {
+            "properties": existing_properties,
+            "classification": classification
+        }
+    
     # Check if classification matches our search categories
     is_search_request = (
         classification == "search_listings" or
@@ -575,40 +586,6 @@ async def handle_image_request(state):
                     best_match_score = score
                     selected_property = prop
     
-    # Look for specific property selections in conversation
-    session_messages = state.get("session_messages", [])
-    recent_messages = session_messages[-20:]  # Look at more messages
-
-    # Look for patterns like "option 3", "the warehouse", "this property"
-    selected_property_context = None
-
-    for msg in recent_messages:
-        if msg.get("role") == "user":
-            content = msg.get("content", "").lower()
-            # Look for option selections
-            if "option" in content:
-                import re
-                option_match = re.search(r'option\s+(\d+)', content)
-                if option_match:
-                    option_num = int(option_match.group(1))
-                    if 1 <= option_num <= len(props):
-                        selected_property_context = props[option_num - 1]
-                        print(f"DEBUG - Found user selected option {option_num}: {selected_property_context.get('title')}")
-                        break
-            
-            # Look for property type mentions that user specifically asked about
-            for i, prop in enumerate(props):
-                title_words = prop.get("title", "").lower().split()
-                for word in ["warehouse", "office", "space", "unit"]:
-                    if word in content and word in title_words:
-                        selected_property_context = prop
-                        print(f"DEBUG - Found user interest in {word}: {prop.get('title')}")
-                        break
-
-    # Use the property the user specifically selected/discussed
-    if selected_property_context:
-        selected_property = selected_property_context
-    
     # Fallback: Use conversation context to find which property user was discussing
     if not selected_property and len(props) > 1:
         # Check conversation history for property context

database.py

@@ -329,8 +329,8 @@ async def search_properties(filters: dict) -> list:
         # Note: Must-have features are removed from database search
         # They will be handled by the LLM in the chat response
         
-        # e. Order & limit
-        query = query.order("is_featured", desc=True).order("price").limit(5)
+        # e. Order & limit - prioritize recent activity, then featured, then price
+        query = query.order("updated_at", desc=True).order("is_featured", desc=True).order("price").limit(5)
         
         # f. Execute and return
         resp = query.execute()

main.py

@@ -113,4 +113,4 @@ async def startup_event():
 
 if __name__ == "__main__":
     import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+    uvicorn.run(app, host="0.0.0.0", port=8000)

requirements.txt

@@ -1,13 +1,10 @@
-fastapi
-uvicorn
-python-dotenv
-httpx
-openai
-langchain
-langchain-openai
-langgraph
-langchain-core
-supabase
-Pillow
-requests
-aiofiles 
+fastapi
+uvicorn
+python-dotenv
+httpx
+openai
+langchain
+langchain-openai
+langgraph
+langchain-core
+supabase