Delete app_deploy.py

app_deploy.py

@@ -1,448 +0,0 @@
-import streamlit as st
-import requests
-import pandas as pd
-from together import Together
-import os
-
-# =============================================================================
-# CONFIGURATION - Using Secrets Management
-# =============================================================================
-NOCODB_URL = "https://app.nocodb.com"  # Base URL
-
-# Get sensitive data from Streamlit secrets or environment variables
-def get_api_credentials():
-    """Get API credentials from secrets or environment"""
-    try:
-        # Try Streamlit secrets first (for Hugging Face Spaces)
-        api_token = st.secrets.get("NOCODB_API_TOKEN", os.environ.get("NOCODB_API_TOKEN", ""))
-        together_key = st.secrets.get("TOGETHER_API_KEY", os.environ.get("TOGETHER_API_KEY", ""))
-        endpoint_path = st.secrets.get("NOCODB_ENDPOINT_PATH", os.environ.get("NOCODB_ENDPOINT_PATH", ""))
-        
-        return api_token, together_key, endpoint_path
-    except:
-        # Fallback to environment variables
-        api_token = os.environ.get("NOCODB_API_TOKEN", "")
-        together_key = os.environ.get("TOGETHER_API_KEY", "")
-        endpoint_path = os.environ.get("NOCODB_ENDPOINT_PATH", "")
-        
-        return api_token, together_key, endpoint_path
-
-# Initialize Together AI client
-@st.cache_resource
-def get_ai_client():
-    """Initialize Together AI client"""
-    _, together_key, _ = get_api_credentials()
-    if not together_key:
-        st.error("Together AI API key not found. Please configure it in the secrets.")
-        return None
-    return Together(api_key=together_key)
-
-# =============================================================================
-# HELPER FUNCTIONS
-# =============================================================================
-def safe_int(value, default=0):
-    """Safely convert value to integer"""
-    try:
-        return int(float(value)) if value else default
-    except (ValueError, TypeError):
-        return default
-
-def safe_float(value, default=0.0):
-    """Safely convert value to float"""
-    try:
-        return float(value) if value else default
-    except (ValueError, TypeError):
-        return default
-
-@st.cache_data(ttl=300)  # Cache for 5 minutes
-def get_properties():
-    """Fetch properties from NocoDB"""
-    api_token, _, endpoint_path = get_api_credentials()
-    
-    if not api_token or not endpoint_path:
-        st.error("NocoDB credentials not configured. Please set up your secrets.")
-        return []
-    
-    headers = {"xc-token": api_token}
-    
-    try:
-        response = requests.get(
-            f"{NOCODB_URL}{endpoint_path}?limit=1000",  # Get more records
-            headers=headers
-        )
-        
-        if response.status_code == 200:
-            data = response.json()
-            return data.get('list', [])
-        else:
-            st.error(f"Failed to fetch data: {response.status_code}")
-            return []
-            
-    except Exception as e:
-        st.error(f"Error connecting to database: {e}")
-        return []
-
-def filter_properties(properties, filters):
-    """Apply filters to properties list"""
-    filtered = []
-    
-    for prop in properties:
-        # Price filter
-        price = safe_int(prop.get('cash_price'))
-        if price > filters['max_price']:
-            continue
-            
-        # Rooms filter
-        rooms = safe_int(prop.get('rooms'))
-        if rooms < filters['min_rooms']:
-            continue
-            
-        # Energy rating filter
-        if filters['energy_ratings'] and prop.get('energy_rating') not in filters['energy_ratings']:
-            continue
-            
-        # City filter
-        if filters['cities'] and prop.get('city') not in filters['cities']:
-            continue
-            
-        filtered.append(prop)
-    
-    return filtered
-
-def create_property_context(properties):
-    """Create context string about current properties for AI"""
-    if not properties:
-        return "No properties match the current filters."
-    
-    total = len(properties)
-    prices = [safe_int(p.get('cash_price')) for p in properties if safe_int(p.get('cash_price')) > 0]
-    
-    if prices:
-        avg_price = sum(prices) / len(prices)
-        min_price = min(prices)
-        max_price = max(prices)
-        
-        context = f"""Currently showing {total} Danish villas. 
-        Price range: {min_price:,} - {max_price:,} DKK. 
-        Average price: {avg_price:,.0f} DKK. """
-    else:
-        context = f"Currently showing {total} Danish villas. "
-    
-    # Add some location info
-    cities = list(set([p.get('city', 'Unknown') for p in properties[:10]]))
-    if cities:
-        context += f"Cities include: {', '.join(cities[:5])}. "
-    
-    return context
-
-def get_ai_response(client, question, context, model_name):
-    """Get response from Together AI"""
-    try:
-        # Create a comprehensive prompt
-        prompt = f"""You are a helpful Danish real estate assistant. Based on the current property data, please answer the user's question accurately and helpfully.
-
-Current Property Data Context:
-{context}
-
-User Question: {question}
-
-Please provide a helpful, accurate response based on the data provided. Keep your answer concise but informative."""
-
-        response = client.chat.completions.create(
-            model=model_name,
-            messages=[
-                {"role": "system", "content": "You are a helpful Danish real estate assistant with expertise in property analysis and market insights."},
-                {"role": "user", "content": prompt}
-            ],
-            max_tokens=300,
-            temperature=0.7,
-        )
-        
-        return response.choices[0].message.content
-        
-    except Exception as e:
-        raise Exception(f"Together AI Error: {str(e)}")
-
-def test_together_models():
-    """Test different Together AI models"""
-    # Include both Gemma and other reliable serverless models
-    models_to_test = [
-        # Gemma models (Google's lightweight models)
-        "google/gemma-2b-it", 
-        "google/gemma-2-27b-it",
-        # Other reliable models
-        "mistralai/Mistral-7B-Instruct-v0.1",
-        "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
-        "mistralai/Mixtral-8x7B-Instruct-v0.1"
-    ]
-    
-    results = {}
-    client = get_ai_client()
-    
-    if not client:
-        return {"error": "Could not initialize AI client"}
-    
-    for model_name in models_to_test:
-        try:
-            test_response = client.chat.completions.create(
-                model=model_name,
-                messages=[
-                    {"role": "system", "content": "You are a helpful assistant."},
-                    {"role": "user", "content": "Hello, can you help me analyze real estate data?"}
-                ],
-                max_tokens=50,
-                temperature=0.7,
-            )
-            
-            results[model_name] = {
-                "status": "✅ Success", 
-                "response": test_response.choices[0].message.content[:100]
-            }
-            
-        except Exception as e:
-            results[model_name] = {"status": "❌ Error", "response": str(e)[:100]}
-    
-    return results
-
-# =============================================================================
-# MAIN APP
-# =============================================================================
-def main():
-    # Page config
-    st.set_page_config(
-        page_title="Danish Villa Assistant",
-        page_icon="🏡",
-        layout="wide"
-    )
-    
-    # Header
-    st.title("🏡 Danish Villa Assistant")
-    st.write("Explore Danish villas with AI-powered insights using Together AI!")
-    
-    # Check API credentials
-    api_token, together_key, endpoint_path = get_api_credentials()
-    
-    if not together_key:
-        st.error("⚠️ Together AI API key not configured!")
-        st.info("Please set your TOGETHER_API_KEY in the Hugging Face Spaces secrets.")
-        st.stop()
-    
-    if not api_token or not endpoint_path:
-        st.error("⚠️ NocoDB credentials not configured!")
-        st.info("Please set NOCODB_API_TOKEN and NOCODB_ENDPOINT_PATH in the Hugging Face Spaces secrets.")
-        st.stop()
-    
-    # Add model testing section
-    with st.expander("🧪 Test Together AI Models (for debugging)"):
-        if st.button("Test Different Models"):
-            with st.spinner("Testing models..."):
-                test_results = test_together_models()
-                for model, result in test_results.items():
-                    st.write(f"**{model}:** {result['status']}")
-                    if result['status'] == "✅ Success":
-                        st.success(f"Response preview: {result['response']}")
-                    else:
-                        st.error(f"Error: {result['response']}")
-    
-    # Initialize AI client
-    try:
-        client = get_ai_client()
-        if not client:
-            st.stop()
-    except Exception as e:
-        st.error(f"Failed to initialize Together AI client: {e}")
-        st.stop()
-    
-    # Sidebar filters
-    st.sidebar.header("🔍 Filter Properties")
-    
-    # Get all properties first to populate filter options
-    with st.spinner("Loading properties..."):
-        all_properties = get_properties()
-    
-    if not all_properties:
-        st.error("Could not load properties. Please check your NocoDB connection.")
-        st.stop()
-    
-    # Extract unique values for filters
-    all_cities = sorted(list(set([p.get('city', 'Unknown') for p in all_properties if p.get('city')])))
-    all_energy_ratings = sorted(list(set([p.get('energy_rating') for p in all_properties if p.get('energy_rating')])))
-    
-    # Sidebar filter controls
-    max_price = st.sidebar.slider(
-        "Maximum Price (DKK)", 
-        min_value=0, 
-        max_value=20000000, 
-        value=10000000, 
-        step=500000,
-        format="%d"
-    )
-    
-    min_rooms = st.sidebar.slider(
-        "Minimum Rooms", 
-        min_value=1, 
-        max_value=15, 
-        value=3
-    )
-    
-    selected_cities = st.sidebar.multiselect(
-        "Cities", 
-        options=all_cities,
-        default=[]
-    )
-    
-    selected_energy_ratings = st.sidebar.multiselect(
-        "Energy Ratings", 
-        options=all_energy_ratings,
-        default=[]
-    )
-    
-    # Create filter dictionary
-    filters = {
-        'max_price': max_price,
-        'min_rooms': min_rooms,
-        'cities': selected_cities,
-        'energy_ratings': selected_energy_ratings
-    }
-    
-    # Apply filters
-    filtered_properties = filter_properties(all_properties, filters)
-    
-    # Main content area
-    col1, col2 = st.columns([2, 1])
-    
-    with col1:
-        # Property listings
-        st.subheader(f"📋 Found {len(filtered_properties)} Properties")
-        
-        if filtered_properties:
-            # Show first 10 properties
-            for i, prop in enumerate(filtered_properties[:10]):
-                with st.expander(
-                    f"{prop.get('address', 'N/A')} - {safe_int(prop.get('cash_price')):,} DKK"
-                ):
-                    # Property details in columns
-                    detail_col1, detail_col2, detail_col3 = st.columns(3)
-                    
-                    with detail_col1:
-                        st.write(f"**🏙️ City:** {prop.get('city', 'N/A')}")
-                        st.write(f"**🚪 Rooms:** {prop.get('rooms', 'N/A')}")
-                        st.write(f"**📐 Living Area:** {prop.get('living_area', 'N/A')} m²")
-                    
-                    with detail_col2:
-                        st.write(f"**⚡ Energy Rating:** {prop.get('energy_rating', 'N/A')}")
-                        st.write(f"**📅 Year Built:** {prop.get('year_built', 'N/A')}")
-                        st.write(f"**🏛️ Municipality:** {prop.get('municipal', 'N/A')}")
-                    
-                    with detail_col3:
-                        price_per_sqm = safe_int(prop.get('square_meter_price'))
-                        st.write(f"**💰 Price/m²:** {price_per_sqm:,} DKK" if price_per_sqm else "**💰 Price/m²:** N/A")
-                        
-                        plot_area = safe_int(prop.get('area'))
-                        st.write(f"**🌿 Plot Area:** {plot_area:,} m²" if plot_area else "**🌿 Plot Area:** N/A")
-                        
-                        st.write(f"**🏠 Type:** {prop.get('legal_type', 'N/A')}")
-            
-            if len(filtered_properties) > 10:
-                st.info(f"Showing first 10 of {len(filtered_properties)} properties. Adjust filters to narrow results.")
-        else:
-            st.info("No properties match your current filters. Try adjusting the criteria.")
-    
-    with col2:
-        # AI Chat Section
-        st.subheader("🤖 Ask AI Assistant")
-        st.write("Ask questions about the Danish villa market!")
-        
-        # Model selection for Together AI
-        model_choice = st.selectbox(
-            "Select AI Model:",
-            [
-                # Gemma models (Google's efficient models)
-                "google/gemma-2b-it",
-                "google/gemma-2-27b-it",
-                # Other reliable models
-                "mistralai/Mistral-7B-Instruct-v0.1",
-                "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
-                "mistralai/Mixtral-8x7B-Instruct-v0.1"
-            ],
-            help="Gemma models are Google's efficient, lightweight models."
-        )
-        
-        # Example questions
-        with st.expander("💡 Example Questions"):
-            st.write("• What's the average price range?")
-            st.write("• Tell me about energy ratings in the data")
-            st.write("• Which areas have the most expensive properties?")
-            st.write("• How many properties are available in each city?")
-            st.write("• What's the price per square meter trend?")
-        
-        user_question = st.text_area(
-            "Your Question:",
-            placeholder="Ask about prices, locations, energy ratings, market trends...",
-            height=100
-        )
-        
-        if st.button("🔍 Ask AI", type="primary"):
-            if user_question:
-                with st.spinner("AI is analyzing the data..."):
-                    # Create context from current filtered data
-                    context = create_property_context(filtered_properties)
-                    
-                    try:
-                        # Get AI response
-                        ai_response = get_ai_response(client, user_question, context, model_choice)
-                        
-                        st.success("**AI Assistant Response:**")
-                        st.write(ai_response)
-                        
-                        # Show debug info
-                        with st.expander("Debug Info"):
-                            st.write(f"Model used: {model_choice}")
-                            st.write(f"Properties analyzed: {len(filtered_properties)}")
-                            st.write(f"Context: {context[:150]}...")
-                        
-                    except Exception as e:
-                        st.error(f"AI Error: {str(e)}")
-                        
-                        # Fallback response with data analysis
-                        st.info("**Fallback Analysis:**")
-                        if filtered_properties:
-                            avg_price = sum(safe_int(p.get('cash_price')) for p in filtered_properties) / len(filtered_properties)
-                            st.write(f"• Found {len(filtered_properties)} properties")
-                            st.write(f"• Average price: {avg_price:,.0f} DKK")
-                            
-                            cities = list(set(p.get('city') for p in filtered_properties if p.get('city')))
-                            if cities:
-                                st.write(f"• Cities: {', '.join(cities[:3])}")
-                                
-                            energy_ratings = list(set(p.get('energy_rating') for p in filtered_properties if p.get('energy_rating')))
-                            if energy_ratings:
-                                st.write(f"• Energy ratings: {', '.join(energy_ratings[:3])}")
-            else:
-                st.warning("Please enter a question first!")
-    
-    # Footer stats
-    st.markdown("---")
-    if all_properties:
-        total_props = len(all_properties)
-        filtered_props = len(filtered_properties)
-        
-        stat_col1, stat_col2, stat_col3, stat_col4 = st.columns(4)
-        
-        with stat_col1:
-            st.metric("Total Properties", total_props)
-        
-        with stat_col2:
-            st.metric("Filtered Results", filtered_props)
-        
-        with stat_col3:
-            if filtered_properties:
-                avg_price = sum(safe_int(p.get('cash_price')) for p in filtered_properties) / len(filtered_properties)
-                st.metric("Avg Price", f"{avg_price:,.0f} DKK")
-        
-        with stat_col4:
-            unique_cities = len(set(p.get('city') for p in filtered_properties if p.get('city')))
-            st.metric("Cities", unique_cities)
-
-if __name__ == "__main__":
-    main()