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app_deploy.py

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+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()

requirements.txt

@@ -1,3 +1,5 @@
-altair
-pandas
-streamlit
+streamlit==1.45.1
+requests==2.31.0
+pandas>=2.1.0
+together==1.5.8
+numpy>=1.24.0