Update app.py

app.py

@@ -1,154 +1,133 @@
 import streamlit as st
 import requests
 from bs4 import BeautifulSoup
-import os
+from geopy.geocoders import Nominatim
+from urllib.parse import urljoin, urlparse
 import re
-from deepseek_api import DeepSeek  # Replace with actual Deepseek SDK
+import os
+import pandas as pd
+import folium
+from streamlit_folium import folium_static
 
-# Configure Deepseek API
-DEEPSEEK_API_KEY = os.getenv("DEEPSEEK_API_KEY")
-ds = DeepSeek(api_key=DEEPSEEK_API_KEY)
+# Configure environment
+DEEPSEEK_API_KEY = os.getenv("DEEPSEEK_KEY")
+API_ENDPOINT = "https://api.deepseek.com/v1/chat/completions"
 
-def search_neighborhood_data(query):
-    """Search for neighborhood information across various sources"""
+# Cache expensive operations
+@st.cache_data
+def scrape_location_data(query):
+    """Scrape location data from public sources"""
     sources = {
         "Niche": f"https://www.niche.com/places-to-live/search/{query}",
         "AreaVibes": f"https://www.areavibes.com/search/?query={query}",
-        "Walkscore": f"https://www.walkscore.com/score/{query}"
     }
     
-    results = {}
+    results = []
     for source, url in sources.items():
         try:
-            response = requests.get(url, timeout=10)
+            response = requests.get(url, timeout=15)
             soup = BeautifulSoup(response.text, 'html.parser')
             
             if source == "Niche":
                 listings = soup.find_all('div', class_='search-results__list__item')
-                results[source] = [{
-                    'name': item.find('h2').text.strip(),
-                    'details': item.find('div', class_='search-result-tagline').text.strip(),
-                    'score': item.find('div', class_='search-result-grade').text.strip()
-                } for item in listings[:3]]
-                
-            elif source == "AreaVibes":
-                # Similar parsing for other sources
-                ...
-                
+                for item in listings[:3]:
+                    results.append({
+                        'name': item.find('h2').text.strip(),
+                        'details': item.find('div', class_='search-result-tagline').text.strip(),
+                        'score': item.find('div', class_='search-result-grade').text.strip()
+                    })
+                    
         except Exception as e:
             continue
             
     return results
 
-def analyze_preferences(preferences):
-    """Use Deepseek to analyze user preferences and generate search parameters"""
-    prompt = f"""
-    User preferences: {preferences}
-    Generate search parameters for neighborhood hunting considering:
-    - Key demographic factors
-    - Important amenities
-    - Commute considerations
-    - Lifestyle priorities
-    - Budget constraints
-    """
-    
-    response = ds.generate(
-        model="neighborhood-matcher",
-        prompt=prompt,
-        max_tokens=500
-    )
-    return response['choices'][0]['text']
-
-def generate_recommendations(criteria, locations):
-    """Generate neighborhood recommendations with Deepseek's analysis"""
-    base_prompt = f"""
-    Based on these verified neighborhood data:
-    {locations}
+def generate_recommendations(preferences):
+    """Generate neighborhood recommendations using Deepseek API"""
+    headers = {
+        "Authorization": f"Bearer {DEEPSEEK_API_KEY}",
+        "Content-Type": "application/json"
+    }
     
-    And user criteria:
-    {criteria}
+    prompt = f"""
+    Create a neighborhood recommendation report based on these preferences:
+    {preferences}
     
-    Create 5 recommendations including:
-    1. Best overall match
-    2. Best value option
-    3. Best for families
-    4. Best for young professionals
-    5. 'Outside the Box' option (creative suggestion)
+    Include these sections:
+    1. Top 5 Neighborhood Matches
+    2. Hidden Gem Recommendation
+    3. Key Amenities Analysis
+    4. Commute Times Overview
+    5. Safety & Community Insights
     
-    For each include:
-    - Key strengths
-    - Potential drawbacks
-    - Notable amenities
-    - Average home prices
-    - Commute times
-    - Unique character
+    Format with markdown headers and bullet points. Keep sections concise.
     """
     
-    response = ds.generate(
-        model="neighborhood-matcher",
-        prompt=base_prompt,
-        max_tokens=1500,
-        temperature=0.7
-    )
-    return response['choices'][0]['text']
+    try:
+        response = requests.post(
+            API_ENDPOINT,
+            json={
+                "model": "deepseek-chat",
+                "messages": [{"role": "user", "content": prompt}],
+                "temperature": 0.7,
+                "max_tokens": 1500
+            },
+            headers=headers,
+            timeout=30
+        )
+        return response.json()["choices"][0]["message"]["content"]
+    except Exception as e:
+        st.error(f"API Error: {str(e)}")
+        return None
 
 # Streamlit UI
-st.set_page_config(layout="wide")
-st.title("🏡 Neighborhood Matchmaker")
+st.set_page_config(layout="wide", page_icon="🏡")
+st.title("Neighborhood Matchmaker")
 
-with st.expander("🔍 Your Lifestyle Preferences"):
-    col1, col2 = st.columns(2)
-    with col1:
-        budget = st.slider("Monthly Housing Budget ($)", 1000, 10000, 3000)
-        commute = st.selectbox("Max Commute Time", ["15 mins", "30 mins", "45 mins", "1 hour"])
-        amenities = st.multiselect("Must-Have Amenities", [
-            "Good Schools", "Parks", "Public Transport", 
-            "Nightlife", "Shopping Centers", "Healthcare"
-        ])
-        
-    with col2:
-        lifestyle = st.selectbox("Primary Lifestyle", [
-            "Family-Friendly", "Urban Professional", "Retirement", 
-            "Student", "Remote Worker", "Outdoor Enthusiast"
-        ])
-        safety = st.slider("Safety Priority (1-10)", 1, 10, 8)
-        extra = st.text_input("Special Requirements", placeholder="e.g., Dog parks, historic district")
+with st.sidebar:
+    st.header("Search Preferences")
+    city = st.text_input("City/Region", "New York, NY")
+    budget = st.slider("Monthly Housing Budget ($)", 1000, 10000, 3000)
+    commute = st.selectbox("Max Commute Time", ["15 mins", "30 mins", "45 mins", "1 hour"])
+    amenities = st.multiselect("Must-Have Amenities", [
+        "Good Schools", "Parks", "Public Transport", 
+        "Nightlife", "Shopping", "Healthcare"
+    ])
+    lifestyle = st.selectbox("Lifestyle Preference", [
+        "Family-Friendly", "Urban Professional", "Retirement", 
+        "Student", "Remote Worker", "Outdoor Enthusiast"
+    ])
 
-# Generate recommendations
-if st.button("Find My Perfect Neighborhood"):
-    with st.spinner("Analyzing preferences and searching neighborhoods..."):
+if st.button("Find My Neighborhood"):
+    with st.spinner("Analyzing locations..."):
         # Collect preferences
         preferences = {
-            "budget": budget,
-            "commute": commute,
+            "city": city,
+            "budget": f"${budget}/mo",
+            "max_commute": commute,
             "amenities": amenities,
-            "lifestyle": lifestyle,
-            "safety": safety,
-            "extra": extra
+            "lifestyle": lifestyle
         }
         
-        # Analyze preferences with AI
-        search_params = analyze_preferences(preferences)
+        # Get location data
+        location_data = scrape_location_data(city)
         
-        # Search for matching locations
-        location_data = search_neighborhood_data(search_params)
-        
-        # Generate final recommendations
-        recommendations = generate_recommendations(preferences, location_data)
+        # Generate recommendations
+        report = generate_recommendations(preferences)
         
         # Display results
-        st.subheader("Your Custom Neighborhood Recommendations")
-        st.markdown(recommendations)
-        
-        # Always include an "outside the box" suggestion
-        st.subheader("🚀 Outside the Box Option")
-        st.markdown("""**Emerging Neighborhood - Innovation District**  
-                    *Why we suggest it:*  
-                    - Up-and-coming tech hub with new amenities  
-                    - Lower prices before expected growth  
-                    - Community development initiatives  
-                    *Consider if:* You want to get in early on a growing area""")
+        if report:
+            st.subheader("Your Personalized Neighborhood Report")
+            st.markdown(report)
+            
+            # Show map
+            try:
+                geolocator = Nominatim(user_agent="neighborhood_finder")
+                location = geolocator.geocode(city)
+                m = folium.Map(location=[location.latitude, location.longitude], zoom_start=12)
+                folium_static(m, width=1200, height=500)
+            except Exception as e:
+                st.warning("Couldn't generate map visualization")
 
-# Disclaimer
 st.markdown("---")
-st.caption("Recommendations are AI-generated and should be verified with local experts.")
+st.caption("Note: Recommendations generated by AI. Verify with local experts.")