Update app.py

app.py

@@ -1,199 +1,154 @@
 import streamlit as st
-import openai
 import requests
 from bs4 import BeautifulSoup
-from urllib.parse import urljoin, urlparse
 import os
 import re
-
-# Ensure your OpenAI API key is set in your environment variables
-openai.api_key = os.getenv("OPENAI_API_KEY")
-
-def scrape_website(url, max_pages=5):
-    """
-    Crawls and scrapes content from the given website URL.
-    Follows internal links and extracts meaningful information from up to `max_pages` pages.
-    """
-    if not url.startswith("http"):
-        url = f"https://{url}"
-
-    visited = set()
-    to_visit = [url]
-    all_content = []
-    scrape_successful = False
-
-    while to_visit and len(visited) < max_pages:
-        current_url = to_visit.pop(0)
-        if current_url in visited:
-            continue
-
+from deepseek_api import DeepSeek  # Replace with actual Deepseek SDK
+
+# Configure Deepseek API
+DEEPSEEK_API_KEY = os.getenv("DEEPSEEK_API_KEY")
+ds = DeepSeek(api_key=DEEPSEEK_API_KEY)
+
+def search_neighborhood_data(query):
+    """Search for neighborhood information across various 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 = {}
+    for source, url in sources.items():
         try:
-            response = requests.get(current_url, timeout=10)
-            response.raise_for_status()
-            soup = BeautifulSoup(response.content, "html.parser")
-            visited.add(current_url)
-            scrape_successful = True
-
-            # Extract meaningful content
-            meta_description = soup.find("meta", {"name": "description"})
-            if meta_description and meta_description.get("content"):
-                all_content.append(meta_description["content"])
-
-            paragraphs = soup.find_all("p")
-            for para in paragraphs:
-                all_content.append(para.get_text(strip=True))
-
-            links = soup.find_all("a", href=True)
-            for link in links:
-                full_url = urljoin(current_url, link["href"])
-                if url in full_url and full_url not in visited:
-                    to_visit.append(full_url)
-
-        except Exception:
+            response = requests.get(url, timeout=10)
+            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
+                ...
+                
+        except Exception as e:
             continue
-
-    return " ".join(all_content[:3000]), scrape_successful
-
-def extract_location(content):
+            
+    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
     """
-    Extract a possible location from the website content using regular expressions.
-    """
-    location_match = re.search(r'\b(?:serving|located in|offices in|based in)\s([\w\s,]+)', content, re.IGNORECASE)
-    return location_match.group(1).strip() if location_match else None
-
-def infer_business_info_from_url(url):
-    """
-    Infer business details from the domain name.
-    """
-    domain_name = urlparse(url).netloc
-    inferred_info = openai.ChatCompletion.create(
-        model="gpt-4",
-        messages=[
-            {
-                "role": "system",
-                "content": "You are a business analyst. Based on domain names, generate likely information about a business, including its industry, target audience, and goals."
-            },
-            {
-                "role": "user",
-                "content": f"The domain is {domain_name}. What can you infer about this business?"
-            }
-        ]
+    
+    response = ds.generate(
+        model="neighborhood-matcher",
+        prompt=prompt,
+        max_tokens=500
     )
-    return inferred_info["choices"][0]["message"]["content"]
+    return response['choices'][0]['text']
 
-def generate_marketing_plan(content, goals, budget, location_focus, campaign_focus, location, inferred_info, messages, fallback=False):
-    """
-    Generates a marketing plan based on provided content, goals, budget, and optional focuses.
-    """
-    location_info = f"The business is located in {location}." if location else "No specific location was mentioned."
-    additional_info = f"Inferred details: {inferred_info}" if inferred_info else "No additional business details were inferred."
-    location_focus_info = "Focus on location-specific strategies." if location_focus else "No explicit focus on location-specific strategies."
-    campaign_focus_info = f"Specific focus: {campaign_focus}" if campaign_focus else "No specific campaign focus provided."
-
-    query = f"""
-    The user has provided the following details:
-    - Content: {content if not fallback else "N/A (no content provided)"}
-    - Goals for 2025: {goals}
-    - Marketing budget for 2025: ${budget}
-    - {location_info}
-    - {location_focus_info}
-    - {campaign_focus_info}
-    - {additional_info}
-
-    Create a detailed 1-year marketing plan that includes:
-    1. **Overview Summary**: Summarize the key focus areas for the business to achieve its goals. Include video marketing as a top priority.
-    2. **Advanced Keywords**: Provide at least 10 long-tail keywords specific to the industry and location (if applicable).
-    3. **Content Topics**: Provide at least 10 blog, YouTube, or social media topics, each with a brief description.
-    4. **SEO Strategies**: Detailed recommendations for improving search rankings, including tools and methods.
-    5. **Content Marketing Plan**: How to leverage the provided content topics to achieve the stated goals.
-    6. **Social Media Strategies**: Platforms, posting frequency, campaign ideas, and location-specific tactics.
-    7. **Advertising Campaigns**: Platforms, budget allocation, target audience details, and creative strategies.
-    8. **Execution Timeline**: A quarterly breakdown of actionable steps with measurable KPIs.
-
-    Ensure the recommendations are detailed, actionable, and tailored to the business's specific goals, budget, and location.
-    Avoid generic suggestions and provide unique, high-value insights.
+def generate_recommendations(criteria, locations):
+    """Generate neighborhood recommendations with Deepseek's analysis"""
+    base_prompt = f"""
+    Based on these verified neighborhood data:
+    {locations}
+    
+    And user criteria:
+    {criteria}
+    
+    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)
+    
+    For each include:
+    - Key strengths
+    - Potential drawbacks
+    - Notable amenities
+    - Average home prices
+    - Commute times
+    - Unique character
     """
     
-    messages.append({"role": "user", "content": query})
-    response = openai.ChatCompletion.create(
-        model="gpt-4",
-        messages=messages
+    response = ds.generate(
+        model="neighborhood-matcher",
+        prompt=base_prompt,
+        max_tokens=1500,
+        temperature=0.7
     )
-    return response["choices"][0]["message"]["content"]
+    return response['choices'][0]['text']
 
-# Define initial_messages
-initial_messages = [{
-    "role": "system",
-    "content": """You are a world-class marketing strategist trained by Neil Patel, David Ogilvy, and Seth Godin. 
-    Your task is to create highly customized marketing plans based on user input. Incorporate any business location 
-    or target areas explicitly mentioned in the website content or user-provided details into the recommendations.
-    Go beyond generic suggestions, and include:
-    - Specific, long-tail keywords to target.
-    - Detailed content ideas, including blogs, videos, and social media campaigns.
-    - Unique strategies tailored to the business's goals, location, and target audience.
-    - Innovative advertising campaigns and emerging platform recommendations.
-    - Video marketing as a critical strategy across all platforms.
-    Ensure every suggestion is actionable and includes measurable KPIs."""
-}]
-
-# Streamlit setup
+# Streamlit UI
 st.set_page_config(layout="wide")
-
-# Initialize session state
-if "reply" not in st.session_state:
-    st.session_state["reply"] = None
-if "show_notice" not in st.session_state:
-    st.session_state["show_notice"] = False
-
-# Centered title
-st.markdown("<h1 style='text-align: center; color: black;'>2025 Marketing Planner</h1>", unsafe_allow_html=True)
-
-# User inputs
-st.markdown("<h2 style='text-align: center; color: black;'>Enter Business Details</h2>", unsafe_allow_html=True)
-website_url = st.text_input("Enter your business website (optional)", placeholder="e.g., https://example.com")
-manual_details = st.text_area(
-    "Enter details about your business (if no website is provided or cannot be scanned)", 
-    placeholder="E.g., Business name, target audience, goals, and location."
-)
-goals = st.text_area("Goals for 2025 (optional)", placeholder="E.g., increase brand awareness, drive online sales")
-budget = st.number_input("Marketing Budget for 2025 ($)", min_value=1000, step=1000)
-
-# Additional inputs for focus areas
-location_focus = st.checkbox("Focus on location-specific strategies?")
-campaign_focus = st.text_input(
-    "Specific campaign focus (optional)", 
-    placeholder="E.g., lead generation, email campaigns, brand awareness"
-)
-
-generate_button = st.button('Generate Marketing Plan')
-
-# Process results on button click
-if generate_button:
-    st.session_state["show_notice"] = True
-    with st.spinner("Analyzing provided details and preparing your report..."):
-        # Attempt to scrape website content if a URL is provided
-        website_content, scrape_successful = scrape_website(website_url) if website_url else ("", False)
-        location = extract_location(website_content) if scrape_successful else None
-        inferred_info = infer_business_info_from_url(website_url) if not scrape_successful and website_url else None
-
-        # Use manual details as fallback content
-        content = website_content if scrape_successful else manual_details
-        fallback_mode = not scrape_successful and not manual_details
-
-        if fallback_mode:
-            st.warning("No valid website content or manual details provided. Please enter business details.")
-
-        messages = initial_messages.copy()
-        st.session_state["reply"] = generate_marketing_plan(
-            content, goals, budget, location_focus, campaign_focus, location, inferred_info, messages, fallback=fallback_mode
-        )
-        st.session_state["show_notice"] = False  # Remove the notice once the report is ready
-
-# Display the waiting notice
-if st.session_state["show_notice"]:
-    st.info("Generating your marketing plan. This process may take a minute or two. Please wait...")
-
-# Display results if there is a reply in session state
-if st.session_state["reply"]:
-    st.markdown("<h2 style='text-align: center; color: black;'>Your 2025 Marketing Plan ⬇️</h2>", unsafe_allow_html=True)
-    st.markdown(st.session_state["reply"])
+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")
+
+# Generate recommendations
+if st.button("Find My Perfect Neighborhood"):
+    with st.spinner("Analyzing preferences and searching neighborhoods..."):
+        # Collect preferences
+        preferences = {
+            "budget": budget,
+            "commute": commute,
+            "amenities": amenities,
+            "lifestyle": lifestyle,
+            "safety": safety,
+            "extra": extra
+        }
+        
+        # Analyze preferences with AI
+        search_params = analyze_preferences(preferences)
+        
+        # Search for matching locations
+        location_data = search_neighborhood_data(search_params)
+        
+        # Generate final recommendations
+        recommendations = generate_recommendations(preferences, location_data)
+        
+        # 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""")
+
+# Disclaimer
+st.markdown("---")
+st.caption("Recommendations are AI-generated and should be verified with local experts.")