Update app.py

app.py

@@ -2,8 +2,8 @@ import streamlit as st
 import requests
 import chromadb
 from sentence_transformers import SentenceTransformer
-from bs4 import BeautifulSoup
-import json  # Import json to convert dictionary to string
+import json
+import googlemaps
 
 # Initialize embedding model
 model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
@@ -13,6 +13,18 @@ DB_PATH = "./recipe_db"
 client = chromadb.PersistentClient(path=DB_PATH)
 collection = client.get_or_create_collection("recipes")
 
+# Google Places API Key (Replace with your key)
+GOOGLE_API_KEY = "YOUR_GOOGLE_PLACES_API_KEY"
+gmaps = googlemaps.Client(key=GOOGLE_API_KEY)
+
+# Predefined Recipe Categories
+recipe_categories = {
+    "Desi": ["Nihari", "Karahi", "Biryani", "Haleem", "Saag"],
+    "Fast Food": ["Burger", "Pizza", "Fries", "Shawarma"],
+    "BBQ": ["Tikka", "Seekh Kebab", "Malai Boti"],
+    "Seafood": ["Prawn Karahi", "Grilled Fish", "Fried Fish"]
+}
+
 # Check if ChromaDB has data, if not, insert sample data
 if not collection.count():
     sample_recipes = [
@@ -22,29 +34,27 @@ if not collection.count():
         {"name": "Chapli Kebab", "city": "Peshawar", "price": 400, "image": "https://example.com/chapli.jpg"},
         {"name": "Saag", "city": "Multan", "price": 600, "image": "https://example.com/saag.jpg"}
     ]
-    
+
     for recipe in sample_recipes:
         embedding = model.encode(recipe["city"]).tolist()
         collection.add(
-            ids=[recipe["name"]],  # IDs should be unique
+            ids=[recipe["name"]],
             embeddings=[embedding],
             documents=[json.dumps(recipe)]  # Convert dictionary to string
         )
     print("Sample data added to ChromaDB")
 
-# Function to scrape restaurant data
-def scrape_restaurant_info(city, recipe):
-    search_url = f"https://www.foodpanda.pk/restaurants?search={recipe}+{city}"
-    headers = {"User-Agent": "Mozilla/5.0"}
-    response = requests.get(search_url, headers=headers)
-
+# Function to fetch restaurant data using Google Places API
+def get_restaurants(city, recipe):
+    query = f"{recipe} restaurant in {city}"
+    places_result = gmaps.places(query=query, type="restaurant")
+    
     restaurant_list = []
-    if response.status_code == 200:
-        soup = BeautifulSoup(response.text, "html.parser")
-        items = soup.find_all("div", class_="vendor-info")
-        for item in items[:5]:  # Get top 5 restaurants
-            name = item.find("span", class_="name").text if item.find("span", class_="name") else "Unknown"
-            restaurant_list.append(name)
+    if "results" in places_result:
+        for place in places_result["results"][:5]:  # Get top 5 restaurants
+            name = place.get("name", "Unknown Restaurant")
+            address = place.get("vicinity", "Unknown Address")
+            restaurant_list.append(f"{name} - {address}")
     return restaurant_list
 
 # Streamlit UI
@@ -53,12 +63,15 @@ st.title("Pakistani Famous Recipes Finder 🍛")
 # User inputs city
 city = st.text_input("Enter a Pakistani City (e.g., Lahore, Karachi, Islamabad)").strip()
 
+# User selects recipe type
+recipe_type = st.selectbox("Select Recipe Type", options=list(recipe_categories.keys()))
+
 # Optional: User inputs recipe (not mandatory)
-query = st.text_input("Enter a Recipe Name (Optional)").strip()
+query = st.selectbox("Select a Recipe (Optional)", ["Any"] + recipe_categories[recipe_type])
 
 if st.button("Find Recipes & Restaurants"):
     if city:
-        if query:
+        if query != "Any":
             # Retrieve specific recipe info from vector DB
             query_embedding = model.encode(query).tolist()
             results = collection.query(query_embedding, n_results=5)
@@ -69,11 +82,11 @@ if st.button("Find Recipes & Restaurants"):
                     for recipe_json in doc:
                         recipe = json.loads(recipe_json)  # Convert back to dictionary
                         st.write(f"**Recipe:** {recipe['name']}")
-                        st.image(recipe["image"], caption=recipe["name"], use_column_width=True)
+                        st.image(recipe["image"], caption=recipe["name"], use_container_width=True)
                         st.write(f"Price: {recipe['price']} PKR")
 
-                # Fetch restaurant data via scraping
-                restaurants = scrape_restaurant_info(city, query)
+                # Fetch restaurant data
+                restaurants = get_restaurants(city, query)
                 if restaurants:
                     st.subheader("Available at These Restaurants:")
                     for r in restaurants:
@@ -94,9 +107,15 @@ if st.button("Find Recipes & Restaurants"):
                     for recipe_json in doc:
                         recipe = json.loads(recipe_json)  # Convert back to dictionary
                         st.write(f"**Recipe:** {recipe['name']}")
-                        st.image(recipe["image"], caption=recipe["name"], use_column_width=True)
+                        st.image(recipe["image"], caption=recipe["name"], use_container_width=True)
                         st.write(f"Price: {recipe['price']} PKR")
-            else:
-                st.write(f"No famous recipes found for {city}.")
+
+            # Fetch restaurant data for multiple recipes
+            for recipe_name in recipe_categories[recipe_type]:
+                restaurants = get_restaurants(city, recipe_name)
+                if restaurants:
+                    st.subheader(f"Where to find {recipe_name}:")
+                    for r in restaurants:
+                        st.write(f"- {r}")
     else:
         st.warning("Please enter a city name.")