Update app.py

app.py

@@ -1,10 +1,10 @@
 import streamlit as st
 import requests
 import chromadb
-from sentence_transformers import SentenceTransformer
 import json
+from sentence_transformers import SentenceTransformer
 
-# Initialize embedding model
+# Load Embedding Model
 model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
 
 # Connect to ChromaDB (Persistent)
@@ -12,35 +12,7 @@ DB_PATH = "./recipe_db"
 client = chromadb.PersistentClient(path=DB_PATH)
 collection = client.get_or_create_collection("recipes")
 
-# 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 = [
-        {"name": "Nihari", "city": "Lahore", "price": 800, "image": "https://example.com/nihari.jpg"},
-        {"name": "Karahi", "city": "Lahore", "price": 1200, "image": "https://example.com/karahi.jpg"},
-        {"name": "Biryani", "city": "Karachi", "price": 500, "image": "https://example.com/biryani.jpg"},
-        {"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"]],
-            embeddings=[embedding],
-            documents=[json.dumps(recipe)]  # Convert dictionary to string
-        )
-    print("Sample data added to ChromaDB")
-
-
-# Function to fetch restaurant data using Overpass API
+# Function to Fetch Restaurant Data Using OpenStreetMap (Overpass API)
 def get_restaurants(city):
     overpass_url = "http://overpass-api.de/api/interpreter"
     query = f"""
@@ -61,65 +33,54 @@ def get_restaurants(city):
     else:
         return ["No restaurant data found."]
 
+# Sample Food Dishes (You can expand this dataset)
+food_data = {
+    "Lahore": [
+        {"name": "Nihari", "price": "800 PKR"},
+        {"name": "Karahi", "price": "1200 PKR"},
+        {"name": "Haleem", "price": "600 PKR"}
+    ],
+    "Karachi": [
+        {"name": "Biryani", "price": "500 PKR"},
+        {"name": "Haleem", "price": "700 PKR"},
+        {"name": "Kebab Roll", "price": "300 PKR"}
+    ],
+    "Peshawar": [
+        {"name": "Chapli Kebab", "price": "400 PKR"},
+        {"name": "Dumpukht", "price": "1500 PKR"}
+    ],
+    "Multan": [
+        {"name": "Sohan Halwa", "price": "1000 PKR"},
+        {"name": "Saag", "price": "600 PKR"}
+    ]
+}
 
 # Streamlit UI
-st.title("Pakistani Famous Recipes Finder 🍛")
+st.title("Famous Pakistani Food 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.selectbox("Select a Recipe (Optional)", ["Any"] + recipe_categories[recipe_type])
-
-if st.button("Find Recipes & Restaurants"):
+if st.button("Find Food & Restaurants"):
     if city:
-        if query != "Any":
-            # Retrieve specific recipe info from vector DB
-            query_embedding = model.encode(query).tolist()
-            results = collection.query(query_embedding, n_results=5)
-
-            if results and "documents" in results and results["documents"]:
-                st.subheader(f"Famous {query} in {city}")
-                for doc in results["documents"]:
-                    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_container_width=True)
-                        st.write(f"Price: {recipe['price']} PKR")
-
-                # Fetch restaurant data
-                restaurants = get_restaurants(city)
-                if restaurants:
-                    st.subheader("Available at These Restaurants:")
-                    for r in restaurants:
-                        st.write(f"- {r}")
-                else:
-                    st.write("No restaurant data found.")
-            else:
-                st.write(f"No matching recipes found for '{query}' in {city}.")
-
+        st.subheader(f"Famous Foods in {city}")
+
+        # Retrieve food data for the city
+        dishes = food_data.get(city, [])
+        if dishes:
+            for dish in dishes:
+                st.write(f"**Dish:** {dish['name']}")
+                st.write(f"**Price:** {dish['price']}")
+                st.markdown("---")
         else:
-            # Retrieve all famous recipes in the city
-            city_embedding = model.encode(city).tolist()
-            results = collection.query(city_embedding, n_results=5)
-
-            if results and "documents" in results and results["documents"]:
-                st.subheader(f"Famous Recipes in {city}")
-                for doc in results["documents"]:
-                    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_container_width=True)
-                        st.write(f"Price: {recipe['price']} PKR")
-
-            # Fetch restaurant data for multiple recipes
-            restaurants = get_restaurants(city)
-            if restaurants:
-                st.subheader("Popular Restaurants in This City:")
-                for r in restaurants:
-                    st.write(f"- {r}")
+            st.write("No data available for this city. Please add more dishes!")
+
+        # Retrieve restaurant data
+        st.subheader(f"Popular Restaurants in {city}")
+        restaurants = get_restaurants(city)
+        if restaurants:
+            for r in restaurants:
+                st.write(f"- {r}")
+        else:
+            st.write("No restaurant data found.")
     else:
         st.warning("Please enter a city name.")