Update app.py

app.py

@@ -3,14 +3,35 @@ import requests
 import chromadb
 from sentence_transformers import SentenceTransformer
 from bs4 import BeautifulSoup
+import os
 
-# Initialize embedding model (Open-Source)
+# Initialize embedding model
 model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
 
-# Connect to ChromaDB (Local Open-Source Vector DB)
-client = chromadb.PersistentClient(path="./recipe_db")
+# Connect to ChromaDB (Persistent)
+DB_PATH = "./recipe_db"
+client = chromadb.PersistentClient(path=DB_PATH)
 collection = client.get_or_create_collection("recipes")
 
+# 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=[recipe]
+        )
+    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}"