Update app.py

app.py

@@ -15,6 +15,10 @@ def load_recipes():
 
 recipes_df = load_recipes()
 
+if recipes_df is None:
+    st.error("❌ Failed to load dataset! Check internet or dataset availability.")
+    st.stop()  # Stops Streamlit from running further if the dataset isn't loaded
+
 # Load embedding model
 @st.cache_resource
 def load_embedding_model():
@@ -26,18 +30,23 @@ embed_model = load_embedding_model()
 chroma_client = chromadb.PersistentClient(path="./chroma_db")  # Saves vectors
 recipe_collection = chroma_client.get_or_create_collection(name="recipes")
 
-# If embeddings are not stored, create them
-if recipe_collection.count() == 0:
-    st.info("Indexing recipes... This will take a few minutes.")
-    for i, recipe in enumerate(recipes_df):
-        title = recipe["title"]
-        ingredients = ", ".join(recipe["ingredients"])
-        embedding = embed_model.encode(title).tolist()
-        recipe_collection.add(
-            ids=[str(i)],
-            embeddings=[embedding],
-            metadatas=[{"title": title, "ingredients": ingredients, "index": i}],
-        )
+# Ensure recipes_df is iterable
+if isinstance(recipes_df, list) or isinstance(recipes_df, dict):
+    if recipe_collection.count() == 0:
+        st.info("Indexing recipes... This will take a few minutes.")
+        for i, recipe in enumerate(recipes_df):
+            title = recipe.get("title", "Unknown Title")  # Handle missing keys
+            ingredients = ", ".join(recipe.get("ingredients", []))
+            instructions = recipe.get("instructions", "No instructions available")
+            
+            embedding = embed_model.encode(title).tolist()
+            recipe_collection.add(
+                ids=[str(i)],
+                embeddings=[embedding],
+                metadatas=[{"title": title, "ingredients": ingredients, "index": i}],
+            )
+else:
+    st.error("❌ Dataset is not in the correct format!")
 
 # UI
 st.title("🍽️ AI Recipe Finder with ChromaDB RAG")
@@ -53,9 +62,9 @@ if query:
     for result in results["metadatas"][0]:
         index = result["index"]
         recipe = recipes_df[index]
-        st.write(f"**🍴 {recipe['title']}**")
-        st.write(f"**Ingredients:** {', '.join(recipe['ingredients'])}")
-        st.write(f"**Instructions:** {recipe['instructions']}")
+        st.write(f"**🍴 {recipe.get('title', 'No title available')}**")
+        st.write(f"**Ingredients:** {', '.join(recipe.get('ingredients', []))}")
+        st.write(f"**Instructions:** {recipe.get('instructions', 'No instructions available')}")
         st.write("---")
 else:
     st.info("Type a recipe name to find similar recipes.")