Update app.py

app.py

@@ -11,67 +11,68 @@ df = pd.read_csv("food_order_cleaned.csv")
 
 model = SentenceTransformer("all-MiniLM-L6-v2")
 
-TEXT_FIELDS = ["restaurant_name", "cuisine_type", "rating", "cost_of_the_order"]
-
 def create_text(row):
-    text = (
+    return (
         f"Restaurant: {row['restaurant_name']}. "
         f"Cuisine: {row['cuisine_type']}. "
         f"Rating: {row['rating']}. "
         f"Cost: {row['cost_of_the_order']}."
     )
-    return text
 
 df["text"] = df.apply(create_text, axis=1)
 corpus_embeddings = model.encode(df["text"].tolist(), normalize_embeddings=True)
 
 # -----------------------------
-# CHAT LOGIC
+# CHATBOT LOGIC
 # -----------------------------
 def bot_reply(user_query, history):
     if not user_query.strip():
-        return history + [("","Please enter a valid question.")]
+        history.append(["", "Please enter a valid question."])
+        return history
 
+    # Encode and find best match
     q_emb = model.encode([user_query], normalize_embeddings=True)
     sims = cosine_similarity(q_emb, corpus_embeddings)[0]
-    idx = np.argmax(sims)
-    best_match = df.iloc[idx]
+    idx = int(np.argmax(sims))
+    best = df.iloc[idx]
 
+    # Build chatbot response
     response = (
         f"🍽 **Recommended Restaurant**\n\n"
-        f"**Name:** {best_match['restaurant_name']}\n"
-        f"**Cuisine:** {best_match['cuisine_type']}\n"
-        f"**Rating:** ⭐ {best_match['rating']}\n"
-        f"**Avg Cost:** {best_match['cost_of_the_order']}\n\n"
-        f"Let me know if you want more options!"
+        f"**Name:** {best['restaurant_name']}\n"
+        f"**Cuisine:** {best['cuisine_type']}\n"
+        f"**Rating:** ⭐ {best['rating']}\n"
+        f"**Avg Cost:** {best['cost_of_the_order']}\n\n"
+        f"Let me know if you want more recommendations!"
     )
 
-    history = history + [(user_query, response)]
+    # IMPORTANT → Must return LISTS, not tuples
+    history.append([user_query, response])
     return history
 
 
 # -----------------------------
-# GRADIO UI (Safe Version)
+# GRADIO UI
 # -----------------------------
 with gr.Blocks() as demo:
 
     gr.Markdown(
         """
         # 🍴 Restaurant Guide Chatbot  
-        Ask me for restaurant recommendations!
+        Ask me anything—I'll help you find the best place to eat!
         """
     )
 
-    chat = gr.Chatbot(label="Chat with Restaurant Guide", height=500)
+    chatbox = gr.Chatbot(label="Chat Window", height=450)
+
+    user_input = gr.Textbox(
+        label="Ask something",
+        placeholder="Examples: Find me a Thai restaurant, Best Italian options, cheapest good food..."
+    )
 
-    with gr.Row():
-        user_in = gr.Textbox(
-            placeholder="Ask: Find me a Thai restaurant, show me cheap Italian...",
-            label="Your Question"
-        )
-        clear = gr.Button("Clear Chat")
+    clear = gr.Button("Clear Chat")
 
-    user_in.submit(bot_reply, [user_in, chat], chat)
-    clear.click(lambda: [], None, chat)
+    user_input.submit(bot_reply, [user_input, chatbox], chatbox)
+    clear.click(lambda: [], None, chatbox)
 
 demo.launch()