Update app.py

app.py

@@ -1,28 +1,90 @@
+# app.py
 import gradio as gr
-from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
+from typing import List, Tuple, Dict, Any
 
-# ✅ Better small chatbot model (normal replies)
 MODEL_ID = "facebook/blenderbot-400M-distill"
+model = None
+tokenizer = None
 
-tokenizer = BlenderbotTokenizer.from_pretrained(MODEL_ID)
-model = BlenderbotForConditionalGeneration.from_pretrained(MODEL_ID)
-
-def chat(history, message):
-    # Convert chat history to context
-    context = ""
-    for user, bot in history:
-        context += f"User: {user}\nBot: {bot}\n"
-    context += f"User: {message}\nBot:"
+def ensure_model_loaded():
+    global model, tokenizer
+    if model is None or tokenizer is None:
+        from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
+        tokenizer = BlenderbotTokenizer.from_pretrained(MODEL_ID)
+        model = BlenderbotForConditionalGeneration.from_pretrained(MODEL_ID)
 
+def generate_reply(context: str) -> str:
+    ensure_model_loaded()
     inputs = tokenizer(context, return_tensors="pt")
     reply_ids = model.generate(**inputs, max_length=120, no_repeat_ngram_size=2)
     reply = tokenizer.decode(reply_ids[0], skip_special_tokens=True)
+    return reply
 
-    history.append((message, reply))
-    return history, history
+def history_to_context_from_tuples(history: List[Tuple[str, str]]) -> str:
+    ctx = ""
+    for u, b in history:
+        ctx += f"User: {u}\nBot: {b}\n"
+    return ctx
+
+def history_to_context_from_messages(history: List[Dict[str, str]]) -> str:
+    # messages are like {"role":"user"/"assistant","content":"..."}
+    ctx = ""
+    for msg in history:
+        role = msg.get("role", "")
+        content = msg.get("content", "")
+        if role and content:
+            if role.lower().startswith("user"):
+                ctx += f"User: {content}\n"
+            else:
+                ctx += f"Bot: {content}\n"
+    return ctx
+
+def chat(state: List[Any], message: str):
+    """
+    state is the Gradio chatbot state.
+    We support both:
+      - tuples: [("hi","hello"), ...]
+      - messages: [{"role":"user","content":"hi"}, {"role":"assistant","content":"hello"}, ...]
+    Gradio will pass state back as-is.
+    """
+    # detect format
+    context = ""
+    if state and isinstance(state[0], dict):
+        # messages format
+        context = history_to_context_from_messages(state)
+    elif state and isinstance(state[0], (list, tuple)):
+        # nested list/tuple format
+        # convert [(user,bot), ...] -> context
+        context = history_to_context_from_tuples(state)
+    else:
+        # empty or unknown -> fine
+        context = ""
+
+    context += f"User: {message}\nBot:"
+    reply = generate_reply(context)
+
+    # append to state in messages format (preferred)
+    # we'll append two entries: user then assistant
+    # If original state was tuples, convert reply to tuple for compatibility
+    if state and isinstance(state[0], dict):
+        state.append({"role":"user","content": message})
+        state.append({"role":"assistant","content": reply})
+        return state, state
+    else:
+        # use tuples format for backward compatibility
+        state.append((message, reply))
+        return state, state
 
 with gr.Blocks() as demo:
-    gr.Markdown("## 🤖 Anuj's Chatbot (Now Smarter!)")
+    gr.Markdown("## 🤖 Anuj's Chatbot — stable (messages format)")
+    # Use the new 'messages' type to avoid deprecation warning
+    chatbot = gr.Chatbot(type="messages")
+    state = gr.State([])
+
+    with gr.Row():
+        msg = gr.Textbox(show_label=False, placeholder="Type a message and press Enter...")
+
+    msg.submit(chat, [state, msg], [state, chatbot])
 
-    chatbot = gr.Chatbot()
-    state = gr
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)