Create app.py

app.py

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+from transformers import pipeline
+import gradio as gr
+
+# Load your model with pipeline
+pipe = pipeline("text-generation", model="prithivMLmods/rStar-Coder-Qwen3-0.6B")
+
+# Keep a conversation history
+history = []
+
+def chat_fn(user_input):
+    global history
+    # Add user message
+    history.append(f"User: {user_input}")
+    
+    # Combine all previous messages as context
+    context = "\n".join(history) + "\nBot:"
+    
+    # Generate response
+    output = pipe(context, max_new_tokens=200, do_sample=True, top_p=0.9)[0]['generated_text']
+    
+    # Extract only the bot's reply (after last "Bot:")
+    bot_reply = output.split("Bot:")[-1].strip()
+    
+    # Add bot reply to history
+    history.append(f"Bot: {bot_reply}")
+    
+    return bot_reply
+
+# Gradio interface
+with gr.Blocks() as demo:
+    chatbot_ui = gr.Chatbot()
+    msg = gr.Textbox(placeholder="Type a message...")
+    
+    def respond(user_input, chat_history):
+        reply = chat_fn(user_input)
+        chat_history.append((user_input, reply))
+        return chat_history, chat_history
+    
+    state = gr.State([])  # store chat history in Gradio
+    msg.submit(respond, [msg, state], [chatbot_ui, state])
+
+demo.launch()