Create main.py

main.py

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+import gradio as gr
+from transformers import pipeline
+
+# Initialize the Qwen model pipeline
+pipe = pipeline("text-generation", model="Qwen/Qwen1.5-0.5B")
+
+def chatbot(user_input, history=[]):
+    # Append user input to the conversation history
+    messages = history + [{"role": "user", "content": user_input}]
+    
+    # Generate response from the model
+    response = pipe(messages, max_new_tokens=150, do_sample=True, temperature=0.7)[0]['generated_text']
+    
+    # Extract the assistant's response (assuming the pipeline returns the full conversation)
+    assistant_response = response[-1]["content"] if isinstance(response, list) and len(response) > 0 else response
+    
+    # Update history with user input and assistant response
+    history = messages + [{"role": "assistant", "content": assistant_response}]
+    
+    # Format the conversation for display
+    chat_display = ""
+    for msg in history:
+        role = "You" if msg["role"] == "user" else "Assistant"
+        chat_display += f"**{role}**: {msg['content']}\n\n"
+    
+    return chat_display, history
+
+# Create Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# AI Chatbot with Qwen1.5-0.5B")
+    chatbot_output = gr.Markdown()
+    user_input = gr.Textbox(label="Your Message", placeholder="Type your message here...")
+    state = gr.State(value=[])  # To store conversation history
+    
+    user_input.submit(
+        fn=chatbot,
+        inputs=[user_input, state],
+        outputs=[chatbot_output, state]
+    )
+