import gradio as gr from transformers import AutoModelForCausalLM, AutoTokenizer # Load the model and tokenizer model_name = "karthikqnq/trainedmodel" model = AutoModelForCausalLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) def respond( message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p, ): # Construct the prompt from history and current message prompt = system_message + "\n\n" for user_msg, assistant_msg in history: if user_msg: prompt += f"User: {user_msg}\nAssistant: {assistant_msg}\n" prompt += f"User: {message}\nAssistant: " # Tokenize the input prompt inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=1024) # Generate response outputs = model.generate( **inputs, max_length=max_tokens, temperature=temperature, top_p=top_p, do_sample=True, num_return_sequences=1 ) # Decode the output and extract only the assistant's response response = tokenizer.decode(outputs[0], skip_special_tokens=True) # Extract the assistant's reply after "Assistant:" try: assistant_response = response.split("Assistant: ")[-1].strip() except: assistant_response = response return assistant_response # Create the Gradio interface demo = gr.ChatInterface( respond, additional_inputs=[ gr.Textbox( value="You are a friendly Chatbot.", label="System message" ), gr.Slider( minimum=1, maximum=2048, value=512, step=1, label="Max new tokens" ), gr.Slider( minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature" ), gr.Slider( minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)" ), ], title="QnQ GPT-2 Chatbot", description="A chatbot powered by the QnQ GPT-2 model" ) if __name__ == "__main__": demo.launch()