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| import gradio as gr | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import torch | |
| MODEL_NAME = "microsoft/DialoGPT-medium" | |
| print("Loading model...") | |
| tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) | |
| model = AutoModelForCausalLM.from_pretrained(MODEL_NAME) | |
| model.eval() | |
| print("Model ready!") | |
| def respond(message, history): | |
| print(f"=== respond called with message: {message!r} ===") | |
| try: | |
| text = str(message) + tokenizer.eos_token | |
| input_ids = tokenizer.encode(text, return_tensors="pt") | |
| with torch.no_grad(): | |
| output_ids = model.generate( | |
| input_ids, | |
| max_new_tokens=50, | |
| pad_token_id=tokenizer.eos_token_id, | |
| do_sample=True, | |
| top_p=0.9, | |
| temperature=0.8, | |
| ) | |
| reply = tokenizer.decode( | |
| output_ids[:, input_ids.shape[-1]:][0], | |
| skip_special_tokens=True, | |
| ).strip() | |
| print(f"Reply: {reply!r}") | |
| return reply or "Tell me more!" | |
| except Exception as e: | |
| import traceback | |
| err_str = traceback.format_exc() | |
| print(f"ERROR: {err_str}") | |
| return f"Error: {type(e).__name__}: {str(e)}" | |
| demo = gr.ChatInterface( | |
| fn=respond, | |
| title="🤖 MiniBot — Mini Chatbot", | |
| description="Powered by microsoft/DialoGPT-medium", | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() |