| import gradio as gr |
| import os |
|
|
| os.system('pip install transformers torch') |
|
|
| from transformers import GPT2LMHeadModel, GPT2Tokenizer |
|
|
| |
| model_name = "microsoft/DialoGPT-small" |
| model = GPT2LMHeadModel.from_pretrained(model_name) |
| tokenizer = GPT2Tokenizer.from_pretrained(model_name) |
|
|
| |
| system_prompt = "You are a helpful assistant." |
| chat_history = system_prompt |
|
|
| def generate_response(prompt, max_length=50, temperature=0.8): |
| global chat_history |
| input_text = chat_history + " User: " + prompt |
| input_ids = tokenizer.encode(input_text, return_tensors="pt") |
| output_ids = model.generate(input_ids, max_length=max_length, temperature=temperature, num_return_sequences=1) |
| response = tokenizer.decode(output_ids[0], skip_special_tokens=True) |
| |
| |
| chat_history += f" User: {prompt} Assistant: {response}" |
| |
| return response |
|
|
| iface = gr.Interface( |
| fn=generate_response, |
| inputs=gr.Textbox(), |
| outputs="text", |
| ) |
|
|
| iface.launch() |
