app.py

import os
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load your model and tokenizer from Hugging Face
model_name = 'redael/model_udc'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# Function to generate response
def generate_response(message, history, system_message, max_tokens, temperature, top_p):
    # Prepare the conversation history
    messages = [{"role": "system", "content": system_message}]
    
    for user_msg, bot_msg in history:
        if user_msg:
            messages.append({"role": "user", "content": user_msg})
        if bot_msg:
            messages.append({"role": "assistant", "content": bot_msg})
    
    messages.append({"role": "user", "content": message})
    
    # Tokenize and prepare the input
    prompt = "\n".join([f"{msg['role'].capitalize()}: {msg['content']}" for msg in messages])
    inputs = tokenizer(prompt, return_tensors='pt', padding=True, truncation=True, max_length=512).to(device)
    
    # Generate the response
    outputs = model.generate(
        inputs['input_ids'],
        max_length=max_tokens,
        num_return_sequences=1,
        pad_token_id=tokenizer.eos_token_id,
        temperature=temperature,
        top_p=top_p,
        early_stopping=True,
        do_sample=True  # Enable sampling
    )
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    
    # Clean up the response
    response = response.split("Assistant:")[-1].strip()
    response_lines = response.split('\n')
    clean_response = []
    for line in response_lines:
        if "User:" not in line and "Assistant:" not in line:
            clean_response.append(line)
    response = ' '.join(clean_response)
    
    return [(message, response)]

# Create the Gradio chat interface
demo = gr.ChatInterface(
    fn=generate_response,
    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="Chatbot",
    description="Ask anything to the chatbot."
)

if __name__ == "__main__":
    demo.launch()