app.py

import gradio as gr
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Wonder-Griffin/TraXLMistral")

pipe = pipeline("text-generation", model="Wonder-Griffin/TraXLMistral")

# Assuming `model_path` is the Hugging Face model hub path or a local directory
model_path = "Wonder-Griffin/TraXLMistral"  # Define this as needed

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    device_map="auto",
    torch_dtype='auto'
).eval()

def respond(
    message,
    history,
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    # Building the conversation history for the model
    messages = [{"role": "system", "content": system_message}]

    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

    messages.append({"role": "user", "content": message})
    
    # Tokenize the input message
    input_text = " ".join([msg["content"] for msg in messages if msg["role"] == "user"])
    input_ids = tokenizer.encode(input_text, return_tensors="pt")
    
    # Generate a response from the model
    output_ids = model.generate(
        input_ids,
        max_new_tokens=max_tokens,
        temperature=temperature,
        top_p=top_p,
        do_sample=True
    )
    
    # Decode the generated tokens into a response
    response = tokenizer.decode(output_ids[0], skip_special_tokens=True)

    return response

# Gradio interface setup
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)",
        ),
    ],
)

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