app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Initialize the pipeline with the specific model
pipe = pipeline("text-generation", model="JamesBentley/Llama-2-7b-chat-hf-fine-tuned")

def respond(message, history, system_message, max_tokens, temperature, top_p):
    # Build the conversation history for the model
    messages = [{"role": "system", "content": system_message}]
    messages.extend([{"role": "user" if role == 'user' else "assistant", "content": content} for role, content in history])
    messages.append({"role": "user", "content": message})
    
    # Generate the response using the model
    response = pipe(messages, max_length=max_tokens, temperature=temperature, top_p=top_p, num_return_sequences=1)
    
    # Extract text from response (assumes single response generation)
    return response[0]['generated_text']

# Setup Gradio interface
demo = gr.ChatInterface(
    fn=respond,
    inputs=[
        gr.Textbox(label="Your message"),
        gr.Dataframe(headers=["Role", "Content"], label="Conversation History"),
        gr.Textbox(default="You are a friendly Chatbot.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, default=512, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=1.0, default=0.7, label="Temperature"),
        gr.Slider(minimum=0.1, maximum=1.0, default=0.95, label="Top-p (nucleus sampling)")
    ],
    outputs=[gr.Textbox(label="Response")]
)

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