app.py

import gradio as gr
from transformers import pipeline, set_seed
import torch

# Function to generate responses using the entire conversation history
def generate_response(messages, model_name, sampling_temperature, max_tokens, top_p):
    generator = pipeline('text-generation', model=model_name, torch_dtype=torch.float16)
    set_seed(42)  # You can set a different seed for reproducibility
    
    # Combine entire conversation history
    conversation = ""
    for message in messages:
        role = message['role']
        content = message['content']
        conversation += f"<|im_start|>{role}\n{content}<|im_end|>\n"
    
    # Generate response
    response = generator(conversation, max_length=2048, temperature=sampling_temperature, max_tokens=max_tokens, top_p=top_p, repetition_penalty=1.1, top_k=12)
    
    return [{'content': response[0]['generated_text'], 'role': 'assistant'}]

# Gradio chatbot interface with conversation history
iface = gr.Interface(
    fn=generate_response,
    inputs=[
        gr.Chat("You", "Chatbot"),
        gr.Dropdown("Select Model", ["Locutusque/TinyMistral-248M-v2.5-Instruct", "Locutusque/Hercules-1.0-Mistral-7B", "Locutusque/UltraQwen-1_8B"]),
        gr.Slider("Sampling Temperature", 0.1, 2.0, 1.0, 0.1),
        gr.Slider("Max Tokens", 5, 200, 50, 5),
        gr.Slider("Top P", 0.1, 0.5, 0.75, 0.1)
    ],
    outputs=gr.Chat(role="Chatbot"),
    live=True,
    capture_session=True
)

# Launch Gradio chatbot interface
iface.launch()