app.py

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

# Function to load model and tokenizer based on selection
def load_model(model_name):
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(model_name)
    return tokenizer, model

# Define the function to generate a response with adjustable parameters and model-specific adjustments
def generate_response(prompt, model_name, persona="I am a helpful assistant.", temperature=0.7, top_p=0.9, repetition_penalty=1.2, max_length=70):
    # Load the chosen model and tokenizer
    tokenizer, model = load_model(model_name)
    
    # Adjust the prompt format for DialoGPT
    if model_name == "microsoft/DialoGPT-small":
        full_prompt = f"User: {prompt}\nBot:"  # Structure as a conversation
    else:
        full_prompt = f"{persona}: {prompt}"  # Standard format for other models

    # Tokenize and generate response
    inputs = tokenizer(full_prompt, return_tensors="pt")
    outputs = model.generate(
        inputs.input_ids,
        max_length=max_length,
        do_sample=True,
        temperature=temperature,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        pad_token_id=tokenizer.eos_token_id
    )
    response = tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)
    
    # Trim the prompt if it appears in the response
    if model_name == "microsoft/DialoGPT-small":
        response_without_prompt = response.split("Bot:", 1)[-1].strip()
    else:
        response_without_prompt = response.split(":", 1)[-1].strip()

    return response_without_prompt if response_without_prompt else "I'm not sure how to respond to that."

# Define Gradio interface function with model selection
def chat_interface(user_input, model_choice, persona="I am a helpful assistant", temperature=0.7, top_p=0.9, repetition_penalty=1.2, max_length=50):
    return generate_response(user_input, model_choice, persona, temperature, top_p, repetition_penalty, max_length)

# Set up Gradio interface with model selection and parameter sliders
interface = gr.Interface(
    fn=chat_interface,
    inputs=[
        gr.Textbox(label="User Input"),
        gr.Dropdown(choices=["distilgpt2", "gpt2", "microsoft/DialoGPT-small"], label="Model Choice", value="distilgpt2"),
        gr.Textbox(label="Persona", value="You are a helpful assistant."),
        gr.Slider(label="Temperature", minimum=0.1, maximum=1.0, value=0.7, step=0.1),
        gr.Slider(label="Top-p (Nucleus Sampling)", minimum=0.1, maximum=1.0, value=0.9, step=0.1),
        gr.Slider(label="Repetition Penalty", minimum=1.0, maximum=2.0, value=1.2, step=0.1),
        gr.Slider(label="Max Length", minimum=10, maximum=100, value=50, step=5)
    ],
    outputs="text",
    title="Interactive Chatbot with Model Comparison",
    description="Chat with the bot! Select a model and adjust parameters to see how they affect the response."
)

# Launch the Gradio app
if __name__ == "__main__":
    interface.launch()