app.py

import spaces
import os
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import gradio as gr
import wandb

# Retrieve Hugging Face and W&B API keys from environment variables
hf_api_key = os.getenv("HF_TOKEN")
wandb_api_key = os.getenv("WANDB_API_KEY")

# Validate API keys
if not hf_api_key:
    raise ValueError("Hugging Face API key not found. Please ensure it's set as a secret in the Space.")
if not wandb_api_key:
    raise ValueError("Weights & Biases API key not found. Please ensure it's set as a secret in the Space.")

# Configure W&B (if using for tracking)
wandb.login(key=wandb_api_key)

# Define function to load model and pipeline dynamically
def load_pipeline(model_name, fine_tuned=False):
    # Set model paths for pre-trained and fine-tuned versions
    paths = {
        "gpt2": ("gpt2-medium", "path/to/finetuned_gpt2"),
        "gpt_neo": ("EleutherAI/gpt-neo-1.3B", "path/to/finetuned_gpt_neo"),
        "gpt_j": ("EleutherAI/gpt-j-6B", "path/to/finetuned_gpt_j")
    }

    pretrained_model_name, finetuned_model_path = paths[model_name]
    model_path = finetuned_model_path if fine_tuned else pretrained_model_name

    # Load model and tokenizer
    model = AutoModelForCausalLM.from_pretrained(model_path)
    tokenizer = AutoTokenizer.from_pretrained(model_path)
    tokenizer.pad_token = tokenizer.eos_token

    # Set up pipeline with GPU
    return pipeline("text-generation", model=model, tokenizer=tokenizer, device=0)

# Add the GPU decorator to the generate function
@spaces.GPU(duration=120)  # Specify duration as needed
def compare_single_model(prompt, model_choice, temperature, top_p, max_length):
    pretrained_pipeline = load_pipeline(model_choice, fine_tuned=False)
    finetuned_pipeline = load_pipeline(model_choice, fine_tuned=True)

    # Generate responses
    pretrained_response = pretrained_pipeline(prompt, temperature=temperature, top_p=top_p, max_length=int(max_length))[0]["generated_text"]
    finetuned_response = finetuned_pipeline(prompt, temperature=temperature, top_p=top_p, max_length=int(max_length))[0]["generated_text"]

    # Free up memory after use
    del pretrained_pipeline, finetuned_pipeline
    torch.cuda.empty_cache()

    return pretrained_response, finetuned_response

# Gradio interface
interface = gr.Interface(
    fn=compare_single_model,
    inputs=[
        "text",
        gr.Dropdown(choices=["gpt2", "gpt_neo", "gpt_j"], label="Select Model"),
        gr.Slider(0.1, 1.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(0.1, 1.0, value=0.9, step=0.1, label="Top-p"),
        gr.Slider(10, 100, value=50, step=10, label="Max Length")
    ],
    outputs=[
        gr.Textbox(label="Pre-trained Response"),
        gr.Textbox(label="Fine-tuned Response")
    ],
    title="Single Model Comparison: Pre-trained vs Fine-tuned",
    description="Enter a prompt and select a model to compare responses from pre-trained and fine-tuned versions. Adjust parameters with the sliders."
)

# Launch the interface
interface.launch()