app.py

import os
import subprocess

def install(package):
    subprocess.check_call([os.sys.executable, "-m", "pip", "install", package])

install("transformers")

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

# Global cache for loaded models
model_cache = {}

# Load a model with progress bar
def load_model(model_name, progress=gr.Progress(track_tqdm=False)):
    if model_name not in model_cache:
        token = os.getenv("HF_TOKEN")
        progress(0, desc="Loading tokenizer...")
        tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=token)
        progress(0.5, desc="Loading model...")
        model = AutoModelForCausalLM.from_pretrained(
            model_name,
            torch_dtype=torch.float16,
            device_map="auto",
            use_auth_token=token
        )
        model_cache[model_name] = (tokenizer, model)
        progress(1, desc="Model ready.")
        return f"{model_name} loaded and ready!"
    else:
        return f"{model_name} already loaded."

# Inference function using GPU
@spaces.GPU
def generate_text(model_name, prompt):
    tokenizer, model = model_cache[model_name]
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    outputs = model.generate(**inputs, max_new_tokens=256)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Available models
model_choices = [
    "deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
    "meta-llama/Llama-3.2-3B-Instruct",
    "google/gemma-7b"
]

# Gradio Interface
with gr.Blocks() as demo:
    gr.Markdown("## Clinical Text Analysis with LLMs (LLaMA, DeepSeek, Gemma)")
    
    with gr.Row():
        model_selector = gr.Dropdown(choices=model_choices, label="Select Model")
        model_status = gr.Textbox(label="Model Status", interactive=False)

    input_text = gr.Textbox(label="Input Clinical Text")
    output_text = gr.Textbox(label="Generated Output")
    
    analyze_button = gr.Button("Analyze")

    # Load model when changed
    model_selector.change(fn=load_model, inputs=model_selector, outputs=model_status)
    
    # Generate output
    analyze_button.click(fn=generate_text, inputs=[model_selector, input_text], outputs=output_text)

demo.launch()