app.py

import os
import subprocess

# Ensure SentencePiece and Accelerate are installed
try:
    import sentencepiece
except ImportError:
    subprocess.check_call([os.sys.executable, "-m", "pip", "install", "sentencepiece"])

try:
    import accelerate
except ImportError:
    subprocess.check_call([os.sys.executable, "-m", "pip", "install", "accelerate"])

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

# Check if a GPU is available, otherwise use CPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load a smaller, faster model and tokenizer
model_name = "distilgpt2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name).to(device)

# Function to evaluate the prompt using the loaded model
def evaluar_prompt(prompt):
    try:
        # Generate analysis using the model
        inputs = tokenizer(prompt, return_tensors="pt").to(device)
        outputs = model.generate(inputs["input_ids"], max_length=150)  # Limit max_length for faster results
        analysis = tokenizer.decode(outputs[0], skip_special_tokens=True)
        
        # Basic logic to guide the analysis based on critical thinking principles
        feedback = "Análisis del Prompt:\n"
        
        # Check clarity
        if len(prompt.split()) < 5:
            feedback += "- Claridad: El prompt es muy breve y puede