app.py

from huggingface_hub import snapshot_download
from transformers import AutoModelForCausalLM, AutoTokenizer
from flask import Flask, request, jsonify
import torch

app = Flask(__name__)

# Carregue o modelo e o tokenizador
model_id = "gpt2"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = AutoModelForCausalLM.from_pretrained(model_id).to(device)
tokenizer = AutoTokenizer.from_pretrained(model_id)

@app.route("/generate", methods=["POST"])
def generate():
    data = request.get_json()
    prompt = data.get("prompt", "")

    # Tokenize a entrada
    inputs = tokenizer.encode(prompt, return_tensors="pt").to(device)

    # Gere a resposta usando o modelo
    outputs = model.generate(inputs, max_length=100, temperature=0.7)

    # Decodifique a resposta
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)

    return jsonify(response)

if __name__ == "__main__":
    app.run(debug=True)