app.py

import os
import io
import torch
from flask import Flask, request, jsonify
from transformers import SegformerForImageClassification, SegformerImageProcessor
from PIL import Image

app = Flask(__name__)

# =============================================================================
# 1. CONFIGURACIÓN Y CARGA DEL MODELO
# =============================================================================
# Ruta donde subiste tu carpeta 'model' en Hugging Face
MODEL_PATH = "./model"
BASE_MODEL_NAME = "nvidia/mit-b0" # Respaldo para el procesador

print("🔄 Cargando sistema de IA...")

try:
    # Cargar Procesador (Intenta local, si falla usa el de NVIDIA)
    try:
        processor = SegformerImageProcessor.from_pretrained(MODEL_PATH)
    except:
        print("⚠️ Procesador local no encontrado, usando base nvidia/mit-b0")
        processor = SegformerImageProcessor.from_pretrained(BASE_MODEL_NAME)

    # Cargar Modelo Entrenado
    model = SegformerForImageClassification.from_pretrained(MODEL_PATH)
    model.eval() # Modo evaluación (ahorra memoria y es más rápido)
    
    print("✅ Modelo SegFormer cargado exitosamente.")
    print(f"   - Clases disponibles: {model.config.id2label}")

except Exception as e:
    print(f"❌ ERROR FATAL CARGANDO MODELO: {e}")

# =============================================================================
# 2. RUTAS DE LA API
# =============================================================================

@app.route('/', methods=['GET'])
def home():
    return "API NOM-083 (SegFormer) - Estado: ACTIVO 🟢"

@app.route('/predict', methods=['POST'])
def predict():
    # Validación básica
    if 'image' not in request.files:
        return jsonify({"error": "No se envió el campo 'image'"}), 400
    
    try:
        file = request.files['image']
        
        # --- CORRECCIÓN "A PRUEBA DE BALAS" ---
        # Leemos los bytes del archivo directamente a la memoria RAM.
        # Esto evita el error "cannot identify image file" con archivos temporales.
        img_bytes = file.read()
        image = Image.open(io.BytesIO(img_bytes)).convert("RGB")
        
        # Preprocesamiento
        inputs = processor(images=image, return_tensors="pt")
        
        # Inferencia
        with torch.no_grad():
            outputs = model(**inputs)
            logits = outputs.logits
            
        # Resultados (Softmax para porcentaje)
        probs = torch.nn.functional.softmax(logits, dim=-1)
        top_score, top_idx = probs.max(1)
        
        clase_nombre = model.config.id2label[top_idx.item()]
        confianza = top_score.item() * 100
        
        # Respuesta JSON limpia
        return jsonify({
            "status": "success",
            "clase": clase_nombre,
            "confianza": round(confianza, 2),
            "mensaje": f"Análisis: {clase_nombre}"
        })

    except Exception as e:
        print(f"❌ Error interno: {e}")
        return jsonify({"error": str(e), "status": "error"}), 500

# =============================================================================
# 3. INICIO DEL SERVIDOR
# =============================================================================
if __name__ == '__main__':
    # Puerto 7860 es el estándar de Hugging Face Spaces
    app.run(host='0.0.0.0', port=7860)