Create app.py

app.py

@@ -3,36 +3,28 @@ from transformers import AutoImageProcessor, AutoModelForObjectDetection
 from PIL import Image
 import torch
 
-# Cargar modelo
-ckpt = 'yainage90/fashion-object-detection'
-processor = AutoImageProcessor.from_pretrained(ckpt)
-model = AutoModelForObjectDetection.from_pretrained(ckpt)
+# Modelo que detecta prendas de vestir
+model_name = "yainage90/fashion-object-detection"
+processor = AutoImageProcessor.from_pretrained(model_name)
+model = AutoModelForObjectDetection.from_pretrained(model_name)
 
-# Precios promedios manuales
+# Precios fijos por prenda (estimados)
 PRECIOS = {
-    'top': 5000,
-    'bottom': 15000,
-    'shoes': 30000,
-    'bag': 20000,
-    'outer': 25000
+    'top': 7000,
+    'bottom': 18000,
+    'shoes': 25000,
+    'bag': 15000,
+    'outer': 22000
 }
 
-def procesar_imagen(img):
-    inputs = processor(images=[img], return_tensors="pt")
+def estimar_precio(imagen):
+    inputs = processor(images=imagen, return_tensors="pt")
     outputs = model(**inputs)
-    results = processor.post_process_object_detection(outputs, threshold=0.4, target_sizes=[[img.height, img.width]])[0]
+
+    target_size = [imagen.height, imagen.width]
+    results = processor.post_process_object_detection(outputs, threshold=0.5, target_sizes=[target_size])[0]
+
     total = 0
-    detalle = []
-    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
-        prenda = model.config.id2label[label.item()]
-        precio = PRECIOS.get(prenda, 0)
-        total += precio
-        detalle.append(f"{prenda}: ${precio}")
-    texto = "\n".join(detalle + [f"\nTotal aprox.: ${total}"])
-    return texto
+    resumen = []
 
-iface = gr.Interface(fn=procesar_imagen,
-                     inputs=gr.Image(type="pil"),
-                     outputs="textbox",
-                     title="Cuánto vale tu outfit?")
-iface.launch()
+    for score, label, box in zip(results["scores"], results["labels"], results["box]()