Create app.py

app.py

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+import gradio as gr
+from fastai.vision.all import *
+import numpy as np
+from PIL import Image
+from huggingface_hub import hf_hub_download
+import torchvision.transforms as transforms
+import torch
+
+# Cargar modelo desde Hugging Face Hub
+model_path = hf_hub_download(repo_id="PablitoGil14/AP-Practica3", filename="model.pkl")
+learn = load_learner(model_path)
+
+def segmentar(img: Image.Image):
+    img = img.resize((640, 480))
+    x = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize(*imagenet_stats)
+    ])(img).unsqueeze(0)
+    
+    with torch.no_grad():
+        preds = learn.model.eval()(x)
+        mask = torch.argmax(preds, dim=1).squeeze().cpu().numpy()
+        
+    # Asignar colores según los valores de clase
+    out_mask = np.zeros_like(mask, dtype=np.uint8)
+    out_mask[mask == 1] = 255
+    out_mask[mask == 2] = 150
+    out_mask[mask == 3] = 29
+    out_mask[mask == 4] = 74
+    return Image.fromarray(out_mask)
+
+# Interfaz de Gradio
+demo = gr.Interface(
+    fn=segmentar,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Image(type="pil"),
+    title="Segmentador de Viñedos",
+    description="Sube una imagen y el modelo segmentará racimos de uva, hojas, madera y postes."
+)
+
+demo.launch()