Delete app.py

app.py

@@ -1,107 +0,0 @@
-import gradio as gr
-import torch
-import numpy as np
-from PIL import Image
-from torch import nn
-from transformers import SegformerForSemanticSegmentation, SegformerImageProcessor
-import os
-
-# --- 1. CONFIGURACIÓN ---
-MODEL_PATH = "modelo_mejorado.pth"
-
-LABELS = [
-    "fondo", "wheat leaf rust", "wheat powdery mildew",
-    "wheat septoria blotch", "wheat stem rust", "wheat stripe rust"
-]
-
-# Paleta (R, G, B)
-PALETA_COLORES = [
-    [0, 0, 0], [220, 38, 38], [22, 163, 74], 
-    [37, 99, 235], [234, 179, 8], [219, 39, 119]
-]
-
-# En Docker CPU (Free Tier) forzamos CPU para evitar errores de memoria
-device = torch.device("cpu")
-print(f"Usando dispositivo: {device}")
-
-# --- 2. CARGAR MODELO ---
-checkpoint_name = "nvidia/segformer-b4-finetuned-ade-512-512"
-
-try:
-    model_inference = SegformerForSemanticSegmentation.from_pretrained(
-        checkpoint_name,
-        num_labels=len(LABELS),
-        id2label={i: label for i, label in enumerate(LABELS)},
-        label2id={label: i for i, label in enumerate(LABELS)},
-        ignore_mismatched_sizes=True
-    )
-    # Cargar pesos
-    if os.path.exists(MODEL_PATH):
-        state_dict = torch.load(MODEL_PATH, map_location=device)
-        model_inference.load_state_dict(state_dict)
-        print("✅ Modelo cargado correctamente.")
-    else:
-        print("⚠️ NO se encontró el archivo .pth")
-
-    model_inference.to(device)
-    model_inference.eval()
-
-    image_processor = SegformerImageProcessor.from_pretrained(checkpoint_name)
-    image_processor.do_resize = False 
-    image_processor.do_rescale = True 
-
-except Exception as e:
-    print(f"Error fatal cargando modelo: {e}")
-
-# --- 3. PREDICCIÓN ---
-def predecir_enfermedad(image):
-    if image is None: return None, "⚠️ Sube una imagen."
-    
-    original_size = image.size 
-    img_resized = image.resize((512, 512))
-    
-    inputs = image_processor(images=img_resized, return_tensors="pt")
-    inputs = {k: v.to(device) for k, v in inputs.items()}
-    
-    with torch.no_grad():
-        outputs = model_inference(**inputs)
-        logits = outputs.logits
-        logits_upsampled = nn.functional.interpolate(logits, size=(512, 512), mode="bilinear", align_corners=False)
-        pred_mask = logits_upsampled.argmax(dim=1).squeeze().cpu().numpy()
-
-    color_mask = np.zeros((512, 512, 3), dtype=np.uint8)
-    classes_found = []
-    unique_classes = np.unique(pred_mask)
-    
-    for class_id in unique_classes:
-        if class_id == 0: continue
-        classes_found.append(LABELS[class_id])
-        color_mask[pred_mask == class_id] = PALETA_COLORES[class_id]
-
-    mask_pil = Image.fromarray(color_mask).resize(original_size, resample=Image.NEAREST)
-    final_image = Image.blend(image.convert("RGB"), mask_pil.convert("RGB"), alpha=0.45)
-    
-    if len(classes_found) > 0:
-        diagnosis = "Enfermedades:\n" + "\n".join(f"- {c}" for c in classes_found)
-    else:
-        diagnosis = "Planta Sana (Solo fondo)."
-
-    return final_image, diagnosis
-
-# --- 4. INTERFAZ ---
-# Usamos Blocks simples compatibles con versiones estables
-with gr.Blocks(title="Wheat AI") as demo:
-    gr.Markdown("# 🌾 Detector de Enfermedades en Trigo")
-    
-    with gr.Row():
-        img_in = gr.Image(type="pil", label="Imagen")
-        img_out = gr.Image(type="pil", label="Resultado")
-    
-    txt_out = gr.Textbox(label="Diagnóstico")
-    btn = gr.Button("Analizar", variant="primary")
-    
-    btn.click(predecir_enfermedad, img_in, [img_out, txt_out])
-
-# Configuración crítica para Docker en Hugging Face
-if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)