añadido codigo para poder usar el modelo

drag_and_drop.py

@@ -0,0 +1,131 @@
+import torch
+import torch.nn as nn
+from PIL import Image, ImageTk
+import torchvision.transforms as transforms
+from pathlib import Path
+
+from tkinterdnd2 import TkinterDnD
+import tkinter as tk
+from tkinter import Label, Button, filedialog
+
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(3, 64, 3, padding=1)
+        self.conv2 = nn.Conv2d(64, 128, 3, padding=1)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.fc1 = nn.Linear(128 * 8 * 8, 512)
+        self.fc2 = nn.Linear(512, 10)
+        self.relu = nn.ReLU()
+        self.dropout = nn.Dropout(0.5)
+
+    def forward(self, x):
+        x = self.pool(self.relu(self.conv1(x)))
+        x = self.pool(self.relu(self.conv2(x)))
+        x = x.view(-1, 128 * 8 * 8)
+        x = self.relu(self.fc1(x))
+        x = self.dropout(x)
+        x = self.fc2(x)
+        return x
+
+model = Net()
+try:
+    model.load_state_dict(torch.load('modelo_cifar10.pth', map_location='cpu'))
+    model.eval()
+    print("✅ Modelo cargado.")
+except FileNotFoundError:
+    print("❌ No se encontró 'modelo_cifar10.pth'. Entrena primero.")
+    exit()
+
+classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
+
+transform = transforms.Compose([
+    transforms.Resize((32, 32)),
+    transforms.ToTensor(),
+    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
+])
+
+class ImageClassifierApp:
+    def __init__(self, root):
+        self.root = root
+        self.root.title("🎯 Clasificador de Imágenes - Arrastra una imagen")
+        self.root.geometry("500x600")
+        self.root.configure(bg="#f0f0f0")
+
+        self.label = Label(
+            root,
+            text="👇 Arrastra una imagen aquí",
+            bg="#f0f0f0",
+            fg="#333",
+            font=("Arial", 14),
+        )
+        self.label.pack(pady=20)
+
+        self.button = Button(
+            root,
+            text="🔍 Seleccionar imagen",
+            command=self.open_file,
+            bg="#4CAF50",
+            fg="white",
+            font=("Arial", 12),
+            padx=20
+        )
+        self.button.pack(pady=10)
+
+        self.image_label = Label(root, bg="#ddd", text="No hay imagen", width=30, height=15)
+        self.image_label.pack(pady=10)
+
+        self.result_label = Label(
+            root,
+            text="Predicción: ---",
+            bg="#f0f0f0",
+            font=("Arial", 16, "bold"),
+            fg="#005bb5"
+        )
+        self.result_label.pack(pady=20)
+
+        self.root.drop_target_register("DND_Files")
+        self.root.dnd_bind("<<Drop>>", self.on_drop)
+
+    def open_file(self):
+        file_path = filedialog.askopenfilename(
+            filetypes=[("Imágenes", "*.jpg *.jpeg *.png *.bmp *.webp")]
+        )
+        if file_path:
+            self.process_image(file_path)
+
+    def on_drop(self, event):
+        try:
+            file_paths = self.root.tk.splitlist(event.data)
+            file_path = str(file_paths[0]).strip("{}")  # Limpia llaves si vienen
+            if Path(file_path).exists():
+                self.process_image(file_path)
+            else:
+                self.result_label.config(text="❌ Archivo no válido")
+        except Exception as e:
+            self.result_label.config(text=f"❌ Error: {e}")
+
+    def process_image(self, file_path):
+        try:
+            image = Image.open(file_path).convert("RGB")
+
+            img_display = image.resize((250, 250), Image.Resampling.LANCZOS)
+            photo = ImageTk.PhotoImage(img_display)
+            self.image_label.configure(image=photo, text="")
+            self.image_label.image = photo
+
+            input_tensor = transform(image).unsqueeze(0)
+            with torch.no_grad():
+                output = model(input_tensor)
+                _, predicted = torch.max(output, 1)
+                class_name = classes[predicted.item()]
+
+            self.result_label.config(text=f"🎯 {class_name}")
+
+        except Exception as e:
+            self.result_label.config(text=f"❌ Error: {str(e)}")
+
+if __name__ == "__main__":
+    root = TkinterDnD.Tk()
+    app = ImageClassifierApp(root)
+    root.mainloop()