Deployment of Bananas Quality App v2 in Hugging Face Space

app.py

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+import torch
+import torch.nn as nn
+import torchvision.transforms as transforms
+import cv2
+import numpy as np
+from PIL import Image
+import gradio as gr
+
+# Preprocesamiento de imágenes
+transform = transforms.Compose([
+    transforms.Resize((512, 512)),
+    transforms.ToTensor(),
+    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
+])
+
+class CNN(nn.Module):
+    def __init__(self):
+        super(CNN, self).__init__()
+        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1)
+        self.relu = nn.ReLU()
+        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.fc1 = nn.Linear(32 * 256 * 256, 128)
+        self.fc2 = nn.Linear(128, 4)  # 4 clases: baja, regular, excelente, mala
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+        x = x.view(x.size(0), -1)
+        x = self.fc1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        return x
+
+# Configurar dispositivo en CPU
+device = torch.device('cpu')
+
+# Cargar el modelo previamente guardado
+model = CNN().to(device)
+model.load_state_dict(torch.load('calidadplatano.pth', map_location=device))
+model.eval()
+
+# Función para clasificar la imagen de entrada
+def classify_image(input_image):
+    input_image = cv2.cvtColor(input_image, cv2.COLOR_BGR2RGB)
+    input_image = Image.fromarray(input_image)
+    input_image = transform(input_image).unsqueeze(0).to(device)
+    output = model(input_image)
+    probabilities = torch.softmax(output, dim=1).squeeze().detach().cpu().numpy()
+    class_labels = ['baja', 'regular', 'excelente', 'mala']
+    predicted_class = class_labels[np.argmax(probabilities)]
+    confidence = probabilities[np.argmax(probabilities)]
+    return predicted_class, confidence
+
+# Definir la interfaz gráfica de usuario
+inputs = gr.inputs.Image()
+outputs = gr.outputs.Label(num_top_classes=1)
+examples=[["imagenesDeEjemplos/1.webp"],["imagenesDeEjemplos/2.webp"],["imagenesDeEjemplos/3.webp"],["imagenesDeEjemplos/4.webp"],["imagenesDeEjemplos/5.webp"]]
+
+def process_image(input_image):
+    predicted_class, confidence = classify_image(input_image)
+    return predicted_class + " (" + str(round(confidence * 100, 2)) + "%)"
+
+title = "Clasificación de calidad de plátanos"
+description = "Carga una imagen de plátano y obtén la clasificación de calidad."
+iface = gr.Interface(fn=process_image, inputs=inputs, outputs=outputs, title=title, description=description,examples=examples)
+iface.launch()

calidadplatano.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:554cdad36dab1919556a7a87fc766101846ce18cc43184bc9b84f166246b61f2
+size 1073750287

requirements.txt

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+torch
+torchvision
+numpy
+Pillow
+gradio
+opencv-python