app.py

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)

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)
iface.launch()