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Browse files- files/cifar_net.pth +0 -3
- files/inference.py +0 -96
- files/static/uploads/avion_img.jpg +0 -0
- files/static/uploads/cat_image.jpg +0 -0
- files/templates/index.html +0 -25
files/cifar_net.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:d68a16753138b325ae729b384dc00046eebd1eefd2c871b8efe9e10bd5a0f7a0
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size 251604
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files/inference.py
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torchvision.transforms as transforms
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from PIL import Image
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from flask import Flask, jsonify, request, render_template
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import os
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app = Flask(__name__)
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# Directorio de carga de im谩genes
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UPLOAD_FOLDER = 'static/uploads'
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app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
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# Aplicar la transformaci贸n
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transform = transforms.Compose([
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transforms.Resize((32, 32)), # Ajustar al tama帽o de entrada de la red
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transforms.ToTensor(),
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transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
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])
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# Mostrar la imagen
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# imshow(transform(image))
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class Net(nn.Module):
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def __init__(self):
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super().__init__()
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self.conv1 = nn.Conv2d(3, 6, 5)
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self.pool = nn.MaxPool2d(2, 2)
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self.conv2 = nn.Conv2d(6, 16, 5)
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self.fc1 = nn.Linear(16 * 5 * 5, 120)
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self.fc2 = nn.Linear(120, 84)
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self.fc3 = nn.Linear(84, 10)
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def forward(self, x):
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x = self.pool(F.relu(self.conv1(x)))
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x = self.pool(F.relu(self.conv2(x)))
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x = torch.flatten(x, 1) # flatten all dimensions except batch
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x = F.relu(self.fc1(x))
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x = F.relu(self.fc2(x))
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x = self.fc3(x)
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return x
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classes = ('plane', 'car', 'bird', 'cat',
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'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
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net = Net()
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PATH='cifar_net.pth'
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net.load_state_dict(torch.load(PATH))
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net.eval() # Establecer la red en modo de evaluaci贸n
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# Endpoint para hacer predicciones
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@app.route('/predict', methods=['GET', 'POST'])
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def predict():
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prediction = None
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image_path = None
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if request.method == 'POST':
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try:
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# Obtener la imagen desde la solicitud POST
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file = request.files['file']
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# Guardar la imagen cargada en el directorio de carga
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image_path = os.path.join(app.config['UPLOAD_FOLDER'], file.filename)
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file.save(image_path)
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# Aplicar la transformaci贸n a la imagen
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image = Image.open(file)
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image_tensor = transform(image).unsqueeze(0)
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# Obtener la salida del modelo
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output = net(image_tensor)
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# Aplicar softmax para obtener las probabilidades
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probabilities = F.softmax(output, dim=1)
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# Obtener la clase predicha y la probabilidad m谩xima
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max_prob, predicted_class = torch.max(probabilities, 1)
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predicted_class_name = classes[predicted_class.item()]
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# Almacenar el resultado de la predicci贸n
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prediction = {
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'predicted_class': predicted_class_name,
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'probability': round(max_prob.item() * 100, 2)
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}
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except Exception as e:
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return jsonify({'error': str(e)})
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return render_template('index.html', prediction=prediction, image_path=image_path)
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if __name__ == '__main__':
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app.run(debug=True, host="0.0.0.0", port="7860")
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files/static/uploads/avion_img.jpg
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files/static/uploads/cat_image.jpg
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files/templates/index.html
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta http-equiv="X-UA-Compatible" content="IE=edge">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Predicci贸n de Im谩genes</title>
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</head>
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<body>
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<h1>Sube una imagen para predecir</h1>
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<form action="/predict" method="post" enctype="multipart/form-data">
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<input type="file" name="file" accept="image/*" required>
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<br>
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<input type="submit" value="Predecir">
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</form>
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<br>
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{% if prediction %}
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<h2>Resultado de la Predicci贸n:</h2>
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<p>Clase: {{ prediction['predicted_class'] }}</p>
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<p>Probabilidad: {{ prediction['probability'] }}%</p>
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<img src="{{ image_path }}" alt="Imagen Cargada">
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<!-- <p>{{ image_path }}</p> -->
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{% endif %}
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</body>
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</html>
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