Upload 5 files

main.py

@@ -0,0 +1,109 @@
+import os
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras import layers, models
+from flask import Flask, request, jsonify, render_template
+from PIL import Image
+import io
+from datasets import load_dataset
+
+# ------------------------------
+# Configuración
+# ------------------------------
+MODEL_PATH = "modelo.keras"  # Extensión .keras (Keras 3)
+IMG_SIZE = (64, 64)
+
+def build_model():
+    model = models.Sequential([
+        layers.Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)),
+        layers.MaxPooling2D((2, 2)),
+        layers.Conv2D(64, (3, 3), activation='relu'),
+        layers.Flatten(),
+        layers.Dense(64, activation='relu'),
+        layers.Dense(1, activation='sigmoid')   # 1 = blanco, 0 = negro
+    ])
+    model.compile(optimizer='adam',
+                  loss='binary_crossentropy',
+                  metrics=['accuracy'])
+    return model
+
+def train_and_save_model():
+    print("Cargando dataset FairFace...")
+    ds = load_dataset("HuggingFaceM4/FairFace", "0.25")
+        
+    ds_filtered = ds['train']
+    
+    # Preparar imágenes y etiquetas
+    images = []
+    labels = []
+    for item in ds_filtered:
+        img = item['image'].convert('RGB').resize(IMG_SIZE)
+        images.append(np.array(img) / 255.0)
+        labels.append(1.0 if item['gender'] == 0 else 0.0)
+    
+    X = np.array(images)
+    y = np.array(labels)
+    
+    print(f"Datos cargados: {len(X)} imágenes (blancas: {sum(y)}, negras: {len(y)-sum(y)})")
+    
+    # Entrenar modelo
+    model = build_model()
+    print("Entrenando clasificador de género...")
+    model.fit(X, y, epochs=5, batch_size=32, validation_split=0.2, verbose=1)
+    
+    # Guardar en formato Keras 3 (.keras)
+    model.save(MODEL_PATH)
+    print(f"Modelo guardado como {MODEL_PATH}")
+    return model
+
+# Cargar modelo con reintento si falla
+def load_or_train_model():
+    if os.path.exists(MODEL_PATH):
+        try:
+            model = tf.keras.models.load_model(MODEL_PATH)
+            print("✅ Modelo cargado desde disco correctamente")
+            return model
+        except Exception as e:
+            print(f"⚠️ Error al cargar el modelo: {e}")
+            print("Entrenando uno nuevo...")
+            return train_and_save_model()
+    else:
+        print("Modelo no encontrado. Entrenando desde cero...")
+        return train_and_save_model()
+# El resto de tu código Flask sigue igual debajo de esto...
+model = load_or_train_model()
+
+# ------------------------------
+# Aplicación Flask
+# ------------------------------
+app = Flask(__name__)
+
+def predecir_sexo(imagen_bytes):
+    img = Image.open(io.BytesIO(imagen_bytes)).convert('RGB')
+    img = img.resize(IMG_SIZE)
+    img_array = np.array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+    prediccion = model.predict(img_array, verbose=0)[0][0]
+    return "mujer" if prediccion >= 0.5 else "hombre"
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    if 'image' not in request.files:
+        return jsonify({'error': 'No se encontró ninguna imagen'}), 400
+    file = request.files['image']
+    if file.filename == '':
+        return jsonify({'error': 'Nombre de archivo vacío'}), 400
+    try:
+        imagen_bytes = file.read()
+        sexo = predecir_sexo(imagen_bytes)
+        return jsonify({'sexo': sexo})
+    except Exception as e:
+        return jsonify({'error': str(e)}), 500
+
+if __name__ == '__main__':
+    port = int(os.environ.get("PORT", 7860))
+    app.run(host='0.0.0.0', port=port)

modelo.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:312abcb4bec5ed5a5d69ae0f622bd220cf33cdcccfe89ba0874277c6315113d9
+size 41604508

static/script.js

@@ -0,0 +1,59 @@
+const imageInput = document.getElementById('imageInput');
+const previewImg = document.getElementById('previewImg');
+const classifyBtn = document.getElementById('classifyBtn');
+const resultDiv = document.getElementById('result');
+
+imageInput.addEventListener('change', (event) => {
+    const file = event.target.files[0];
+    if (file) {
+        const reader = new FileReader();
+        reader.onload = function(e) {
+            previewImg.src = e.target.result;
+            previewImg.style.display = 'block';
+            classifyBtn.disabled = false;
+            resultDiv.innerHTML = '';
+            resultDiv.className = 'result';
+        };
+        reader.readAsDataURL(file);
+    } else {
+        previewImg.style.display = 'none';
+        classifyBtn.disabled = true;
+    }
+});
+
+classifyBtn.addEventListener('click', async () => {
+    const file = imageInput.files[0];
+    if (!file) return;
+
+    classifyBtn.disabled = true;
+    classifyBtn.innerText = '🔍 Analizando...';
+    resultDiv.innerHTML = '<em>Procesando imagen...</em>';
+
+    const formData = new FormData();
+    formData.append('image', file);
+
+    try {
+        const response = await fetch('/predict', {
+            method: 'POST',
+            body: formData
+        });
+
+        if (!response.ok) {
+            const errorData = await response.json();
+            throw new Error(errorData.error || 'Error del servidor');
+        }
+
+        const data = await response.json();
+        const sexo = data.sexo;
+
+        resultDiv.innerHTML = `🧬 La persona es <strong>${sexo}</strong>.`;
+        resultDiv.classList.add(sexo === 'mujer' ? 'mujer' : 'hombre');
+    } catch (error) {
+        console.error(error);
+        resultDiv.innerHTML = `❌ Error: ${error.message}`;
+        resultDiv.classList.add('error');
+    } finally {
+        classifyBtn.disabled = false;
+        classifyBtn.innerText = '🔍 Clasificar';
+    }
+});

static/style.css

@@ -0,0 +1,110 @@
+body {
+    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+    background: linear-gradient(135deg, #1e3c72, #2a5298);
+    margin: 0;
+    min-height: 100vh;
+    display: flex;
+    justify-content: center;
+    align-items: center;
+    color: #333;
+}
+
+.container {
+    background: rgba(255, 255, 255, 0.95);
+    border-radius: 2rem;
+    padding: 2rem;
+    max-width: 500px;
+    width: 90%;
+    text-align: center;
+    box-shadow: 0 20px 35px rgba(0,0,0,0.2);
+    transition: transform 0.2s;
+}
+
+h1 {
+    margin-top: 0;
+    color: #1e3c72;
+}
+
+.upload-area {
+    margin: 1.5rem 0;
+}
+
+.file-label {
+    background-color: #2a5298;
+    color: white;
+    padding: 0.8rem 1.5rem;
+    border-radius: 2rem;
+    cursor: pointer;
+    display: inline-block;
+    transition: background 0.3s;
+}
+
+.file-label:hover {
+    background-color: #1e3c72;
+}
+
+input[type="file"] {
+    display: none;
+}
+
+.preview {
+    margin: 1rem 0;
+    min-height: 200px;
+    display: flex;
+    justify-content: center;
+}
+
+#previewImg {
+    max-width: 100%;
+    max-height: 250px;
+    border-radius: 1rem;
+    box-shadow: 0 4px 12px rgba(0,0,0,0.2);
+    display: none;
+}
+
+button {
+    background-color: #28a745;
+    color: white;
+    border: none;
+    padding: 0.8rem 1.8rem;
+    font-size: 1rem;
+    border-radius: 2rem;
+    cursor: pointer;
+    transition: all 0.2s;
+    margin-top: 1rem;
+}
+
+button:disabled {
+    background-color: #6c757d;
+    cursor: not-allowed;
+}
+
+button:hover:not(:disabled) {
+    background-color: #218838;
+    transform: scale(1.02);
+}
+
+.result {
+    margin-top: 1.5rem;
+    font-size: 1.6rem;
+    font-weight: bold;
+    padding: 1rem;
+    border-radius: 1rem;
+    background: #f8f9fa;
+}
+
+.result.hombre {
+    color: #28a745;
+    background: #e6f4ea;
+}
+
+.result.mujer {
+    color: #28a745;
+    background: #fce8e6;
+}
+
+footer {
+    margin-top: 2rem;
+    font-size: 0.8rem;
+    color: #6c757d;
+}

templates/index.html

@@ -0,0 +1,30 @@
+<!DOCTYPE html>
+<html lang="es">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Clasificador de sexo</title>
+    <link rel="stylesheet" href="{{ url_for('static', filename='style.css') }}">
+</head>
+<body>
+    <div class="container">
+        <h1>📸 Clasificador de sexo</h1>
+        <p>Sube una foto de una cara y te diré si es <strong>mujer</strong> o <strong>hombre</strong>.</p>
+        
+        <div class="upload-area">
+            <input type="file" id="imageInput" accept="image/*" capture="environment">
+            <label for="imageInput" class="file-label">📁 Seleccionar imagen</label>
+        </div>
+
+        <div class="preview">
+            <img id="previewImg" alt="Vista previa">
+        </div>
+
+        <button id="classifyBtn" disabled>🔍 Clasificar</button>
+
+        <div id="result" class="result"></div>
+    </div>
+
+    <script src="{{ url_for('static', filename='script.js') }}"></script>
+</body>
+</html>