Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# Load the trained model (ensure the model file is uploaded)
+model = tf.keras.models.load_model('best_model.h5')
+
+# Define the prediction function
+def classify_image(img):
+    img = img.resize((150, 150))  # Resize to match model input
+    img = np.array(img) / 255.0   # Normalize pixel values
+    img = np.expand_dims(img, axis=0)  # Add batch dimension
+
+    # Make prediction
+    prediction = model.predict(img)
+    class_names = ['Boy', 'Girl']  # Labels for prediction
+    predicted_class = class_names[np.argmax(prediction)]
+    
+    return predicted_class
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.inputs.Image(type="pil"),
+    outputs=gr.outputs.Label(),
+    title="Boy or Girl Classifier",
+    description="Upload an image, and the model will predict whether it's a boy or a girl."
+)
+
+# Launch the app
+interface.launch()