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app.py

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+import gradio as gr
+import numpy as np
+import tensorflow as tf
+import cv2
+
+# Load your trained model
+model = tf.keras.models.load_model('path_to_your_model.h5')
+
+def predict_gender(image):
+    # Convert image to format expected by your model & preprocess
+    img = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+    img = cv2.resize(img, (224, 224))  # Example size
+    img = img / 255.0  # Normalizing
+    img = np.expand_dims(img, axis=0)
+
+    prediction = model.predict(img)
+
+    # Assuming binary classification with a single output neuron
+    return "Male" if prediction[0] < 0.5 else "Female"
+
+# Define Gradio interface
+iface = gr.Interface(
+    fn=predict_gender,
+    inputs=gr.inputs.Image(type="webcam", label="Capture an Image from Webcam"),
+    outputs=gr.outputs.Label(),
+    live=True
+)
+
+iface.launch()

requeriments.txt

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+gradio
+opencv-python
+tensorflow