Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from tensorflow.keras.preprocessing.image import load_img, img_to_array
+
+# Load the trained model
+model = tf.keras.models.load_model("my_model.h5")
+
+# Class labels (modify according to your dataset)
+class_labels = ["Angry", "Disgust", "Fear", "Happy", "Neutral", "Sad", "Surprise"]
+
+# Function to make predictions
+def predict_emotion(image):
+    # Resize image to match model's expected input
+    image = image.resize((224, 224))  
+    img_array = img_to_array(image) / 255.0  # Normalize
+    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+
+    # Make prediction
+    prediction = model.predict(img_array)
+    predicted_class = class_labels[np.argmax(prediction)]
+    confidence = np.max(prediction)
+
+    return f"Emotion: {predicted_class} (Confidence: {confidence:.2f})"
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=predict_emotion,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Textbox(label="Prediction"),
+    title="Facial Emotion Detection",
+    description="Upload an image, and the model will predict the emotion.",
+)
+
+# Run the app
+if __name__ == "__main__":
+    interface.launch()