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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() | |