Update app.py

app.py

@@ -38,54 +38,3 @@ interface = gr.Interface(
 
 # Launch the Gradio interface
 interface.launch()
-
-
-
-# import numpy as np
-# from tensorflow.keras.preprocessing.image import img_to_array, load_img
-# import gradio as gr
-# import gradio as gr
-# import tensorflow as tf
-# import numpy as np
-# from PIL import Image
-# import cv2
-# from tensorflow.keras.preprocessing import image
-
-# model = tf.keras.models.load_model('Final_Resnet50_Best_model.keras')
-
-# # Emotion labels dictionary
-# emotion_labels = {'angry': 0, 'disgust': 1, 'fear': 2, 'happy': 3, 'neutral': 4, 'sad': 5, 'surprise': 6}
-# index_to_emotion = {v: k for k, v in emotion_labels.items()}
-
-# def prepare_image(img_pil):
-#     """Preprocess the PIL image to fit your model's input requirements."""
-#     # Convert the PIL image to a numpy array with the target size
-#     img = img_pil.resize((224, 224))
-#     img_array = img_to_array(img)
-#     img_array = np.expand_dims(img_array, axis=0)  # Convert single image to a batch.
-#     img_array /= 255.0  # Rescale pixel values to [0,1], as done during training
-#     return img_array
-
-
-
-# # Define the Gradio interface
-# def predict_emotion(image):
-#     # Preprocess the image
-#     processed_image = prepare_image(image)
-#     # Make prediction using the model
-#     prediction = model.predict(processed_image)
-#     # Get the emotion label with the highest probability
-#     predicted_class = np.argmax(prediction, axis=1)
-#     predicted_emotion = index_to_emotion.get(predicted_class[0], "Unknown Emotion")
-#     return predicted_emotion
-
-# interface = gr.Interface(
-#     fn=predict_emotion,  # Your prediction function
-#     inputs=gr.Image(type="pil"),  # Input for uploading an image, directly compatible with PIL images
-#     outputs="text",  # Output as text displaying the predicted emotion
-#     title="Emotion Detection",
-#     description="Upload an image and see the predicted emotion."
-# )
-
-# # Launch the Gradio interface
-# interface.launch()