Delete app2.py

app2.py

@@ -1,56 +0,0 @@
-import gradio as gr
-import tensorflow as tf
-import numpy as np
-from PIL import Image # Pillow for image processing
-import os
-
-# Hugging Face Space Link: https://huggingface.co/spaces/YourUsername/YourSpaceName
-
-# Load the trained Keras model
-model = tf.keras.models.load_model('facial_emotion_model.h5')
-
-# Define the class labels explicitly, as train_generator is not available in deployment
-# These labels should match the order your model was trained on.
-class_labels = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18']
-
-# Define target image size used during training
-TARGET_SIZE = (128, 128)
-
-def preprocess_image(image) -> np.ndarray:
-    """Preprocesses the input image for model prediction."""
-    # Resize image to target size
-    image = image.resize(TARGET_SIZE)
-    # Convert to numpy array
-    image_array = np.array(image)
-    # Normalize pixel values to [0, 1]
-    image_array = image_array / 255.0
-    # Expand dimensions to create a batch (1, height, width, channels)
-    image_array = np.expand_dims(image_array, axis=0)
-    return image_array
-
-def predict_emotion(image) -> dict:
-    """Predicts the emotion from a facial image and returns probabilities."""
-    if image is None:
-        return {label: 0.0 for label in class_labels}
-
-    # Preprocess the image
-    processed_image = preprocess_image(image)
-
-    # Make prediction
-    predictions = model.predict(processed_image)[0]
-
-    # Create a dictionary of emotion labels and their probabilities
-    results = {class_labels[i]: float(predictions[i]) for i in range(len(class_labels))}
-    return results
-
-# Create the Gradio interface
-iface = gr.Interface(
-    fn=predict_emotion,
-    inputs=gr.Image(type="pil", label="Upload Face Image"),
-    outputs=gr.Label(num_top_classes=len(class_labels)),
-    title="Facial Emotion Recognition",
-    description="Upload an image of a face to get emotion predictions."
-)
-
-# Launch the interface
-iface.launch()