Delete app.py

app.py

@@ -1,38 +0,0 @@
-
-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()