Update app.py

app.py

@@ -0,0 +1,46 @@
+import gradio as gr
+import tensorflow as tf
+import cv2
+import numpy as np
+
+# Load the FER model from the current directory (make sure the model is uploaded to Hugging Face Space)
+model = tf.keras.models.load_model("FER_DATA.keras")
+
+# Function to predict emotion
+def predict_emotion(image):
+    # Convert the image to grayscale as the model expects grayscale images
+    image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+    
+    # Resize image to (48, 48) as expected by the model
+    image = cv2.resize(image, (48, 48))
+    
+    # Normalize the image
+    image = image / 255.0
+    
+    # Add a channel dimension (model expects a single channel)
+    image = np.expand_dims(image, axis=-1)
+    
+    # Add a batch dimension (model expects a batch of images)
+    image = np.expand_dims(image, axis=0)
+    
+    # Make a prediction
+    prediction = model.predict(image)
+    
+    # Get the predicted emotion
+    emotion = np.argmax(prediction)
+    emotions = ['Angry', 'Happy', 'Sad', 'Neutral']
+    
+    # Return the predicted emotion
+    return emotions[emotion]
+
+# Set up Gradio Interface
+iface = gr.Interface(
+    fn=predict_emotion,  # Function that makes predictions
+    inputs=gr.Image(shape=(224, 224), image_mode="RGB"),  # Input is an image
+    outputs="text",  # Output is a text (emotion)
+    title="MoodSync - Emotion Detection",  # Title of the app
+    description="This app detects emotions from images using a deep learning model.",  # Description
+)
+
+# Launch the app
+iface.launch()