import gradio as gr
import tensorflow as tf
import cv2
import numpy as np

# Load the model
model = tf.keras.models.load_model("FER_DATA.keras")

# Define the emotion prediction function
def predict_emotion(image_path):
    image = cv2.imread(image_path)
    image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    image = cv2.resize(image, (48, 48))
    image = image / 255.0
    image = np.expand_dims(image, axis=-1)
    image = np.expand_dims(image, axis=0)
    prediction = model.predict(image)
    emotion_index = np.argmax(prediction)
    emotions = ['Angry', 'Happy', 'Sad', 'Neutral']
    return emotions[emotion_index]

# Launch the Gradio interface
iface = gr.Interface(
    fn=predict_emotion,
    inputs=gr.Image(type="filepath"),
    outputs="text",
    title="MoodSync - Emotion Detection"
)

# Make it public
iface.launch(share=True)