app.py

import gradio as gr
import cv2
from deepface import DeepFace
import numpy as np

def predict_emotion(image):
    # Convert Gradio image (PIL format) to an OpenCV image
    img = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
    
    # Analyze the emotion using DeepFace
    result = DeepFace.analyze(img, actions=['emotion'])
    
    # Get the dominant emotion
    dominant_emotion = result[0]['dominant_emotion']
    
    return dominant_emotion

# Define the Gradio interface using the new API
iface = gr.Interface(fn=predict_emotion, 
                     inputs=gr.Image(type="pil"),  # Updated gr.Image input
                     outputs="text",  # Text output for dominant emotion
                     title="Facial Emotion Recognizer",
                     description="Upload an image and get the predicted emotion")

# Launch the Gradio app
iface.launch(share=True)