import gradio as gr
from deepface import DeepFace

# Function to analyze face
def analyze_face(image):
    try:
        results = DeepFace.analyze(
            img_path = image,
            actions = ['emotion'],
            detector_backend = 'retinaface',  # more accurate than opencv
            enforce_detection = False
        )

        if results is None or len(results) == 0:
            return "Not a Face"

        outputs = []
        for i, face in enumerate(results):
            emotion = face['dominant_emotion']
            outputs.append(f"Face {i+1}: {emotion.capitalize()}")

        return "\n".join(outputs)

    except Exception as e:
        return "Not a Face"

# Gradio UI
iface = gr.Interface(
    fn = analyze_face,
    inputs = [
        gr.Image(type="filepath", label="Upload or Capture Image")
    ],
    outputs = [
        gr.Textbox(label="Prediction")
    ],
    title = "Face Emotion Detector application 😊",
    description = "Upload an image or take a photo. The app detects if a face is present, then predicts the dominant emotion. If no face is detected, it returns 'Not a Face'."
)

if __name__ == "__main__":
    iface.launch(server_name="0.0.0.0", server_port=7860)