import cv2
from deepface import DeepFace

# Load the pre-trained emotion recognition model
model = DeepFace.build_model("Emotion")

# Initialize the webcam
cap = cv2.VideoCapture(0)

if not cap.isOpened():
    print("Error: Could not open webcam.")
    exit()

# Define the emotion labels
emotion_labels = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']

while True:
    # Capture frame-by-frame
    ret, frame = cap.read()
    if not ret:
        print("Error: Could not read frame.")
        break

    # Convert the frame to RGB (required by DeepFace)
    rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

    # Analyze the frame for emotions
    try:
        results = DeepFace.analyze(rgb_frame, actions=['emotion'], enforce_detection=False)
        for result in results:
            # Get the dominant emotion
            dominant_emotion = result['dominant_emotion']
            emotion_scores = result['emotion']

            # Display the dominant emotion on the frame
            cv2.putText(frame, f"Emotion: {dominant_emotion}", (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)

            # Display emotion scores (optional)
            y_offset = 80
            for emotion, score in emotion_scores.items():
                cv2.putText(frame, f"{emotion}: {score:.2f}", (10, y_offset), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 1)
                y_offset += 30
    except Exception as e:
        print(f"Error analyzing frame: {e}")

    # Display the frame
    cv2.imshow('Emotion Recognition', frame)

    # Break the loop if 'q' is pressed
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release the webcam and close all OpenCV windows
cap.release()
cv2.destroyAllWindows()