import cv2
from deepface import DeepFace
import gradio as gr
import os

def analyze_emotion_from_video(video_path):
    if not video_path or not os.path.exists(video_path):
        return "❌ Error: No video file found or path is invalid."

    cap = cv2.VideoCapture(video_path)
    if not cap.isOpened():
        return "❌ Error: Could not open the video file."

    frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    middle_frame_index = frame_count // 2
    cap.set(cv2.CAP_PROP_POS_FRAMES, middle_frame_index)
    success, frame = cap.read()

    if not success or frame is None:
        return "❌ Error: Could not read a valid frame from the video."

    try:
        analysis = DeepFace.analyze(frame, actions=['emotion'], enforce_detection=False)
        dominant_emotion = analysis[0]['dominant_emotion']
        return f"✅ Dominant Emotion: {dominant_emotion}"
    except Exception as e:
        return f"❌ Error during analysis: {str(e)}"
    finally:
        cap.release()

interface = gr.Interface(
    fn=analyze_emotion_from_video,
    inputs=gr.Video(label="Upload a Video File"),
    outputs=gr.Textbox(label="Emotion Analysis Result"),
    title="🎭 Video Emotion Detection",
    description="Upload a short video with a visible face to detect the dominant emotion using DeepFace."
)

interface.launch()