import gradio as gr
import torch
import cv2
import numpy as np
import time
from ultralytics import YOLO
import spaces
import os

class CrowdDetection:
    def __init__(self, model_path="yolov8n.pt"):
        """Initialize the YOLO model once."""
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        if not os.path.exists(model_path):
            self.model = YOLO("yolov8n.pt")  # Downloads if not present
            self.model.save(model_path)
        else:
            self.model = YOLO(model_path)
        self.model.to(self.device)

    @spaces.GPU
    def detect_crowd(self, video_path):
        """Process video for crowd detection."""
        cap = cv2.VideoCapture(video_path)
        if not cap.isOpened():
            raise ValueError(f"❌ Failed to open video: {video_path}")

        fps = int(cap.get(cv2.CAP_PROP_FPS))
        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))

        output_path = "output_crowd.mp4"
        fourcc = cv2.VideoWriter_fourcc(*"mp4v")
        out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
        if not out.isOpened():
            cap.release()
            raise ValueError(f"❌ Failed to initialize video writer")

        CROWD_THRESHOLD = 10
        frame_count = 0

        while cap.isOpened():
            ret, frame = cap.read()
            if not ret:
                break
            frame_count += 1

            results = self.model(frame)
            person_count = sum(1 for result in results for cls in result.boxes.cls.cpu().numpy() if int(cls) == 0)

            for result in results:
                boxes = result.boxes.xyxy.cpu().numpy()
                classes = result.boxes.cls.cpu().numpy()
                for box, cls in zip(boxes, classes):
                    if int(cls) == 0:  # Person class
                        x1, y1, x2, y2 = map(int, box)
                        cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
                        cv2.putText(frame, "Person", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

            alert_text = "Crowd Alert!" if person_count > CROWD_THRESHOLD else f"People: {person_count}"
            cv2.putText(frame, alert_text, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1,
                        (0, 0, 255) if person_count > CROWD_THRESHOLD else (0, 255, 0), 2)
            out.write(frame)

        cap.release()
        out.release()
        if frame_count == 0 or not os.path.exists(output_path):
            raise ValueError("❌ Processing failed")
        return output_path

class PeopleTracking:
    def __init__(self, yolo_model_path="yolov8n.pt"):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        if not os.path.exists(yolo_model_path):
            self.model = YOLO("yolov8n.pt")
            self.model.save(yolo_model_path)
        else:
            self.model = YOLO(yolo_model_path)
        self.model.to(self.device)

    @spaces.GPU
    def track_people(self, video_path):
        cap = cv2.VideoCapture(video_path)
        if not cap.isOpened():
            raise ValueError(f"❌ Failed to open video: {video_path}")

        fps = int(cap.get(cv2.CAP_PROP_FPS))
        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
        output_path = "output_tracking.mp4"
        out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (width, height))
        if not out.isOpened():
            cap.release()
            raise ValueError(f"❌ Failed to initialize video writer")

        while cap.isOpened():
            ret, frame = cap.read()
            if not ret:
                break

            results = self.model.track(frame, persist=True)
            for result in results:
                boxes = result.boxes.xyxy.cpu().numpy()
                classes = result.boxes.cls.cpu().numpy()
                ids = result.boxes.id.cpu().numpy() if result.boxes.id is not None else np.arange(len(boxes))

                for box, cls, obj_id in zip(boxes, classes, ids):
                    if int(cls) == 0:
                        x1, y1, x2, y2 = map(int, box)
                        cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 2)
                        cv2.putText(frame, f"ID {int(obj_id)}", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)

            out.write(frame)

        cap.release()
        out.release()
        if not os.path.exists(output_path):
            raise ValueError("❌ Processing failed")
        return output_path

class FallDetection:
    def __init__(self, yolo_model_path="yolov8l.pt"):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        if not os.path.exists(yolo_model_path):
            self.model = YOLO("yolov8l.pt")
            self.model.save(yolo_model_path)
        else:
            self.model = YOLO(yolo_model_path)
        self.model.to(self.device)

    @spaces.GPU
    def detect_fall(self, video_path):
        cap = cv2.VideoCapture(video_path)
        if not cap.isOpened():
            raise ValueError(f"❌ Failed to open video: {video_path}")

        fps = int(cap.get(cv2.CAP_PROP_FPS))
        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
        output_path = "output_fall.mp4"
        out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (width, height))
        if not out.isOpened():
            cap.release()
            raise ValueError(f"❌ Failed to initialize video writer")

        while cap.isOpened():
            ret, frame = cap.read()
            if not ret:
                break

            results = self.model(frame)
            for result in results:
                boxes = result.boxes.xyxy.cpu().numpy()
                classes = result.boxes.cls.cpu().numpy()

                for box, cls in zip(boxes, classes):
                    if int(cls) == 0:
                        x1, y1, x2, y2 = map(int, box)
                        width = x2 - x1
                        height = y2 - y1
                        aspect_ratio = width / height if height > 0 else float('inf')

                        if aspect_ratio > 0.55:  # Person lying down
                            color = (0, 0, 255)
                            label = "FALL DETECTED"
                        else:
                            color = (0, 255, 0)
                            label = "Standing"
                        
                        cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
                        cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)

            out.write(frame)

        cap.release()
        out.release()
        if not os.path.exists(output_path):
            raise ValueError("❌ Processing failed")
        return output_path

class FightDetection:
    def __init__(self, yolo_model_path="yolov8n-pose.pt"):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        if not os.path.exists(yolo_model_path):
            self.model = YOLO("yolov8n-pose.pt")
            self.model.save(yolo_model_path)
        else:
            self.model = YOLO(yolo_model_path)
        self.model.to(self.device)

    @spaces.GPU
    def detect_fight(self, video_path):
        cap = cv2.VideoCapture(video_path)
        if not cap.isOpened():
            raise ValueError(f"❌ Failed to open video: {video_path}")

        fps = int(cap.get(cv2.CAP_PROP_FPS))
        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
        output_path = "output_fight.mp4"
        out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (width, height))
        if not out.isOpened():
            cap.release()
            raise ValueError(f"❌ Failed to initialize video writer")

        while cap.isOpened():
            ret, frame = cap.read()
            if not ret:
                break

            results = self.model.track(frame, persist=True)
            fight_detected = False
            person_count = 0

            for result in results:
                keypoints = result.keypoints.xy.cpu().numpy() if result.keypoints else []
                boxes = result.boxes.xyxy.cpu().numpy() if result.boxes else []
                classes = result.boxes.cls.cpu().numpy() if result.boxes else []

                for box, kp, cls in zip(boxes, keypoints, classes):
                    if int(cls) == 0:
                        person_count += 1
                        x1, y1, x2, y2 = map(int, box)
                        # Simple fight detection: check if arms (keypoints 5, 7) are raised high
                        if len(kp) > 7 and (kp[5][1] < y1 + (y2 - y1) * 0.3 or kp[7][1] < y1 + (y2 - y1) * 0.3):
                            fight_detected = True
                        cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255) if fight_detected else (0, 255, 0), 2)
                        label = "FIGHT DETECTED" if fight_detected else "Person"
                        cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, 
                                    (0, 0, 255) if fight_detected else (0, 255, 0), 2)

            if fight_detected and person_count > 1:
                cv2.putText(frame, "FIGHT ALERT!", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
            out.write(frame)

        cap.release()
        out.release()
        if not os.path.exists(output_path):
            raise ValueError("❌ Processing failed")
        return output_path

# Unified processing function
def process_video(feature, video):
    detectors = {
        "Crowd Detection": CrowdDetection,
        "People Tracking": PeopleTracking,
        "Fall Detection": FallDetection,
        "Fight Detection": FightDetection
    }
    try:
        detector = detectors[feature]()
        method_name = feature.lower().replace(" ", "_")  # Match method names exactly
        output_path = getattr(detector, method_name)(video)
        return output_path
    except Exception as e:
        raise ValueError(f"Error processing video: {str(e)}")

# Gradio Interface
interface = gr.Interface(
    fn=process_video,
    inputs=[
        gr.Dropdown(choices=["Crowd Detection", "People Tracking", "Fall Detection", "Fight Detection"], label="Select Feature"),
        gr.Video(label="Upload Video")
    ],
    outputs=gr.Video(label="Processed Video"),
    title="YOLOv8 Multitask Video Processing",
    description="Select a feature to process your video: Crowd Detection, People Tracking, Fall Detection, or Fight Detection."
)

if __name__ == "__main__":
    interface.launch(debug=True)