main.py

import os
import uuid
import cv2
import torch
import torch.nn as nn
import numpy as np
import datetime
import threading
import httpx
import time
import asyncio
from collections import deque
from fastapi import FastAPI, UploadFile, File, BackgroundTasks, HTTPException, Query, Request
from fastapi.responses import FileResponse, RedirectResponse, StreamingResponse
from pydantic import BaseModel
from pytorchvideo.models.hub import slowfast_r50
from ultralytics import YOLO
from dotenv import load_dotenv
load_dotenv(override=True)


# --- DOCUMENTATION METADATA ---
tags_metadata = [
    {
        "name": "Live Stream Monitoring",
        "description": "Real-time AI surveillance for RTSP cameras.",
    },
    {
        "name": "Recorded Video Analysis",
        "description": "Upload and process video files for theft detection.",
    },
]

description = """
<details>
<summary><b> USER GUIDE & ENDPOINT MANUAL (Click to Expand)</b></summary>

<br>

###  Section 1: Live Stream Monitoring
*Use these endpoints to manage and watch live AI security feeds.*

1. **`POST /stream/create` (Register Camera)**
   - **What it does:** Saves your camera's RTSP address into the system.
   - **How to use:** Enter a name, the RTSP link, and the location.

2. **`POST /stream/start/{id}` (Activate AI)**
   - **What it does:** Starts the AI processing. 
   - **How to use:** Returns stream URL to view the stream.

3. **`PUT /stream/update/{id}` (Change Settings)**
   - **What it does:** Updates the name or RTSP link of an existing camera.

4. **`DELETE /stream/delete/{id}` (Remove Camera)**
   - **What it does:** Stops the stream and removes the camera from the list.

5. **`GET /cameras/{id}/frame` (Main Video Feed)**
   - **What it does:** The actual live video stream.

6. **`GET /stream/list_cameras` (Camera Directory)**
   - **What it does:** Displays all added cameras and their status.

7. **`POST /stream/stop/{id}` (Deactivate AI)**
   - **What it does:** Shuts down the AI for a camera.

<br>

###  Section 2: Recorded Video Analysis
*Use these endpoints to scan uploaded files for theft incidents.*

1. **`POST /video/detect` (Upload for Analysis)**
   - **What it does:** Uploads a video file for a full AI scan. Returns a **job_id**.

2. **`GET /video/status/{job_id}` (Check Progress)**
   - **What it does:** Provides the scan percentage (0% to 100%).

3. **`GET /video/jobs` (Task History)**
   - **What it does:** Lists every video ever uploaded.

4. **`GET /video/download/{job_id}` (Get Result)**
   - **What it does:** Download the final processed video once status is 100%.

</details>

---
"""


# --- CONFIGURATION ---
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
MODEL_PATH = "best_slowfast_theft.pth"
DISCORD_WEBHOOK_URL = os.getenv("DISCORD_WEBHOOK_URL")
UPLOAD_DIR = "uploads"
OUTPUT_DIR = "outputs"
CLIP_LEN = 32
IMG_SIZE = 224
THEFT_THRESHOLD = 0.6

os.makedirs(UPLOAD_DIR, exist_ok=True)
os.makedirs(OUTPUT_DIR, exist_ok=True)

app = FastAPI(title="AI Theft Detection Backend",
               description=description,
               openapi_tags=tags_metadata,
                version="1.0.0")

# --- DATABASE & STATE ---
MOCK_DB = {"cameras": {}}
jobs = {} 

# --- LOAD MODELS ---
print(f"Initializing Models on {DEVICE}...")
yolo = YOLO("yolov8n.pt")
slowfast_model = slowfast_r50(pretrained=False)
slowfast_model.blocks[-1].proj = nn.Sequential(
    nn.Dropout(p=0.5), 
    nn.Linear(slowfast_model.blocks[-1].proj.in_features, 2)
)
if os.path.exists(MODEL_PATH):
    ckpt = torch.load(MODEL_PATH, map_location=DEVICE)
    slowfast_model.load_state_dict(ckpt["model"] if "model" in ckpt else ckpt)
slowfast_model = slowfast_model.to(DEVICE).eval()

# --- SCHEMAS ---
class CameraCreate(BaseModel):
    name: str; rtspUrl: str; location: str

class CameraUpdate(BaseModel):
    name: str = None; rtspUrl: str = None; location: str = None

# --- NOTIFICATION LOGIC ---

import json

async def send_discord_alert(source_name, score, crop_frame=None, mode="live"):
    if not DISCORD_WEBHOOK_URL or "YOUR_DISCORD" in DISCORD_WEBHOOK_URL:
        return

    now = datetime.datetime.now().strftime("%Y-%m-%d %I:%M:%S %p")
    filename = "theft_crop.jpg"
    footer_text = "Security AI Alert System • Recorded video" if mode == "recorded" else "Security AI Alert System • Live Monitoring"
    
    payload = {
        "username": "Surveillance Monitoring",
        "avatar_url": "https://cdn-icons-png.flaticon.com/512/2564/2564388.png",
        "embeds": [{
            "title": "🚨 THEFT DETECTED",
            "description": f"AI System has flagged suspicious activity at **{source_name}**.",
            "color": 15548997,
            "fields": [
                {"name": "📊 Confidence Score", "value": f"**{int(score*100)}%**", "inline": True},
                {"name": "📍 Location", "value": f"**{source_name}**", "inline": True},
                {"name": "⏱️ Timestamp", "value": f"**{now}**", "inline": False}
            ],
            "image": {"url": f"attachment://{filename}"},
            "footer": {"text": footer_text}
        }]
    }

    try:
        async with httpx.AsyncClient() as client:
            if crop_frame is not None:
                _, buffer = cv2.imencode('.jpg', crop_frame)
                files = {
                    'file': (filename, buffer.tobytes(), 'image/jpeg')
                }
                await client.post(
                    DISCORD_WEBHOOK_URL, 
                    data={"payload_json": json.dumps(payload)}, 
                    files=files
                )
            else:
                await client.post(DISCORD_WEBHOOK_URL, json=payload)
    except Exception as e:
        print(f"Discord Alert Error: {e}")

# --- VISUALIZATION HELPERS ---

def draw_corner_rect(img, pt1, pt2, color, thickness=2, r=15, d=25):
    x1, y1 = pt1
    x2, y2 = pt2
    cv2.line(img, (x1+r, y1), (x1+r+d, y1), color, thickness)
    cv2.line(img, (x1, y1+r), (x1, y1+r+d), color, thickness)
    cv2.ellipse(img, (x1+r, y1+r), (r,r), 180, 0, 90, color, thickness)
    cv2.line(img, (x2-r, y1), (x2-r-d, y1), color, thickness)
    cv2.line(img, (x2, y1+r), (x2, y1+r+d), color, thickness)
    cv2.ellipse(img, (x2-r, y1+r), (r,r), 270, 0, 90, color, thickness)
    cv2.line(img, (x1+r, y2), (x1+r+d, y2), color, thickness)
    cv2.line(img, (x1, y2-r), (x1, y2-r-d), color, thickness)
    cv2.ellipse(img, (x1+r, y2-r), (r,r), 90, 0, 90, color, thickness)
    cv2.line(img, (x2-r, y2), (x2-r-d, y2), color, thickness)
    cv2.line(img, (x2, y2-r), (x2, y2-r-d), color, thickness)
    cv2.ellipse(img, (x2-r, y2-r), (r,r), 0, 0, 90, color, thickness)

def draw_security_card(frame, avg_prob, theft_flag, title="AI ANALYZER"):
    card_x, card_y = 35, 35
    padding = 30
    line_spacing = 45
    card_w, card_h = 620, 260
    orange = (0, 165, 255)
    bg_color = (25, 25, 25)
    overlay = frame.copy()
    cv2.rectangle(overlay, (card_x, card_y), (card_x + card_w, card_y + card_h), bg_color, -1)
    cv2.addWeighted(overlay, 0.85, frame, 0.15, 0, frame)
    cv2.rectangle(frame, (card_x, card_y), (card_x + card_w, card_y + card_h), orange, 3)
    status_label = "ALERT: THEFT DETECTED" if theft_flag else "STATUS: SYSTEM NORMAL"
    status_color = (0, 0, 255) if theft_flag else (0, 255, 0)
    now = datetime.datetime.now().strftime("%b %d, %Y  |  %I:%M:%S %p")
    confidence = f"AI CONFIDENCE: {int(avg_prob * 100)}%"
    curr_y = card_y + padding + 15
    header_text = "THEFT DETECTION LIVE MONITORING"
    (tw, th), _ = cv2.getTextSize(header_text, cv2.FONT_HERSHEY_DUPLEX, 0.9, 2)
    cv2.putText(frame, header_text, (card_x + (card_w - tw) // 2, curr_y), 
                cv2.FONT_HERSHEY_DUPLEX, 0.9, (255, 255, 255), 2, cv2.LINE_AA)
    curr_y += line_spacing + 5
    cv2.putText(frame, f"SOURCE: {title.upper()}", (card_x + padding, curr_y), 
                cv2.FONT_HERSHEY_DUPLEX, 0.8, (200, 200, 200), 1, cv2.LINE_AA)
    curr_y += line_spacing
    cv2.putText(frame, status_label, (card_x + padding, curr_y), 
                cv2.FONT_HERSHEY_DUPLEX, 1.0, status_color, 2, cv2.LINE_AA)
    curr_y += line_spacing
    cv2.putText(frame, confidence, (card_x + padding, curr_y), 
                cv2.FONT_HERSHEY_DUPLEX, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
    curr_y += line_spacing
    cv2.putText(frame, now, (card_x + padding, curr_y), 
                cv2.FONT_HERSHEY_SIMPLEX, 0.6, (150, 150, 150), 1, cv2.LINE_AA)

# --- PROCESSING CORE ---

def preprocess(frames):
    processed = [cv2.resize(f, (IMG_SIZE, IMG_SIZE))[:, :, ::-1] / 255.0 for f in frames]
    clip = np.transpose(np.array(processed), (3, 0, 1, 2))
    return torch.tensor(clip).float().unsqueeze(0).to(DEVICE)

# --- BACKGROUND TASK: VIDEO FILE ---
async def process_video_file(job_id, in_p, out_p):
    try:
        cap = cv2.VideoCapture(in_p)
        w, h = int(cap.get(3)), int(cap.get(4))
        fps, total_frames = int(cap.get(5)), int(cap.get(7))
        out = cv2.VideoWriter(out_p, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))

        frame_buffer = deque(maxlen=CLIP_LEN)
        prediction_buffer = deque(maxlen=10)
        last_alert_time = 0
        frame_counter = 0
        source_name = jobs.get(job_id, {}).get("filename") or f"Recorded Video ({job_id})"

        while cap.isOpened():
            if jobs.get(job_id, {}).get("stop_requested"): break
            ret, frame = cap.read()
            if not ret: break
            frame_counter += 1
            
            theft_flag, avg_prob, current_crop = False, 0.0, None
            results = yolo(frame, verbose=False)
            
            for r in results:
                for box in r.boxes:
                    if int(box.cls[0]) == 0:
                        x1, y1, x2, y2 = map(int, box.xyxy[0])
                        current_crop = frame[y1:y2, x1:x2]
                        if current_crop.size > 0:
                            frame_buffer.append(current_crop)
                            if len(frame_buffer) == CLIP_LEN:
                                clip_ts = preprocess(frame_buffer)
                                with torch.no_grad():
                                    probs = torch.softmax(slowfast_model([clip_ts[:,:,::4,:,:], clip_ts]), dim=1)
                                prediction_buffer.append(probs[0][1].item())
                                avg_prob = np.mean(prediction_buffer)

                        if avg_prob > THEFT_THRESHOLD:
                            theft_flag = True
                            draw_corner_rect(frame, (x1,y1), (x2,y2), (0,0,255))
                            current_time = time.time()
                            if current_time - last_alert_time > 30 and current_crop is not None:
                                await send_discord_alert(source_name, avg_prob, current_crop, mode="recorded")
                                last_alert_time = current_time
                        else:
                            draw_corner_rect(frame, (x1,y1), (x2,y2), (0,255,0))

            draw_security_card(frame, avg_prob, theft_flag, "VIDEO ANALYSIS")
            out.write(frame)
            jobs[job_id]["progress"] = int((frame_counter / total_frames) * 100)

        cap.release(); out.release()
        jobs[job_id]["status"] = "completed" if not jobs[job_id].get("stop_requested") else "stopped"
    except Exception as e:
        jobs[job_id]["status"] = f"failed: {str(e)}"

# --- RTSP CAMERA PIPELINE ---

class CameraPipeline:
    def __init__(self, cam_id, name, url):
        self.cam_id = cam_id
        self.name = name
        self.url = url
        self.running = True
        self.latest_frame = None
        self.frame_buffer = deque(maxlen=CLIP_LEN)
        self.prediction_buffer = deque(maxlen=10)
        self.last_alert_time = 0
        self.raw_frame = None
        self.capture_thread = threading.Thread(target=self._capture_loop, daemon=True)
        self.ai_thread = threading.Thread(target=self._ai_loop, daemon=True)
        self.capture_thread.start()
        self.ai_thread.start()

    def _capture_loop(self):
        os.environ["OPENCV_FFMPEG_CAPTURE_OPTIONS"] = "rtsp_transport;tcp"
        cap = cv2.VideoCapture(self.url, cv2.CAP_FFMPEG)
        cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
        while self.running:
            ret, frame = cap.read()
            if not ret:
                cap.release()
                time.sleep(2)
                cap = cv2.VideoCapture(self.url, cv2.CAP_FFMPEG)
                continue
            self.raw_frame = frame
        cap.release()

    def _ai_loop(self):
        while self.running:
            if self.raw_frame is None:
                time.sleep(0.01)
                continue
            frame = self.raw_frame.copy()
            theft_flag, avg_prob = False, 0.0
            current_crop = None
            results = yolo(frame, verbose=False, conf=0.4)
            for r in results:
                for box in r.boxes:
                    if int(box.cls[0]) == 0:
                        x1, y1, x2, y2 = map(int, box.xyxy[0])
                        current_crop = frame[y1:y2, x1:x2]
                        if current_crop.size > 0:
                            p_frame = cv2.resize(current_crop, (IMG_SIZE, IMG_SIZE))[:,:,::-1]/255.0
                            self.frame_buffer.append(p_frame)
                            if len(self.frame_buffer) == CLIP_LEN:
                                clip = np.transpose(np.array(self.frame_buffer), (3,0,1,2))
                                clip_ts = torch.tensor(clip).float().unsqueeze(0).to(DEVICE)
                                with torch.no_grad():
                                    probs = torch.softmax(slowfast_model([clip_ts[:,:,::4,:,:], clip_ts]), dim=1)
                                self.prediction_buffer.append(probs[0][1].item())
                                avg_prob = np.mean(prediction_buffer)
                        is_theft = avg_prob > THEFT_THRESHOLD
                        draw_corner_rect(frame, (x1,y1), (x2,y2), (0,0,255) if is_theft else (0,255,0))
                        if is_theft:
                            theft_flag = True
            if theft_flag and (time.time() - self.last_alert_time > 30) and current_crop is not None:
                try:
                    asyncio.run(send_discord_alert(self.name, avg_prob, current_crop, mode="live"))
                except RuntimeError:
                    loop = asyncio.new_event_loop()
                    loop.run_until_complete(send_discord_alert(self.name, avg_prob, current_crop, mode="live"))
                    loop.close()
                self.last_alert_time = time.time()
            draw_security_card(frame, avg_prob, theft_flag, self.name)
            self.latest_frame = frame

class StreamManager:
    def __init__(self):
        self.active_pipelines = {}
    def start_camera(self, cam_id, name, url):
        if cam_id in self.active_pipelines:
            return False
        self.active_pipelines[cam_id] = CameraPipeline(cam_id, name, url)
        return True
    def stop_camera(self, cam_id):
        if cam_id not in self.active_pipelines:
            return False
        self.active_pipelines[cam_id].running = False
        del self.active_pipelines[cam_id]
        return True

manager = StreamManager()

# --- ENDPOINTS ---

@app.get("/", include_in_schema=False)
async def root(): return RedirectResponse(url="/docs")

# --- CAMERA ENDPOINTS ---
@app.get("/stream/list_cameras",  tags=["Live Stream Monitoring"])
def get_cameras(): return {"cameras": list(MOCK_DB["cameras"].values())}

@app.post("/stream/create",  tags=["Live Stream Monitoring"])
def create_camera(cam: CameraCreate):
    new_id = f"cam-{len(MOCK_DB['cameras']) + 1}"
    camera = {**cam.dict(), "id": new_id, "status": "offline", "isStreaming": False}
    MOCK_DB["cameras"][new_id] = camera
    return {"camera": camera}

@app.post("/stream/start/{id}",  tags=["Live Stream Monitoring"])
async def start_camera(id: str, request: Request):
    if id not in MOCK_DB["cameras"]:
        raise HTTPException(404)
    cam = MOCK_DB["cameras"][id]
    started = manager.start_camera(id, cam["name"], cam["rtspUrl"])
    if started:
        cam["status"] = "online"
        cam["isStreaming"] = True

    # Generate URLs for the user
    local_url = f"{request.base_url}cameras/{id}/frame"

    return {
        "success": started,
        "job_id": id,
        "view_urls": {
            "stream_url": str(local_url)
        }
    }

@app.put("/stream/update/{id}", tags=["Live Stream Monitoring"])
def update_camera(id: str, cam_data: CameraUpdate):
    if id not in MOCK_DB["cameras"]:
        raise HTTPException(status_code=404, detail="Camera not found")
    current_cam = MOCK_DB["cameras"][id]
    update_dict = cam_data.dict(exclude_unset=True)
    current_cam.update(update_dict)
    return {"message": "Camera updated successfully", "camera": current_cam}

@app.delete("/stream/delete/{id}", tags=["Live Stream Monitoring"])
def delete_camera(id: str):
    if id not in MOCK_DB["cameras"]:
        raise HTTPException(status_code=404, detail="Camera not found")
    manager.stop_camera(id)
    del MOCK_DB["cameras"][id]
    return {"message": f"Camera {id} deleted successfully"}

@app.post("/stream/stop/{id}",  tags=["Live Stream Monitoring"])
def stop_camera(id: str):
    if id not in MOCK_DB["cameras"]:
        raise HTTPException(404)
    stopped = manager.stop_camera(id)
    if stopped:
        MOCK_DB["cameras"][id]["status"] = "offline"
        MOCK_DB["cameras"][id]["isStreaming"] = False
    return {"success": stopped}

@app.get("/cameras/{id}/frame",  tags=["Live Stream Monitoring"])
def get_frame_legacy(id: str):
    if id not in manager.active_pipelines:
        raise HTTPException(404)
    def generate():
        while id in manager.active_pipelines:
            frame = manager.active_pipelines[id].latest_frame
            if frame is not None:
                _, buffer = cv2.imencode('.jpg', frame)
                yield (b'--frame\r\nContent-Type: image/jpeg\r\n\r\n' + buffer.tobytes() + b'\r\n')
            time.sleep(0.05)
    return StreamingResponse(generate(), media_type="multipart/x-mixed-replace; boundary=frame")

# --- VIDEO PROCESSING ENDPOINTS ---
@app.post("/video/detect", tags=["Recorded Video Analysis"])
async def detect(background_tasks: BackgroundTasks, file: UploadFile = File(...)):
    jid = str(uuid.uuid4())
    in_p = os.path.join(UPLOAD_DIR, f"{jid}_{file.filename}")
    out_p = os.path.join(OUTPUT_DIR, f"result_{jid}.mp4")
    with open(in_p, "wb") as f: f.write(await file.read())
    
    jobs[jid] = {"status": "processing", "progress": 0, "output_path": out_p, "stop_requested": False, "filename": file.filename}
    
    # We use a lambda to run the async function in background tasks
    background_tasks.add_task(lambda: asyncio.run(process_video_file(jid, in_p, out_p)))
    return {"job_id": jid}

@app.get("/video/jobs", tags=["Recorded Video Analysis"])
async def list_jobs():
    return [{"job_id": j, "status": d["status"], "progress": f"{d['progress']}%"} for j, d in jobs.items()]

@app.get("/video/status/{job_id}", tags=["Recorded Video Analysis"])
async def get_status(job_id: str):
    return jobs.get(job_id, {"error": "Not found"})

@app.get("/video/download/{job_id}", tags=["Recorded Video Analysis"])
async def download(job_id: str):
    if job_id in jobs and jobs[job_id]["status"] == "completed":
        return FileResponse(jobs[job_id]["output_path"])
    raise HTTPException(400, "File not ready")

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=7860)