app.py

import os
import sys
import subprocess
import logging
import warnings
import cv2
import gradio as gr
import torch
import numpy as np
from ultralytics import YOLO
import time
from simple_salesforce import Salesforce
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
from reportlab.lib.units import inch
from io import BytesIO
import base64
from retrying import retry
import uuid
from multiprocessing import Pool, cpu_count
from functools import partial
import tempfile
import shutil
import tenacity

# ========================== # Configuration and Setup # ==========================
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger(__name__)

def check_ffmpeg():
    try:
        subprocess.run(["ffmpeg", "-version"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=True)
        logger.info("FFmpeg is available.")
        return True
    except (subprocess.CalledProcessError, FileNotFoundError):
        logger.error("FFmpeg is not installed or not found in PATH. Video processing may fail.")
        return False

FFMPEG_AVAILABLE = check_ffmpeg()

# ========================== # ByteTrack Implementation # ==========================
class BYTETracker:
    def __init__(self, track_thresh=0.3, track_buffer=90, match_thresh=0.4, frame_rate=30):
        self.track_thresh = track_thresh
        self.track_buffer = track_buffer
        self.match_thresh = match_thresh  # Lowered to 0.4 to improve tracking sensitivity
        self.frame_rate = frame_rate
        self.next_id = 1
        self.tracks = {}
        self.worker_history = {}
        self.last_positions = {}

    def update(self, dets, scores, cls):
        tracks = []
        current_time = time.time()
        
        for i, (det, score, cl) in enumerate(zip(dets, scores, cls)):
            if score < self.track_thresh:
                continue
                
            x, y, w, h = det
            matched = False
            best_iou = 0
            best_track_id = None
            
            for track_id, track_info in self.tracks.items():
                if current_time - track_info['last_seen'] > self.track_buffer / self.frame_rate:
                    continue
                    
                tx, ty, tw, th = track_info['bbox']
                iou = self._calculate_iou([x, y, w, h], [tx, ty, tw, th])
                
                if iou > self.match_thresh and iou > best_iou:
                    best_iou = iou
                    best_track_id = track_id
                    matched = True
            
            if matched:
                self.tracks[best_track_id].update({
                    'bbox': [x, y, w, h],
                    'score': score,
                    'cls': cl,
                    'last_seen': current_time
                })
                if best_track_id not in self.worker_history:
                    self.worker_history[best_track_id] = []
                self.worker_history[best_track_id].append([x, y])
                self.last_positions[best_track_id] = [x, y]
                
                tracks.append({
                    'id': best_track_id,
                    'bbox': [x, y, w, h],
                    'score': score,
                    'cls': cl
                })
            else:
                same_worker = False
                for worker_id, last_pos in self.last_positions.items():
                    if self._is_same_worker([x, y], last_pos):
                        self.tracks[worker_id] = {
                            'bbox': [x, y, w, h],
                            'score': score,
                            'cls': cl,
                            'last_seen': current_time
                        }
                        tracks.append({
                            'id': worker_id,
                            'bbox': [x, y, w, h],
                            'score': score,
                            'cls': cl
                        })
                        same_worker = True
                        break
                
                if not same_worker:
                    self.tracks[self.next_id] = {
                        'bbox': [x, y, w, h],
                        'score': score,
                        'cls': cl,
                        'last_seen': current_time
                    }
                    self.worker_history[self.next_id] = [[x, y]]
                    self.last_positions[self.next_id] = [x, y]
                    tracks.append({
                        'id': self.next_id,
                        'bbox': [x, y, w, h],
                        'score': score,
                        'cls': cl
                    })
                    self.next_id += 1
        
        current_time = time.time()
        stale_ids = []
        for track_id, track_info in self.tracks.items():
            if current_time - track_info['last_seen'] > self.track_buffer / self.frame_rate:
                stale_ids.append(track_id)
        
        for track_id in stale_ids:
            del self.tracks[track_id]
            if track_id in self.worker_history:
                del self.worker_history[track_id]
            if track_id in self.last_positions:
                del self.last_positions[track_id]
            
        return tracks

    def _calculate_iou(self, box1, box2):
        x1, y1, w1, h1 = box1
        x2, y2, w2, h2 = box2
        x_left = max(x1 - w1/2, x2 - w2/2)
        y_top = max(y1 - h1/2, y2 - h2/2)
        x_right = min(x1 + w1/2, x2 + w2/2)
        y_bottom = min(y1 + h1/2, y2 + h2/2)
        if x_right < x_left or y_bottom < y_top:
            return 0.0
        intersection_area = (x_right - x_left) * (y_bottom - y_top)
        box1_area = w1 * h1
        box2_area = w2 * h2
        iou = intersection_area / (box1_area + box2_area - intersection_area)
        return iou
    
    def _is_same_worker(self, pos1, pos2, threshold=100):  # Reduced threshold for 384x384 frames
        x1, y1 = pos1
        x2, y2 = pos2
        distance = np.sqrt((x1 - x2)**2 + (y1 - y2)**2)
        return distance < threshold

# ========================== # Optimized Configuration # ==========================
CONFIG = {
    "MODEL_PATH": "yolov8_safety.pt",
    "FALLBACK_MODEL": "yolov8n.pt",
    "VIOLATION_LABELS": {
        0: "no_helmet",
        1: "no_harness",
        2: "unsafe_posture",
        3: "unsafe_zone",
        4: "improper_tool_use"
    },
    "CLASS_COLORS": {
        "no_helmet": (0, 0, 255),
        "no_harness": (0, 165, 255),
        "unsafe_posture": (0, 255, 0),
        "unsafe_zone": (255, 0, 0),
        "improper_tool_use": (255, 255, 0)
    },
    "DISPLAY_NAMES": {
        "no_helmet": "No Helmet Violation",
        "no_harness": "No Harness Violation",
        "unsafe_posture": "Unsafe Posture",
        "unsafe_zone": "Unsafe Zone Entry",
        "improper_tool_use": "Improper Tool Use"
    },
    "SF_CREDENTIALS": {
        "username": os.getenv("SF_USERNAME", "prashanth1ai@safety.com"),
        "password": os.getenv("SF_PASSWORD", "SaiPrash461"),
        "security_token": os.getenv("SF_SECURITY_TOKEN", "AP4AQnPoidIKPvSvNEfAHyoK"),
        "domain": "login"
    },
    "PUBLIC_URL_BASE": "https://huggingface.co/spaces/PrashanthB461/AI_Safety_Demo2/resolve/main/static/output/",
    "CONFIDENCE_THRESHOLDS": {  # Lowered thresholds to improve detection
        "no_helmet": 0.4,
        "no_harness": 0.25,
        "unsafe_posture": 0.25,
        "unsafe_zone": 0.25,
        "improper_tool_use": 0.25
    },
    "MIN_VIOLATION_FRAMES": 1,
    "VIOLATION_COOLDOWN": 30.0,
    "WORKER_TRACKING_DURATION": 5.0,
    "MAX_PROCESSING_TIME": 60,
    "FRAME_SKIP": 1,
    "BATCH_SIZE": 4,
    "PARALLEL_WORKERS": max(1, cpu_count() - 1),
    "TRACK_BUFFER": 90,
    "TRACK_THRESH": 0.3,
    "MATCH_THRESH": 0.4,
    "SNAPSHOT_QUALITY": 95,
    "MAX_WORKER_DISTANCE": 100,  # Adjusted to match BYTETracker threshold
    "TARGET_RESOLUTION": (384, 384)
}

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
logger.info(f"Using device: {device}")

def load_model():
    try:
        if os.path.isfile(CONFIG["MODEL_PATH"]):
            model_path = CONFIG["MODEL_PATH"]
            logger.info(f"Model loaded: {model_path}")
        else:
            model_path = CONFIG["FALLBACK_MODEL"]
            logger.warning("Using fallback model. Train yolov8_safety.pt for best results.")
            if not os.path.isfile(model_path):
                logger.info(f"Downloading fallback model: {model_path}")
                torch.hub.download_url_to_file('https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8n.pt', model_path)
                
        model = YOLO(model_path).to(device)
        if device.type == "cuda":
            model.model.half()
        logger.info(f"Model classes: {model.names}")
        return model
    except Exception as e:
        logger.error(f"Failed to load model: {e}")
        raise

model = load_model()

# ========================== # Helper Functions # ==========================
def preprocess_frame(frame):
    target_res = CONFIG["TARGET_RESOLUTION"]
    frame = cv2.resize(frame, target_res, interpolation=cv2.INTER_LINEAR)
    frame = cv2.convertScaleAbs(frame, alpha=1.2, beta=20)
    return frame

def draw_detections(frame, detections):
    result_frame = frame.copy()
    
    for det in detections:
        label = det.get("violation", "Unknown")
        confidence = det.get("confidence", 0.0)
        x, y, w, h = det.get("bounding_box", [0, 0, 0, 0])
        worker_id = det.get("worker_id", "Unknown")

        x1 = int(x - w/2)
        y1 = int(y - h/2)
        x2 = int(x + w/2)
        y2 = int(y + h/2)
        
        color = CONFIG["CLASS_COLORS"].get(label, (0, 0, 255))
        
        cv2.rectangle(result_frame, (x1, y1), (x2, y2), color, 3)
        
        display_text = f"{CONFIG['DISPLAY_NAMES'].get(label, label)} (Worker {worker_id})"
        text_size = cv2.getTextSize(display_text, cv2.FONT_HERSHEY_SIMPLEX, 0.6, 2)[0]
        cv2.rectangle(result_frame, (x1, y1-text_size[1]-10), (x1+text_size[0]+10, y1), (0, 0, 0), -1)
        cv2.putText(result_frame, display_text, (x1+5, y1-5), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)
        
        conf_text = f"Conf: {confidence:.2f}"
        cv2.putText(result_frame, conf_text, (x1+5, y2+20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
        
    return result_frame

def calculate_safety_score(violations):
    penalties = {
        "no_helmet": 25,
        "no_harness": 30,
        "unsafe_posture": 20,
        "unsafe_zone": 35,
        "improper_tool_use": 25
    }
    
    worker_violations = {}
    for v in violations:
        worker_id = v.get("worker_id", "Unknown")
        violation_type = v.get("violation", "Unknown")
        
        if worker_id not in worker_violations:
            worker_violations[worker_id] = set()
        worker_violations[worker_id].add(violation_type)
    
    total_penalty = 0
    for worker_violations_set in worker_violations.values():
        worker_penalty = sum(penalties.get(v, 0) for v in worker_violations_set)
        total_penalty += worker_penalty
    
    score = max(0, 100 - total_penalty)
    return score

def generate_violation_pdf(violations, score, output_dir):
    try:
        pdf_filename = f"violations_{int(time.time())}.pdf"
        pdf_path = os.path.join(output_dir, pdf_filename)
        pdf_file = BytesIO()
        c = canvas.Canvas(pdf_file, pagesize=letter)
        
        c.setFont("Helvetica-Bold", 16)
        c.drawString(1 * inch, 10 * inch, "Worksite Safety Violation Report")
        
        c.setFont("Helvetica", 12)
        c.drawString(1 * inch, 9.5 * inch, f"Date: {time.strftime('%Y-%m-%d')}")
        c.drawString(1 * inch, 9.2 * inch, f"Time: {time.strftime('%H:%M:%S')}")
        
        c.setFont("Helvetica-Bold", 14)
        c.drawString(1 * inch, 8.7 * inch, f"Safety Compliance Score: {score}%")

        y_position = 8.2 * inch
        c.setFont("Helvetica-Bold", 12)
        c.drawString(1 * inch, y_position, "Summary:")
        y_position -= 0.3 * inch
        
        worker_violations = {}
        for v in violations:
            worker_id = v.get("worker_id", "Unknown")
            if worker_id not in worker_violations:
                worker_violations[worker_id] = []
            worker_violations[worker_id].append(v)
        
        c.setFont("Helvetica", 10)
        summary_data = {
            "Total Workers with Violations": len(worker_violations),
            "Total Violations Found": len(violations),
            "Analysis Timestamp": time.strftime("%Y-%m-%d %H:%M:%S")
        }
        
        for key, value in summary_data.items():
            c.drawString(1 * inch, y_position, f"{key}: {value}")
            y_position -= 0.25 * inch

        y_position -= 0.5 * inch
        c.setFont("Helvetica-Bold", 12)
        c.drawString(1 * inch, y_position, "Violations by Worker:")
        y_position -= 0.3 * inch
        
        c.setFont("Helvetica", 10)
        for worker_id, worker_vios in worker_violations.items():
            c.drawString(1 * inch, y_position, f"Worker {worker_id}:")
            y_position -= 0.2 * inch
            
            for v in worker_vios:
                display_name = CONFIG["DISPLAY_NAMES"].get(v.get("violation", "Unknown"), "Unknown")
                time_str = f"{v.get('timestamp', 0.0):.2f}s"
                conf_str = f"{v.get('confidence', 0.0):.2f}"
                
                violation_text = f"  - {display_name} at {time_str} (Confidence: {conf_str})"
                c.drawString(1.2 * inch, y_position, violation_text)
                y_position -= 0.2 * inch
                
                if y_position < 1 * inch:
                    c.showPage()
                    c.setFont("Helvetica", 10)
                    y_position = 10 * inch

        c.save()
        pdf_file.seek(0)

        with open(pdf_path, "wb") as f:
            f.write(pdf_file.getvalue())
            
        public_url = f"{CONFIG['PUBLIC_URL_BASE']}{pdf_filename}"
        logger.info(f"PDF generated: {public_url}")
        return pdf_path, public_url, pdf_file
    except Exception as e:
        logger.error(f"Error generating PDF: {e}")
        return "", "", None

@retry(stop_max_attempt_number=3, wait_fixed=2000)
def connect_to_salesforce():
    try:
        sf = Salesforce(**CONFIG["SF_CREDENTIALS"])
        logger.info("Connected to Salesforce")
        sf.describe()
        return sf
    except Exception as e:
        logger.error(f"Salesforce connection failed: {e}")
        raise

def upload_pdf_to_salesforce(sf, pdf_file, report_id):
    try:
        if not pdf_file:
            logger.error("No PDF file provided for upload")
            return ""
            
        encoded_pdf = base64.b64encode(pdf_file.getvalue()).decode('utf-8')
        content_version_data = {
            "Title": f"Safety_Violation_Report_{int(time.time())}",
            "PathOnClient": f"safety_violation_{int(time.time())}.pdf",
            "VersionData": encoded_pdf,
            "FirstPublishLocationId": report_id
        }
        content_version = sf.ContentVersion.create(content_version_data)
        result = sf.query(f"SELECT Id, ContentDocumentId FROM ContentVersion WHERE Id = '{content_version['id']}'")
        
        if not result['records']:
            logger.error("Failed to retrieve ContentVersion")
            return ""
            
        file_url = f"https://{sf.sf_instance}/sfc/servlet.shepherd/version/download/{content_version['id']}"
        logger.info(f"PDF uploaded to Salesforce: {file_url}")
        return file_url
    except Exception as e:
        logger.error(f"Error uploading PDF to Salesforce: {e}")
        return ""

def push_report_to_salesforce(violations, score, pdf_path, pdf_file):
    try:
        sf = connect_to_salesforce()
        
        violations_text = ""
        for v in violations:
            display_name = CONFIG['DISPLAY_NAMES'].get(v.get('violation', 'Unknown'), 'Unknown')
            worker_id = v.get('worker_id', 'Unknown')
            timestamp = v.get('timestamp', 0.0)
            confidence = v.get('confidence', 0.0)
            
            violations_text += f"Worker {worker_id}: {display_name} at {timestamp:.2f}s (Conf: {confidence:.2f})\n"
        
        if not violations_text:
            violations_text = "No violations detected."
            
        pdf_url = f"{CONFIG['PUBLIC_URL_BASE']}{os.path.basename(pdf_path)}" if pdf_path else ""

        record_data = {
            "Compliance_Score__c": score,
            "Violations_Found__c": len(violations),
            "Violations_Details__c": violations_text,
            "Status__c": "Pending",
            "PDF_Report_URL__c": pdf_url
        }
        
        logger.info(f"Creating Salesforce record with data: {record_data}")
        
        try:
            record = sf.Safety_Video_Report__c.create(record_data)
            logger.info(f"Created Safety_Video_Report__c record: {record['id']}")
        except Exception as e:
            logger.error(f"Failed to create Safety_Video_Report__c: {e}")
            record = sf.Account.create({"Name": f"Safety_Report_{int(time.time())}"})
            logger.warning(f"Fell back to Account record: {record['id']}")
            
        record_id = record["id"]

        if pdf_file:
            uploaded_url = upload_pdf_to_salesforce(sf, pdf_file, record_id)
            if uploaded_url:
                try:
                    sf.Safety_Video_Report__c.update(record_id, {"PDF_Report_URL__c": uploaded_url})
                    logger.info(f"Updated record {record_id} with PDF URL: {uploaded_url}")
                except Exception as e:
                    logger.error(f"Failed to update Safety_Video_Report__c: {e}")
                    sf.Account.update(record_id, {"Description": uploaded_url})
                    logger.info(f"Updated Account record {record_id} with PDF URL")
                pdf_url = uploaded_url

        return record_id, pdf_url
    except Exception as e:
        logger.error(f"Salesforce record creation failed: {e}")
        return "N/A", "Salesforce integration failed."

@tenacity.retry(
    stop=tenacity.stop_after_attempt(3),
    wait=tenacity.wait_fixed(1),
    retry=tenacity.retry_if_exception_type((IOError, OSError)),
    before_sleep=lambda retry_state: logger.info(f"Retrying file access (attempt {retry_state.attempt_number}/3)...")
)
def verify_and_open_video(video_path):
    if not os.path.exists(video_path):
        raise FileNotFoundError(f"Temporary video file not found: {video_path}")
    
    file_size = os.path.getsize(video_path)
    if file_size == 0:
        raise ValueError(f"Temporary video file is empty: {video_path}")
    
    with open(video_path, "rb") as f:
        f.read(1)
    
    cap = cv2.VideoCapture(video_path)
    if not cap.isOpened():
        raise ValueError("Could not open video file. Ensure the video format is supported (e.g., MP4) and FFmpeg is installed.")
    
    return cap

def process_video(video_data, temp_dir):
    video_path = None
    output_dir = os.path.join(temp_dir, "output")
    os.makedirs(output_dir, exist_ok=True)
    os.environ['YOLO_CONFIG_DIR'] = temp_dir
    
    try:
        if not video_data:
            raise ValueError("Empty video data provided.")
        
        logger.info(f"Received video data size: {len(video_data)} bytes")
        if len(video_data) == 0:
            raise ValueError("Video data is empty.")

        with tempfile.NamedTemporaryFile(suffix=".mp4", dir=temp_dir, delete=False) as temp_file:
            temp_file.write(video_data)
            temp_file.flush()
            video_path = temp_file.name
        logger.info(f"Video saved to temporary file: {video_path}")

        if not os.path.exists(video_path):
            raise FileNotFoundError(f"Temporary video file not found: {video_path}")
        file_size = os.path.getsize(video_path)
        if file_size == 0:
            raise ValueError(f"Temporary video file is empty: {video_path}")
        logger.info(f"Temporary video file size: {file_size} bytes")

        cap = verify_and_open_video(video_path)
        logger.info(f"Successfully opened video file: {video_path}")

        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
        fps = cap.get(cv2.CAP_PROP_FPS) or 30
        duration = total_frames / fps
        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
        logger.info(f"Video properties: {duration:.2f}s, {total_frames} frames, {fps:.1f} FPS, {width}x{height}")

        if total_frames <= 0:
            raise ValueError("Video has no frames.")

        tracker = BYTETracker(
            track_thresh=CONFIG["TRACK_THRESH"],
            track_buffer=CONFIG["TRACK_BUFFER"],
            match_thresh=CONFIG["MATCH_THRESH"],
            frame_rate=fps
        )

        worker_id_mapping = {}
        unique_violations = {}
        violation_frames = {}
        start_time = time.time()
        frame_skip = CONFIG["FRAME_SKIP"]
        processed_frames = 0
        last_yield_time = start_time
        worker_counter = 1  # For assigning unique worker IDs

        while processed_frames < total_frames:
            batch_frames = []
            batch_indices = []
            
            for _ in range(CONFIG["BATCH_SIZE"]):
                frame_idx = int(cap.get(cv2.CAP_PROP_POS_FRAMES))
                if frame_idx >= total_frames:
                    break
                
                ret, frame = cap.read()
                if not ret:
                    logger.warning(f"Failed to read frame {frame_idx}. Skipping.")
                    break
                
                frame = preprocess_frame(frame)
                
                for _ in range(frame_skip - 1):
                    if not cap.grab():
                        break
                
                batch_frames.append(frame)
                batch_indices.append(frame_idx)
                processed_frames += 1

            if not batch_frames:
                logger.info("No more frames to process.")
                break

            try:
                batch_frames_np = np.array(batch_frames)
                batch_frames_tensor = torch.from_numpy(batch_frames_np).permute(0, 3, 1, 2).float() / 255.0
                batch_frames_tensor = batch_frames_tensor.to(device)
                if device.type == "cuda":
                    batch_frames_tensor = batch_frames_tensor.half()

                results = model(batch_frames_tensor, device=device, conf=0.1, verbose=False)
            except Exception as e:
                logger.error(f"Model inference failed: {e}")
                raise ValueError(f"Failed to process video frames with YOLO model: {str(e)}")
            finally:
                batch_frames = []
                if device.type == "cuda":
                    torch.cuda.empty_cache()

            current_time = time.time()
            if current_time - last_yield_time > 0.1:
                progress = (processed_frames / total_frames) * 100
                elapsed_time = current_time - start_time
                fps_processed = processed_frames / elapsed_time if elapsed_time > 0 else 0
                yield f"Processing video... {progress:.1f}% complete (Frame {processed_frames}/{total_frames}, {fps_processed:.1f} FPS)", "", "", "", ""
                last_yield_time = current_time

            for i, (result, frame_idx) in enumerate(zip(results, batch_indices)):
                current_time = frame_idx / fps
                
                boxes = result.boxes
                track_inputs = []
                
                for box in boxes:
                    cls = int(box.cls)
                    conf = float(box.conf)
                    label = CONFIG["VIOLATION_LABELS"].get(cls, None)
                    
                    if label is None:
                        continue
                        
                    if conf < CONFIG["CONFIDENCE_THRESHOLDS"].get(label, 0.25):
                        continue

                    bbox = box.xywh.cpu().numpy()[0]
                    track_inputs.append({
                        "bbox": bbox,
                        "conf": conf,
                        "cls": cls
                    })

                if not track_inputs:
                    continue
                    
                tracked_objects = tracker.update(
                    np.array([t["bbox"] for t in track_inputs]),
                    np.array([t["conf"] for t in track_inputs]),
                    np.array([t["cls"] for t in track_inputs])
                )
                logger.info(f"Frame {frame_idx}: Detected {len(tracked_objects)} workers")

                for obj in tracked_objects:
                    tracker_id = obj['id']
                    label = CONFIG["VIOLATION_LABELS"].get(int(obj['cls']), None)
                    conf = obj['score']
                    bbox = obj['bbox']
                    
                    if label is None:
                        continue
                    
                    if tracker_id not in worker_id_mapping:
                        worker_id_mapping[tracker_id] = worker_counter
                        worker_counter += 1
                    
                    worker_id = worker_id_mapping[tracker_id]
                    
                    violation_key = (worker_id, label)
                    
                    if violation_key not in unique_violations:
                        unique_violations[violation_key] = current_time
                        violation_frames[violation_key] = frame_idx

        cap.release()
        processing_time = time.time() - start_time
        logger.info(f"Processing complete in {processing_time:.2f}s")
        logger.info(f"Total unique workers detected: {len(set(worker_id_mapping.values()))}")

        violations = []
        for (worker_id, label), detection_time in unique_violations.items():
            violations.append({
                "worker_id": worker_id,
                "violation": label,
                "timestamp": detection_time,
                "confidence": 0.0,
                "frame_idx": violation_frames[(worker_id, label)]
            })

        if not violations:
            logger.info("No violations detected after processing")
            yield "No violations detected in the video.", "Safety Score: 100%", "No snapshots captured.", "N/A", "N/A"
            return

        snapshots = []
        cap = cv2.VideoCapture(video_path)
        for violation in violations:
            frame_idx = violation["frame_idx"]
            cap.set(cv2.CAP_PROP_POS_FRAMES, frame_idx)
            ret, frame = cap.read()
            if not ret:
                logger.warning(f"Failed to read frame {frame_idx} for snapshot.")
                continue

            frame = preprocess_frame(frame)
            frame_tensor = torch.from_numpy(frame).permute(2, 0, 1).float() / 255.0
            frame_tensor = frame_tensor.unsqueeze(0).to(device)
            if device.type == "cuda":
                frame_tensor = frame_tensor.half()

            result = model(frame_tensor, device=device, conf=0.1, verbose=False)[0]
            boxes = result.boxes

            for box in boxes:
                cls = int(box.cls)
                conf = float(box.conf)
                label = CONFIG["VIOLATION_LABELS"].get(cls, None)
                if label == violation["violation"]:
                    violation["confidence"] = round(conf, 2)
                    bbox = box.xywh.cpu().numpy()[0]
                    detection = {
                        "worker_id": violation["worker_id"],
                        "violation": label,
                        "confidence": violation["confidence"],
                        "bounding_box": bbox,
                        "timestamp": violation["timestamp"]
                    }
                    snapshot_frame = frame.copy()
                    snapshot_frame = draw_detections(snapshot_frame, [detection])
                    cv2.putText(
                        snapshot_frame,
                        f"Time: {violation['timestamp']:.2f}s",
                        (10, 30),
                        cv2.FONT_HERSHEY_SIMPLEX,
                        0.7,
                        (255, 255, 255),
                        2
                    )
                    snapshot_filename = f"violation_{label}_worker{violation['worker_id']}_{int(violation['timestamp']*100)}.jpg"
                    snapshot_path = os.path.join(output_dir, snapshot_filename)
                    cv2.imwrite(
                        snapshot_path,
                        snapshot_frame,
                        [cv2.IMWRITE_JPEG_QUALITY, CONFIG["SNAPSHOT_QUALITY"]]
                    )
                    snapshots.append({
                        "violation": label,
                        "worker_id": violation["worker_id"],
                        "timestamp": violation["timestamp"],
                        "snapshot_path": snapshot_path,
                        "snapshot_url": f"{CONFIG['PUBLIC_URL_BASE']}{snapshot_filename}",
                        "confidence": violation["confidence"]
                    })
                    logger.info(f"Captured snapshot for {label} violation by worker {violation['worker_id']} at {violation['timestamp']:.2f}s")
                    break

        cap.release()

        score = calculate_safety_score(violations)
        pdf_path, pdf_url, pdf_file = generate_violation_pdf(violations, score, output_dir)
        
        record_id, final_pdf_url = push_report_to_salesforce(violations, score, pdf_path, pdf_file)

        violation_table = "| Violation | Worker ID | Time (s) | Confidence |\n"
        violation_table += "|-----------|-----------|----------|------------|\n"
        
        for v in sorted(violations, key=lambda x: (x.get("worker_id", "Unknown"), x.get("timestamp", 0.0))):
            display_name = CONFIG["DISPLAY_NAMES"].get(v.get("violation", "Unknown"), "Unknown")
            worker_id = v.get("worker_id", "Unknown")
            timestamp = v.get("timestamp", 0.0)
            confidence = v.get("confidence", 0.0)
            violation_table += f"| {display_name} | {worker_id} | {timestamp:.2f} | {confidence:.2f} |\n"

        snapshots_text = ""
        for s in snapshots:
            display_name = CONFIG["DISPLAY_NAMES"].get(s["violation"], "Unknown")
            worker_id = s.get("worker_id", "Unknown")
            timestamp = s.get("timestamp", 0.0)
            snapshots_text += f"### {display_name} - Worker {worker_id} at {timestamp:.2f}s\n\n"
            snapshots_text += f"![Violation]({s['snapshot_url']})\n\n"

        if not snapshots_text:
            snapshots_text = "No snapshots captured."

        yield (
            violation_table,
            f"Safety Score: {score}%",
            snapshots_text,
            f"Salesforce Record ID: {record_id}",
            final_pdf_url
        )

    except Exception as e:
        logger.error(f"Error processing video: {str(e)}", exc_info=True)
        yield f"Error processing video: {str(e)}", "", "", "", ""
    finally:
        if video_path and os.path.exists(video_path):
            try:
                os.remove(video_path)
                logger.info(f"Cleaned up temporary video file: {video_path}")
            except Exception as e:
                logger.error(f"Failed to clean up temporary video file {video_path}: {e}")
        if device.type == "cuda":
            torch.cuda.empty_cache()

def gradio_interface(video_file):
    temp_dir = None
    local_video_path = None
    try:
        if not video_file:
            return "No file uploaded.", "", "No file uploaded.", "", ""
        
        temp_dir = tempfile.mkdtemp(prefix="Ultralytics_")
        logger.info(f"Created temporary directory for video processing: {temp_dir}")

        with open(video_file, "rb") as f:
            video_data = f.read()
        logger.info(f"Read Gradio video file: {video_file}, size: {len(video_data)} bytes")
        
        if len(video_data) == 0:
            return "Uploaded video file is empty.", "", "", "", ""

        with tempfile.NamedTemporaryFile(suffix=".mp4", dir=temp_dir, delete=False) as temp_file:
            temp_file.write(video_data)
            temp_file.flush()
            local_video_path = temp_file.name
        logger.info(f"Copied Gradio video to local temporary file: {local_video_path}")

        if not FFMPEG_AVAILABLE:
            return "FFmpeg is not available in the environment. Please install FFmpeg to process videos.", "", "", "", ""

        for status, score, snapshots_text, record_id, details_url in process_video(video_data, temp_dir):
            yield status, score, snapshots_text, record_id, details_url
            
    except Exception as e:
        logger.error(f"Error in Gradio interface: {e}", exc_info=True)
        yield f"Error: {str(e)}", "", "Error in processing.", "", ""
    finally:
        if local_video_path and os.path.exists(local_video_path):
            try:
                os.remove(local_video_path)
                logger.info(f"Cleaned up local temporary video file: {local_video_path}")
            except Exception as e:
                logger.error(f"Failed to clean up local temporary video file {local_video_path}: {e}")
        
        if temp_dir and os.path.exists(temp_dir):
            shutil.rmtree(temp_dir, ignore_errors=True)
            logger.info(f"Cleaned up temporary directory: {temp_dir}")
        if device.type == "cuda":
            torch.cuda.empty_cache()

# ========================== # Gradio Interface # ==========================
interface = gr.Interface(
    fn=gradio_interface,
    inputs=gr.Video(label="Upload Site Video"),
    outputs=[
        gr.Markdown(label="Detected Safety Violations"),
        gr.Textbox(label="Compliance Score"),
        gr.Markdown(label="Snapshots"),
        gr.Textbox(label="Salesforce Record ID"),
        gr.Textbox(label="Violation Details URL")
    ],
    title="Worksite Safety Violation Analyzer",
    description="Upload site videos to detect safety violations (No Helmet, No Harness, Unsafe Posture, Unsafe Zone, Improper Tool Use). Each unique violation is detected only once per worker.",
    allow_flagging="never"
)

if __name__ == "__main__":
    logger.info("Launching Enhanced Safety Analyzer App...")
    interface.launch()