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 transformers import DetrImageProcessor, DetrForObjectDetection
from PIL import Image
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
from scipy.spatial import distance

# ========================== # 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()

# ========================== # BYTETracker Implementation # ==========================
class BYTETracker:
    def __init__(self, track_thresh=0.3, track_buffer=90, match_thresh=0.5, frame_rate=30):
        self.track_thresh = track_thresh
        self.track_buffer = track_buffer
        self.match_thresh = match_thresh
        self.frame_rate = frame_rate
        self.next_id = 1
        self.tracks = {}
        self.worker_history = {}
        self.last_positions = {}
        self.recently_removed = {}
        self.helmet_status = {}
        self.harness_status = {}

    def update(self, dets, scores, cls):
        tracks = []
        current_time = time.time()
        
        # Prune stale tracks
        stale_ids = [track_id for track_id, track_info in self.tracks.items()
                     if current_time - track_info['last_seen'] > self.track_buffer / self.frame_rate]
        for track_id in stale_ids:
            self.recently_removed[track_id] = {
                'bbox': self.tracks[track_id]['bbox'],
                'last_seen': current_time,
                'last_position': self.last_positions.get(track_id, [0, 0])
            }
            del self.tracks[track_id]
            self.worker_history.pop(track_id, None)
            self.last_positions.pop(track_id, None)

        # Clean up recently_removed tracks older than 1 second
        to_remove = [track_id for track_id, info in self.recently_removed.items()
                     if current_time - info['last_seen'] > 1.0]
        for track_id in to_remove:
            del self.recently_removed[track_id]

        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():
                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 cl == "no_helmet" and score > CONFIG["CONFIDENCE_THRESHOLDS"]["no_helmet"]:
                    self.helmet_status[best_track_id] = True
                elif cl == "no_harness" and score > CONFIG["CONFIDENCE_THRESHOLDS"]["no_harness"]:
                    self.harness_status[best_track_id] = True
                
                self.worker_history[best_track_id] = self.worker_history.get(best_track_id, []) + [[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:
                reidentified = False
                for track_id, info in list(self.recently_removed.items()):
                    if self._is_same_worker([x, y], info['last_position'], threshold=CONFIG["MAX_WORKER_DISTANCE"]):
                        self.tracks[track_id] = {
                            'bbox': [x, y, w, h],
                            'score': score,
                            'cls': cl,
                            'last_seen': current_time
                        }
                        self.worker_history[track_id] = [[x, y]]
                        self.last_positions[track_id] = [x, y]
                        
                        if cl == "no_helmet" and score > CONFIG["CONFIDENCE_THRESHOLDS"]["no_helmet"]:
                            self.helmet_status[track_id] = True
                        elif cl == "no_harness" and score > CONFIG["CONFIDENCE_THRESHOLDS"]["no_harness"]:
                            self.harness_status[track_id] = True
                        
                        tracks.append({
                            'id': track_id,
                            'bbox': [x, y, w, h],
                            'score': score,
                            'cls': cl
                        })
                        reidentified = True
                        del self.recently_removed[track_id]
                        break
                
                if not reidentified:
                    same_worker = False
                    for worker_id, last_pos in self.last_positions.items():
                        if self._is_same_worker([x, y], last_pos, threshold=CONFIG["MAX_WORKER_DISTANCE"]):
                            self.tracks[worker_id] = {
                                'bbox': [x, y, w, h],
                                'score': score,
                                'cls': cl,
                                'last_seen': current_time
                            }
                            
                            if cl == "no_helmet" and score > CONFIG["CONFIDENCE_THRESHOLDS"]["no_helmet"]:
                                self.helmet_status[worker_id] = True
                            elif cl == "no_harness" and score > CONFIG["CONFIDENCE_THRESHOLDS"]["no_harness"]:
                                self.harness_status[worker_id] = True
                            
                            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]
                        
                        if cl == "no_helmet" and score > CONFIG["CONFIDENCE_THRESHOLDS"]["no_helmet"]:
                            self.helmet_status[self.next_id] = True
                        elif cl == "no_harness" and score > CONFIG["CONFIDENCE_THRESHOLDS"]["no_harness"]:
                            self.harness_status[self.next_id] = True
                        
                        tracks.append({
                            'id': self.next_id,
                            'bbox': [x, y, w, h],
                            'score': score,
                            'cls': cl
                        })
                        self.next_id += 1
        
        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=150):
        x1, y1 = pos1
        x2, y2 = pos2
        return np.sqrt((x1 - x2)**2 + (y1 - y2)**2) < threshold

    def validate_helmet_violation(self, worker_id, current_confidence):
        return worker_id in self.helmet_status and self.helmet_status[worker_id]

    def validate_harness_violation(self, worker_id, current_confidence):
        return worker_id in self.harness_status and self.harness_status[worker_id]

# ========================== # Optimized Configuration # ==========================
CONFIG = {
    "MODEL_NAME": "facebook/detr-resnet-50",
    "VIOLATION_LABELS": {
        "no_helmet": "No Helmet",
        "no_harness": "No Harness",
        "unsafe_posture": "Unsafe Posture",
        "unsafe_zone": "Unsafe Zone",
        "improper_tool_use": "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": {
        "no_helmet": 0.45,
        "no_harness": 0.25,
        "unsafe_posture": 0.25,
        "unsafe_zone": 0.25,
        "improper_tool_use": 0.25
    },
    "MIN_VIOLATION_FRAMES": 2,
    "VIOLATION_COOLDOWN": 30.0,
    "WORKER_TRACKING_DURATION": 10.0,
    "MAX_PROCESSING_TIME": 120,  # Increased to allow more time for CPU processing
    "FRAME_SKIP": 4,
    "BATCH_SIZE": 2,  # Reduced for better CPU performance
    "PARALLEL_WORKERS": max(1, cpu_count() - 1),
    "TRACK_BUFFER": 150,
    "TRACK_THRESH": 0.3,
    "MATCH_THRESH": 0.5,
    "SNAPSHOT_QUALITY": 95,
    "MAX_WORKER_DISTANCE": 150,
    "TARGET_RESOLUTION": (256, 256),  # Further reduced for faster inference
    "HELMET_VALIDATION_FRAMES": 3
}

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
logger.info(f"Using device: {device}")
if device.type == "cpu":
    logger.warning("Running on CPU, which may lead to slower processing. Consider using a GPU for better performance.")

def load_model():
    try:
        import timm
        logger.info("timm library is available.")
    except ImportError as e:
        logger.error("timm library is not installed. Install it with: pip install timm")
        raise ImportError("timm is required for DetrConvEncoder. Run `pip install timm` and restart your runtime.") from e

    try:
        processor = DetrImageProcessor.from_pretrained(CONFIG["MODEL_NAME"])
        model = DetrForObjectDetection.from_pretrained(CONFIG["MODEL_NAME"]).to(device)
        if device.type == "cuda":
            model = model.half()
        logger.info(f"Loaded DETR model: {CONFIG['MODEL_NAME']}")
        logger.info(f"Model classes: {model.config.id2label}")
        return processor, model
    except Exception as e:
        logger.error(f"Failed to load model: {str(e)}")
        raise

processor, 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=1.3, beta=20)
    # Removed cv2.filter2D to reduce processing time
    return frame

def is_unsafe_posture(box, frame_shape):
    x1, y1, x2, y2 = box
    height = y2 - y1
    width = x2 - x1
    aspect_ratio = height / max(width, 1)
    return aspect_ratio > 2.0

def is_improper_tool_use(person_box, tool_box):
    person_center = ((person_box[0] + person_box[2]) / 2, (person_box[1] + person_box[3]) / 2)
    tool_center = ((tool_box[0] + tool_box[2]) / 2, (tool_box[1] + tool_box[3]) / 2)
    dist = distance.euclidean(person_center, tool_center)
    return dist > 100

def is_unsafe_zone(person_box, frame_shape):
    px, py, pw, ph = person_box
    frame_h, frame_w = frame_shape
    person_center = (px + pw / 2, py + ph / 2)
    unsafe_zone = (0, 0, 0.5, 0.5)
    return (unsafe_zone[0] * frame_w < person_center[0] < unsafe_zone[2] * frame_w and
            unsafe_zone[1] * frame_h < person_center[1] < unsafe_zone[3] * frame_h)

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))
        line_thickness = 4 if label == "no_helmet" else 3
        cv2.rectangle(result_frame, (x1, y1), (x2, y2), color, line_thickness)
        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 = sum(sum(penalties.get(v, 0) for v in worker_violations[wid]) for wid in worker_violations)
    return max(0, 100 - total_penalty)

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 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": 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_Violation_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 validate_helmet_detection(frame, bbox, confidence_threshold=0.45):
    x, y, w, h = bbox
    x1 = int(max(0, x - w/2))
    y1 = int(max(0, y - h/2))
    x2 = int(min(frame.shape[1], x + w/2))
    y2 = int(min(frame.shape[0], y + h/2))
    head_region = frame[y1:y2, x1:x2]
    if head_region.size == 0:
        return False
    hsv = cv2.cvtColor(head_region, cv2.COLOR_BGR2HSV)
    yellow_lower = np.array([20, 100, 100])
    yellow_upper = np.array([30, 255, 255])
    yellow_mask = cv2.inRange(hsv, yellow_lower, yellow_upper)
    white_lower = np.array([0, 0, 200])
    white_upper = np.array([180, 30, 255])
    white_mask = cv2.inRange(hsv, white_lower, white_upper)
    orange_lower = np.array([5, 100, 100])
    orange_upper = np.array([15, 255, 255])
    orange_mask = cv2.inRange(hsv, orange_lower, orange_upper)
    blue_lower = np.array([100, 100, 100])
    blue_upper = np.array([130, 255, 255])
    blue_mask = cv2.inRange(hsv, blue_lower, blue_upper)
    helmet_mask = cv2.bitwise_or(yellow_mask, white_mask)
    helmet_mask = cv2.bitwise_or(helmet_mask, orange_mask)
    helmet_mask = cv2.bitwise_or(helmet_mask, blue_mask)
    helmet_percentage = np.sum(helmet_mask > 0) / (head_region.shape[0] * head_region.shape[1])
    if helmet_percentage > 0.25:
        return False
    gray = cv2.cvtColor(head_region, cv2.COLOR_BGR2GRAY)
    texture_score = np.std(gray)
    if texture_score < 15:
        return False
    edges = cv2.Canny(gray, 50, 150)
    edge_density = np.sum(edges > 0) / (head_region.shape[0] * head_region.shape[1])
    if edge_density > 0.15:
        return True
    if confidence_threshold >= 0.6:
        return True
    return True

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)
    
    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}")

        cap = verify_and_open_video(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 = {}
        helmet_detections = {}
        frame_detections = {}
        start_time = time.time()
        frame_skip = CONFIG["FRAME_SKIP"]
        processed_frames = 0  # Track actual frames processed
        frames_read = 0  # Track frames read from video
        last_yield_time = start_time
        worker_counter = 1

        while True:
            batch_frames = []
            batch_indices = []
            batch_originals = []
            
            for _ in range(CONFIG["BATCH_SIZE"]):
                frame_idx = int(cap.get(cv2.CAP_PROP_POS_FRAMES))
                frames_read = frame_idx
                if frame_idx >= total_frames:
                    logger.info("Reached end of video.")
                    break
                ret, frame = cap.read()
                if not ret:
                    logger.warning(f"Failed to read frame {frame_idx}. Assuming end of video.")
                    frames_read = total_frames  # Assume we've reached the end
                    break
                original_frame = frame.copy()
                frame = preprocess_frame(frame)
                for _ in range(frame_skip - 1):
                    if not cap.grab():
                        logger.warning(f"Failed to grab frame after {frame_idx}. Assuming end of video.")
                        frames_read = total_frames
                        break
                    frames_read += 1
                batch_frames.append(Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)))
                batch_indices.append(frame_idx)
                batch_originals.append(original_frame)
                processed_frames += 1  # Increment for each frame actually processed

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

            # Check for timeout
            elapsed_time = time.time() - start_time
            if elapsed_time > CONFIG["MAX_PROCESSING_TIME"]:
                logger.warning(f"Processing exceeded time limit of {CONFIG['MAX_PROCESSING_TIME']}s. Terminating early.")
                break

            try:
                inputs = processor(images=batch_frames, return_tensors="pt").to(device)
                if device.type == "cuda":
                    inputs = {k: v.half() for k, v in inputs.items()}
                with torch.no_grad():
                    outputs = model(**inputs)
                target_sizes = torch.tensor([frame.size[::-1] for frame in batch_frames]).to(device)
                results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.1)
            except Exception as e:
                logger.error(f"Model inference failed: {e}")
                raise ValueError(f"Failed to process video frames with DETR 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 = (frames_read / total_frames) * 100
                progress = min(progress, 100.0)  # Ensure progress doesn't exceed 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 {frames_read}/{total_frames}, {fps_processed:.1f} FPS)", "", "", "", "", f"Elapsed: {elapsed_time:.1f}s"
                last_yield_time = current_time

            for i, (result, frame_idx, original_frame) in enumerate(zip(results, batch_indices, batch_originals)):
                current_time = frame_idx / fps
                track_inputs = []
                person_boxes = []
                tool_boxes = []

                frame_detections[frame_idx] = []

                for score, label, box in zip(result["scores"], result["labels"], result["boxes"]):
                    label_name = model.config.id2label[label.item()]
                    conf = float(score)
                    bbox = box.cpu().numpy()
                    x, y, x2, y2 = bbox
                    w, h = x2 - x, y2 - y
                    bbox_xywh = [x + w/2, y + h/2, w, h]

                    if label_name in ["no_helmet", "no_harness"] and conf >= CONFIG["CONFIDENCE_THRESHOLDS"].get(label_name, 0.25):
                        if label_name == "no_helmet" and not validate_helmet_detection(original_frame, bbox_xywh, conf):
                            logger.info(f"Frame {frame_idx}: Helmet false positive filtered at {conf:.2f} confidence")
                            continue
                        track_inputs.append({"bbox": bbox_xywh, "conf": conf, "cls": label_name})
                        frame_detections[frame_idx].append({"label": label_name, "conf": conf, "bbox": bbox_xywh})
                    elif label_name == "person":
                        person_boxes.append(bbox_xywh)
                    elif label_name in ["hammer", "wrench"]:
                        tool_boxes.append(bbox_xywh)

                for pbox in person_boxes:
                    if is_unsafe_posture(pbox, original_frame.shape[:2]):
                        track_inputs.append({"bbox": pbox, "conf": 0.9, "cls": "unsafe_posture"})
                        frame_detections[frame_idx].append({"label": "unsafe_posture", "conf": 0.9, "bbox": pbox})
                    if is_unsafe_zone(pbox, original_frame.shape[:2]):
                        track_inputs.append({"bbox": pbox, "conf": 0.9, "cls": "unsafe_zone"})
                        frame_detections[frame_idx].append({"label": "unsafe_zone", "conf": 0.9, "bbox": pbox})
                    for tbox in tool_boxes:
                        if is_improper_tool_use(pbox, tbox):
                            track_inputs.append({"bbox": pbox, "conf": 0.9, "cls": "improper_tool_use"})
                            frame_detections[frame_idx].append({"label": "improper_tool_use", "conf": 0.9, "bbox": pbox})

                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 = obj['cls']
                    conf = obj['score']
                    bbox = obj['bbox']
                    
                    if label not in CONFIG["VIOLATION_LABELS"]:
                        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]
                    
                    if label == "no_helmet":
                        if worker_id not in helmet_detections:
                            helmet_detections[worker_id] = []
                        helmet_detections[worker_id].append({
                            "frame_idx": frame_idx,
                            "confidence": conf,
                            "bbox": bbox
                        })
                        if len(helmet_detections[worker_id]) >= CONFIG["HELMET_VALIDATION_FRAMES"]:
                            avg_conf = sum(d["confidence"] for d in helmet_detections[worker_id]) / len(helmet_detections[worker_id])
                            if avg_conf >= CONFIG["CONFIDENCE_THRESHOLDS"]["no_helmet"]:
                                violation_key = (worker_id, label)
                                if violation_key not in unique_violations:
                                    unique_violations[violation_key] = current_time
                                    violation_frames[violation_key] = frame_idx
                                    logger.info(f"Frame {frame_idx}: Valid helmet violation for worker {worker_id} with avg conf {avg_conf:.2f}")
                    else:
                        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()))}")

        # Ensure final progress update
        final_progress = (frames_read / total_frames) * 100
        final_progress = min(final_progress, 100.0)
        yield f"Processing video... {final_progress:.1f}% complete (Frame {frames_read}/{total_frames})", "", "", "", "", f"Elapsed: {processing_time:.1f}s"

        violations = []
        for (worker_id, label), detection_time in unique_violations.items():
            frame_idx = violation_frames[(worker_id, label)]
            conf = next((d["conf"] for d in frame_detections.get(frame_idx, []) if d["label"] == label), 0.0)
            violations.append({
                "worker_id": worker_id,
                "violation": label,
                "timestamp": detection_time,
                "confidence": conf,
                "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", f"Completed in {processing_time:.1f}s"
            return

        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"
        yield violation_table, "", "", "", "", f"Violations detected in {processing_time:.1f}s"

        snapshots = []
        cap = cv2.VideoCapture(video_path)
        for violation in violations:
            try:
                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)
                detections = frame_detections.get(frame_idx, [])
                for det in detections:
                    if det["label"] == violation["violation"]:
                        violation["confidence"] = round(det["conf"], 2)
                        detection = {
                            "worker_id": violation["worker_id"],
                            "violation": det["label"],
                            "confidence": violation["confidence"],
                            "bounding_box": det["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_{det['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": det["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 {det['label']} violation by worker {violation['worker_id']} at {violation['timestamp']:.2f}s")
                        break
            except Exception as e:
                logger.error(f"Error generating snapshot for violation: {e}")
                continue

        cap.release()

        score = calculate_safety_score(violations)
        pdf_path, pdf_url, pdf_file = "", "", None
        try:
            pdf_path, pdf_url, pdf_file = generate_violation_pdf(violations, score, output_dir)
        except Exception as e:
            logger.error(f"PDF generation failed: {e}")
            yield violation_table, f"Safety Score: {score}%", "Failed to generate snapshots due to PDF error.", "N/A", "N/A", f"Completed in {processing_time:.1f}s\nError: {str(e)}"
            return

        record_id, final_pdf_url = "N/A", "Salesforce integration failed."
        try:
            record_id, final_pdf_url = push_report_to_salesforce(violations, score, pdf_path, pdf_file)
        except Exception as e:
            logger.error(f"Salesforce integration failed: {e}")

        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,
            f"Completed in {processing_time:.1f}s"
        )

    except Exception as e:
        logger.error(f"Error processing video: {str(e)}", exc_info=True)
        yield f"Error processing video: {str(e)}", "", "", "", "", f"Failed after {time.time() - start_time:.1f}s"
    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="DETR_")
        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, log in process_video(video_data, temp_dir):
            yield status, score, snapshots_text, record_id, details_url, log
            
    except Exception as e:
        logger.error(f"Error in Gradio interface: {e}", exc_info=True)
        yield f"Error: {str(e)}", "", "Error in processing.", "", "", str(e)
    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"),
        gr.Textbox(label="Processing Log")
    ],
    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 with DETR...")
    interface.launch()