app.py

# backend.py

import os
import cv2
import numpy as np
import tensorflow as tf
import smtplib

from fastapi import FastAPI, File, UploadFile, Form, HTTPException
from fastapi.responses import JSONResponse, StreamingResponse
from fastapi.middleware.cors import CORSMiddleware

from typing import Dict, Any
from datetime import datetime, timezone
from io import BytesIO

# SQLAlchemy imports
from sqlalchemy import create_engine, Column, Integer, String, DateTime, ForeignKey, Text, func
from sqlalchemy.orm import sessionmaker, relationship, declarative_base, Session

# ReportLab (PDF generation)
from reportlab.lib.pagesizes import A4
from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Image as RLImage, Table, TableStyle
from reportlab.lib.styles import getSampleStyleSheet
from reportlab.lib import colors

# Matplotlib (Chart generation)
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt

# YOLO-related imports
from src.yolo3.model import yolo_body
from src.yolo3.detect import detection
from src.utils.image import letterbox_image
from src.utils.fixes import fix_tf_gpu
from tensorflow.keras.layers import Input


##############################################################################
#                           Database Setup (SQLite)
##############################################################################

DB_URL = "sqlite:///./safety_monitor.db"

engine = create_engine(
    DB_URL, connect_args={"check_same_thread": False}  # for single-threaded SQLite
)
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
Base = declarative_base()

class Upload(Base):
    """
    Stores information about each upload (image or video), plus the user's email.
    """
    __tablename__ = "uploads"

    id = Column(Integer, primary_key=True, index=True)
    filename = Column(String)
    filepath = Column(String)
    timestamp = Column(DateTime)
    approach = Column(Integer)
    user_email = Column(String)  # The user’s email address
    total_workers = Column(Integer, default=0)
    total_helmets = Column(Integer, default=0)
    total_vests = Column(Integer, default=0)
    # We'll store worker_images as a comma-separated string for simplicity
    worker_images = Column(Text, default="")

    # Relationship to SafetyDetection
    detections = relationship("SafetyDetection", back_populates="upload", cascade="all, delete-orphan")


class SafetyDetection(Base):
    """
    Stores individual safety gear detections (e.g., bounding boxes for helmets/vests).
    """
    __tablename__ = "safety_detections"

    id = Column(Integer, primary_key=True, index=True)
    label = Column(String)  # e.g. 'H', 'V'
    box = Column(String)     # bounding box as string, e.g. "x1,y1,x2,y2"
    timestamp = Column(DateTime)

    upload_id = Column(Integer, ForeignKey("uploads.id"))
    upload = relationship("Upload", back_populates="detections")


Base.metadata.create_all(bind=engine)


##############################################################################
#                           FastAPI App & Configuration
##############################################################################

app = FastAPI(
    title="Industrial Safety Monitor (FastAPI + SQLite)",
    description="A YOLO-based safety gear detection app. Three endpoints: upload, results, dashboard.",
    version="1.0.0",
)

# Allow cross-origin requests (optional)
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Directories
UPLOAD_FOLDER = "static/uploads"
PROCESSED_FOLDER = "static/processed"
WORKER_FOLDER = "static/workers"
CHARTS_FOLDER = "static/charts"
ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg', 'gif', 'mp4'}

os.makedirs(UPLOAD_FOLDER, exist_ok=True)
os.makedirs(PROCESSED_FOLDER, exist_ok=True)
os.makedirs(WORKER_FOLDER, exist_ok=True)
os.makedirs(CHARTS_FOLDER, exist_ok=True)

##############################################################################
#                           YOLO Model Setup
##############################################################################

input_shape = (416, 416)
class_names = []
anchor_boxes = None
num_classes = 0
num_anchors = 0
model = None

def prepare_model(approach: int):
    """
    Prepares the YOLO model for the selected approach (1, 2, or 3).
    """
    global input_shape, class_names, anchor_boxes
    global num_classes, num_anchors

    if approach not in [1, 2, 3]:
        raise NotImplementedError("Approach must be 1, 2, or 3")

    # Classes: H=Helmet, V=Vest, W=Worker
    class_names[:] = ['H', 'V', 'W']

    # Anchor boxes by approach
    if approach == 1:
        anchor_boxes = np.array(
            [
                np.array([[76, 59], [84, 136], [188, 225]]) / 32,
                np.array([[25, 15], [46, 29], [27, 56]]) / 16,
                np.array([[5, 3], [10, 8], [12, 26]]) / 8
            ],
            dtype='float64'
        )
    elif approach == 2:
        anchor_boxes = np.array(
            [
                np.array([[73, 158], [128, 209], [224, 246]]) / 32,
                np.array([[32, 50], [40, 104], [76, 73]]) / 16,
                np.array([[6, 11], [11, 23], [19, 36]]) / 8
            ],
            dtype='float64'
        )
    else:  # approach == 3
        anchor_boxes = np.array(
            [
                np.array([[76, 59], [84, 136], [188, 225]]) / 32,
                np.array([[25, 15], [46, 29], [27, 56]]) / 16,
                np.array([[5, 3], [10, 8], [12, 26]]) / 8
            ],
            dtype='float64'
        )

    num_classes = len(class_names)
    num_anchors = anchor_boxes.shape[0] * anchor_boxes.shape[1]

    input_tensor = Input(shape=(input_shape[0], input_shape[1], 3))
    num_out_filters = (num_anchors // 3) * (5 + num_classes)
    _model = yolo_body(input_tensor, num_out_filters)

    weight_path = f"model-data/weights/pictor-ppe-v302-a{approach}-yolo-v3-weights.h5"
    if not os.path.exists(weight_path):
        raise FileNotFoundError(f"Weight file not found: {weight_path}")

    _model.load_weights(weight_path)
    return _model

##############################################################################
#                           Utility & Detection Logic
##############################################################################

def allowed_file(filename: str) -> bool:
    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS

def get_db() -> Session:
    """
    Yields a database session.
    """
    db = SessionLocal()
    try:
        yield db
    finally:
        db.close()

def run_detection_on_frame(frame: np.ndarray, 
                           approach: int, 
                           upload_id: int, 
                           db: Session) -> np.ndarray:
    """
    Runs YOLO detection on a single frame, updates DB counters/detections,
    and returns the annotated frame.
    """
    global model, anchor_boxes, class_names, input_shape

    ih, iw = frame.shape[:2]
    resized = letterbox_image(frame, input_shape)
    resized_expanded = np.expand_dims(resized, 0)
    image_data = np.array(resized_expanded) / 255.0

    prediction = model.predict(image_data)
    boxes = detection(
        prediction,
        anchor_boxes,
        len(class_names),
        image_shape=(ih, iw),
        input_shape=input_shape,
        max_boxes=50,
        score_threshold=0.3,
        iou_threshold=0.45,
        classes_can_overlap=False
    )[0].numpy()

    # Tally
    workers, helmets, vests = [], [], []
    for box in boxes:
        x1, y1, x2, y2, score, cls_id = box
        x1, y1, x2, y2 = map(int, [x1, y1, x2, y2])
        cls_id = int(cls_id)
        label = class_names[cls_id]

        if label == 'W':
            workers.append((x1, y1, x2, y2))
            color = (0, 255, 0)
        elif label == 'H':
            helmets.append((x1, y1, x2, y2))
            color = (255, 0, 0)
        elif label == 'V':
            vests.append((x1, y1, x2, y2))
            color = (0, 0, 255)
        else:
            color = (255, 255, 0)

        cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
        cv2.putText(frame, label, (x1, y1 - 10),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.9, color, 2)

    upload_obj = db.query(Upload).filter(Upload.id == upload_id).first()
    if upload_obj:
        upload_obj.total_workers += len(workers)
        upload_obj.total_helmets += len(helmets)
        upload_obj.total_vests += len(vests)
        db.commit()

        # Insert SafetyDetection for helmets/vests
        now_utc = datetime.now(timezone.utc)
        for (hx1, hy1, hx2, hy2) in helmets:
            db.add(SafetyDetection(
                label='H',
                box=f"{hx1},{hy1},{hx2},{hy2}",
                timestamp=now_utc,
                upload_id=upload_id
            ))
        for (vx1, vy1, vx2, vy2) in vests:
            db.add(SafetyDetection(
                label='V',
                box=f"{vx1},{vy1},{vx2},{vy2}",
                timestamp=now_utc,
                upload_id=upload_id
            ))
        db.commit()

        # Also save worker crops
        worker_images_list = []
        for idx, (wx1, wy1, wx2, wy2) in enumerate(workers, start=1):
            crop = frame[wy1:wy2, wx1:wx2]
            if crop.size == 0:
                continue
            worker_filename = f"worker_{upload_id}_{idx}.jpg"
            worker_path = os.path.join(WORKER_FOLDER, worker_filename)
            cv2.imwrite(worker_path, crop)
            worker_images_list.append(worker_path)

        # Append new worker images
        existing_imgs = upload_obj.worker_images.split(",") if upload_obj.worker_images else []
        all_imgs = existing_imgs + worker_images_list
        upload_obj.worker_images = ",".join([w for w in all_imgs if w])
        db.commit()

    return frame

def generate_and_email_pdf(upload_obj: Upload, db: Session):
    """
    Generates a PDF report for a single upload, then emails it to upload_obj.user_email.
    """
    # We’ll produce a single-page-ish PDF with the detection summary for this upload.

    # Grab top-level stats
    total_workers = upload_obj.total_workers
    total_helmets = upload_obj.total_helmets
    total_vests = upload_obj.total_vests
    worker_images = upload_obj.worker_images.split(",") if upload_obj.worker_images else []

    # Create a PDF
    buffer = BytesIO()
    doc = SimpleDocTemplate(buffer, pagesize=A4)
    elements = []
    styles = getSampleStyleSheet()

    # Title
    elements.append(Paragraph("Industrial Safety Monitor Report", styles["Title"]))
    elements.append(Paragraph(f"Upload ID: {upload_obj.id}", styles["Normal"]))
    elements.append(Paragraph(f"Filename: {upload_obj.filename}", styles["Normal"]))
    elements.append(Paragraph(f"Timestamp: {upload_obj.timestamp.strftime('%Y-%m-%d %H:%M:%S')}", styles["Normal"]))
    elements.append(Paragraph(f"Approach: {upload_obj.approach}", styles["Normal"]))
    elements.append(Paragraph(f"User Email: {upload_obj.user_email}", styles["Normal"]))
    elements.append(Spacer(1, 12))

    # Table of basic detection metrics
    data = [
        ["Total Workers", total_workers],
        ["Total Helmets", total_helmets],
        ["Total Vests", total_vests]
    ]
    table = Table(data, colWidths=[200, 200])
    table.setStyle(TableStyle([
        ("BACKGROUND", (0, 0), (-1, 0), colors.grey),
        ("TEXTCOLOR", (0, 0), (-1, 0), colors.whitesmoke),
        ("ALIGN", (0, 0), (-1, -1), "CENTER"),
        ("FONTNAME", (0, 0), (-1, 0), "Helvetica-Bold"),
        ("FONTSIZE", (0, 0), (-1, 0), 12),
        ("BOTTOMPADDING", (0, 0), (-1, 0), 12),
        ("BACKGROUND", (0, 1), (-1, -1), colors.beige),
        ("GRID", (0, 0), (-1, -1), 1, colors.black),
    ]))
    elements.append(table)
    elements.append(Spacer(1, 12))

    # Show worker crops, if any
    if worker_images:
        elements.append(Paragraph("Detected Workers:", styles["Heading3"]))
        elements.append(Spacer(1, 12))
        for wimg in worker_images:
            wimg = wimg.strip()
            if wimg and os.path.exists(wimg):
                elements.append(RLImage(wimg, width=100, height=75))
        elements.append(Spacer(1, 12))

    doc.build(elements)
    buffer.seek(0)
    pdf_data = buffer.getvalue()

    # Email the PDF
    receiver_email = upload_obj.user_email
    if not receiver_email:
        print("No email to send to.")
        return  # skip emailing if no user email

    # Adjust credentials
    sender_email = "[email protected]"
    sender_password = "aobh rdgp iday bpwg"
    subject = "Industrial Safety Monitor - Your Detection Report"
    body = (
        "Hello,\n\n"
        "Please find attached the Industrial Safety Monitor detection report.\n"
        "Regards,\nISM Bot"
    )

    from email.mime.multipart import MIMEMultipart
    from email.mime.text import MIMEText
    from email.mime.application import MIMEApplication

    msg = MIMEMultipart()
    msg["From"] = sender_email
    msg["To"] = receiver_email
    msg["Subject"] = subject
    msg.attach(MIMEText(body, "plain"))

    part = MIMEApplication(pdf_data, _subtype="pdf")
    part.add_header("Content-Disposition", "attachment", filename="ISM_Report.pdf")
    msg.attach(part)

    try:
        with smtplib.SMTP("smtp.gmail.com", 587) as server:
            server.starttls()
            server.login(sender_email, sender_password)
            server.send_message(msg)
        print(f"Email sent successfully to {receiver_email}!")
    except Exception as e:
        print(f"Error sending email: {e}")


##############################################################################
#                               1) /upload
##############################################################################

@app.post("/upload", summary="Upload image/video + email; run detection, send PDF to email.")
async def upload_file(
    approach: int = Form(...),
    file: UploadFile = File(...),
    user_email: str = Form(...),
):
    """
    1) User uploads an image/video with approach + email.
    2) We run YOLO detection.
    3) We store results in DB.
    4) We generate a PDF and email it to `user_email`.
    5) Return detection counts in JSON.
    """
    global model

    db = SessionLocal()

    # Prepare YOLO model for the chosen approach
    try:
        if (model is None) or (approach not in [1, 2, 3]):
            model = prepare_model(approach)
    except Exception as e:
        db.close()
        raise HTTPException(status_code=500, detail=str(e))

    # Check file type
    filename = file.filename
    if not allowed_file(filename):
        db.close()
        raise HTTPException(
            status_code=400,
            detail="Unsupported file type. Allowed: .png, .jpg, .jpeg, .gif, .mp4",
        )

    # Save the uploaded file
    filepath = os.path.join(UPLOAD_FOLDER, filename)
    with open(filepath, "wb") as f:
        f.write(await file.read())

    # Create an Upload record
    upload_obj = Upload(
        filename=filename,
        filepath=filepath,
        timestamp=datetime.now(timezone.utc),
        approach=approach,
        user_email=user_email,
        total_workers=0,
        total_helmets=0,
        total_vests=0,
        worker_images=""
    )
    db.add(upload_obj)
    db.commit()
    db.refresh(upload_obj)
    upload_id = upload_obj.id

    # If it's an image
    if filename.lower().endswith((".png", ".jpg", ".jpeg", ".gif")):
        img = cv2.imread(filepath)
        if img is None:
            db.close()
            raise HTTPException(status_code=400, detail="Failed to read the image file.")

        # Run detection on the single image
        annotated_frame = run_detection_on_frame(img, approach, upload_id, db)

        # Save processed image
        processed_filename = f"processed_{filename}"
        processed_path = os.path.join(PROCESSED_FOLDER, processed_filename)
        cv2.imwrite(processed_path, annotated_frame)

    # If it's a video
    elif filename.lower().endswith(".mp4"):
        video = cv2.VideoCapture(filepath)
        if not video.isOpened():
            db.close()
            raise HTTPException(status_code=400, detail="Failed to read the video file.")

        fourcc = cv2.VideoWriter_fourcc(*"mp4v")
        processed_filename = f"processed_{filename}"
        processed_path = os.path.join(PROCESSED_FOLDER, processed_filename)

        original_width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
        original_height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
        fps = video.get(cv2.CAP_PROP_FPS)
        frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT))

        out = cv2.VideoWriter(
            processed_path, fourcc, fps, (original_width, original_height)
        )

        current_frame = 0
        while True:
            ret, frame = video.read()
            if not ret:
                break
            current_frame += 1
            print(f"Processing frame {current_frame}/{frame_count} (Upload ID={upload_id})")

            annotated_frame = run_detection_on_frame(frame, approach, upload_id, db)
            out.write(annotated_frame)

        video.release()
        out.release()

    # Now fetch updated counts
    db.refresh(upload_obj)

    # Generate & email PDF
    generate_and_email_pdf(upload_obj, db)

    counts = {
        "total_workers": upload_obj.total_workers,
        "total_helmets": upload_obj.total_helmets,
        "total_vests": upload_obj.total_vests
    }

    db.close()
    return {
        "message": f"File uploaded, detection done, PDF emailed to {user_email}.",
        "upload_id": upload_id,
        "counts": counts
    }


##############################################################################
#                               2) /results
##############################################################################

@app.get("/results", summary="Fetch the most recent upload’s details.")
def get_results():
    """
    Returns the details (counts, file paths, worker_images) of the most recent upload.
    """
    db = SessionLocal()
    latest = db.query(Upload).order_by(Upload.timestamp.desc()).first()
    if not latest:
        db.close()
        return {"message": "No uploads found in the database."}

    processed_filename = f"processed_{latest.filename}"
    processed_path = os.path.join(PROCESSED_FOLDER, processed_filename)
    data = {
        "upload_id": latest.id,
        "filename": latest.filename,
        "original_path": latest.filepath,
        "processed_path": processed_path if os.path.exists(processed_path) else None,
        "approach": latest.approach,
        "user_email": latest.user_email,
        "total_workers": latest.total_workers,
        "total_helmets": latest.total_helmets,
        "total_vests": latest.total_vests,
        "worker_images": (latest.worker_images.split(",") if latest.worker_images else []),
        "timestamp": latest.timestamp.strftime("%Y-%m-%d %H:%M:%S")
    }
    db.close()
    return data


##############################################################################
#                               3) /dashboard
##############################################################################

@app.get("/dashboard", summary="Get aggregated statistics for a dashboard.")
def dashboard():
    """
    Returns aggregated stats (uploads, detection sums, time-series, approach usage) in JSON.
    """
    db = SessionLocal()

    # Total uploads
    total_uploads = db.query(Upload).count()

    # Summation of detections
    agg = db.query(
        func.sum(Upload.total_workers).label("tw"),
        func.sum(Upload.total_helmets).label("th"),
        func.sum(Upload.total_vests).label("tv")
    ).one()
    total_workers = agg.tw or 0
    total_helmets = agg.th or 0
    total_vests = agg.tv or 0

    # Time-series by day
    day_rows = db.query(
        func.date(Upload.timestamp).label("day"),
        func.count(Upload.id).label("uploads"),
        func.sum(Upload.total_workers).label("workers"),
        func.sum(Upload.total_helmets).label("helmets"),
        func.sum(Upload.total_vests).label("vests")
    ).group_by(func.date(Upload.timestamp)).order_by(func.date(Upload.timestamp)).all()

    dates = []
    uploads_per_day = []
    workers_per_day = []
    helmets_per_day = []
    vests_per_day = []

    for row in day_rows:
        dates.append(row.day)
        uploads_per_day.append(row.uploads or 0)
        workers_per_day.append(row.workers or 0)
        helmets_per_day.append(row.helmets or 0)
        vests_per_day.append(row.vests or 0)

    # Approach usage
    approach_rows = db.query(
        Upload.approach,
        func.count(Upload.id).label("count")
    ).group_by(Upload.approach).all()
    approach_data = []
    for ar in approach_rows:
        approach_data.append({
            "approach": f"Approach-{ar.approach}",
            "count": ar.count
        })

    # Basic distribution of helmets vs. vests
    safety_gear_labels = ["Helmets", "Vests"]
    safety_gear_counts = [total_helmets, total_vests]

    db.close()
    return {
        "total_uploads": total_uploads,
        "total_workers": total_workers,
        "total_helmets": total_helmets,
        "total_vests": total_vests,
        "time_series": {
            "dates": dates,
            "uploads_per_day": uploads_per_day,
            "workers_per_day": workers_per_day,
            "helmets_per_day": helmets_per_day,
            "vests_per_day": vests_per_day
        },
        "approach_usage": approach_data,
        "safety_gear_distribution": {
            "labels": safety_gear_labels,
            "counts": safety_gear_counts
        }
    }


##############################################################################
#                           Startup (Load YOLO Model)
##############################################################################

@app.on_event("startup")
def on_startup():
    fix_tf_gpu()
    global model
    try:
        # Load default approach=1 at startup (optional)
        model_local = prepare_model(approach=1)
        model = model_local
        print("YOLO model (Approach=1) loaded successfully.")
    except FileNotFoundError as e:
        print(f"Model file not found on startup: {e}")
    except Exception as e:
        print(f"Error preparing model on startup: {e}")