import os
import cv2
import gradio as gr
import torch
import numpy as np
from simple_salesforce import Salesforce
import time

# Salesforce connection details (replace these with your actual credentials)
sf = Salesforce(username='prashanth@safetyanaluzer.com', password='SaiPrash461', security_token='MOA3BXBfGyqnjBneog8a9IcGw')

# Test Salesforce connection
try:
    # Simple query to verify the connection
    result = sf.query("SELECT Id FROM Safety_Video_Report__c LIMIT 1")
    print("✅ Salesforce connection successful.")
except Exception as e:
    print(f"❌ Error connecting to Salesforce: {e}")

try:
    from ultralytics import YOLO
except ImportError as e:
    print("❌ Ultralytics not installed. Run: pip install ultralytics")
    raise

# ==========================
# Configuration
# ==========================
DEFAULT_MODEL_PATH = "models/yolov8_safety.pt"
FALLBACK_MODEL = "yolov8n.pt"  # Use nano model if custom one is missing
MODEL_PATH = os.getenv("SAFETY_MODEL_PATH", DEFAULT_MODEL_PATH)

VIOLATION_LABELS = {
    0: "no_helmet",
    1: "no_harness",
    2: "unsafe_posture",
    3: "unsafe_zone"
}

# ==========================
# Device Setup
# ==========================
try:
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    print(f"✅ Using device: {device}")
except Exception as e:
    print(f"⚠️ Error setting device: {e}")
    device = torch.device("cpu")

# ==========================
# Load Model (Use YOLOv8n for Faster Inference)
# ==========================
if os.path.isfile(MODEL_PATH):
    selected_model = MODEL_PATH
    print(f"✅ Found model at: {selected_model}")
else:
    print(f"⚠️ Model file '{MODEL_PATH}' not found. Falling back to: {FALLBACK_MODEL}")
    selected_model = FALLBACK_MODEL

try:
    model = YOLO(selected_model)
    print(f"✅ Model loaded: {selected_model}")
except Exception as e:
    print(f"❌ Failed to load model: {e}")
    raise

# ==========================
# Video Processing with Optimizations
# ==========================
def process_video(video_path, frame_skip=5, max_frames=100):
    try:
        video = cv2.VideoCapture(video_path)
        if not video.isOpened():
            raise ValueError("Could not open video file.")

        frame_count = 0
        violations = []
        processed_frame_count = 0
        start_time = time.time()

        while True:
            ret, frame = video.read()
            if not ret:
                break

            # Skip frames to reduce processing time (process only every 'frame_skip' frame)
            if frame_count % frame_skip != 0:
                frame_count += 1
                continue

            # Model inference for detecting violations
            results = model(frame, device=device)

            for result in results:
                for box in result.boxes:
                    cls = int(box.cls)
                    conf = float(box.conf)
                    xywh = box.xywh.cpu().numpy()[0]

                    label = VIOLATION_LABELS.get(cls, f"class_{cls}")
                    violations.append({
                        "frame": frame_count,
                        "violation": label,
                        "confidence": round(conf, 2),
                        "bounding_box": [round(x, 2) for x in xywh]
                    })

            frame_count += 1
            processed_frame_count += 1

            # Stop processing after a fixed number of frames to save time
            if processed_frame_count >= max_frames:
                break

            # Check elapsed time, stop if we exceed 30 seconds
            elapsed_time = time.time() - start_time
            if elapsed_time > 30:
                print("⏰ Exceeded 30 seconds of processing time.")
                break

        video.release()
        score = calculate_safety_score(violations)

        # Send data to Salesforce
        send_to_salesforce(violations, score, video_path)

        return violations, score

    except Exception as e:
        print(f"❌ Error processing video: {e}")
        return [], f"Error: {e}"

# ==========================
# Safety Score Calculation
# ==========================
def calculate_safety_score(violations):
    base_score = 100
    penalties = {
        "no_helmet": 25,
        "no_harness": 30,
        "unsafe_posture": 20,
        "unsafe_zone": 25
    }
    for v in violations:
        base_score -= penalties.get(v["violation"], 0)
    return max(base_score, 0)

def send_to_salesforce(violations, score, video_path):
    # Dynamic values (e.g., from user input)
    site_name = "Construction Site 1"  # Replace with dynamic data from your app
    uploaded_by = "JohnDoe"  # Replace with actual user info (e.g., from session or UI)
    upload_date = "2025-05-08T12:00:00Z"  # Replace with actual upload date
    status = "Reviewed"  # Update based on video processing result

    print("Starting file upload to Salesforce...")
    # Video file upload
    file_id = upload_video_to_salesforce(video_path)  # Upload the video and get the file ID
    print(f"File uploaded to Salesforce with ID: {file_id}")

    # Violations details: Frame number, violation type, confidence level
    violations_details = "\n".join([f"Frame {v['frame']}: {v['violation']} (Confidence: {v['confidence']})" for v in violations])

    # Data to be inserted into Salesforce
    data = {
        'Site__c': site_name,
        'Uploaded_By__c': uploaded_by,
        'Upload_Date__c': upload_date,
        'Status__c': status,
        'Compliance_Score__c': score,
        'Violations_Found__c': len(violations),
        'Violations_Details__c': violations_details,
        'Video_File__c': file_id,  # This is the file ID from ContentVersion
        'PDF_Report_URL__c': "http://path_to_pdf_report"  # Replace with actual PDF report URL if available
    }

    try:
        print(f"Creating Salesforce record with data: {data}")  # Log data being sent to Salesforce
        result = sf.Safety_Video_Report__c.create(data)  # Changed the object name to Safety_Video_Report__c
        print(f"✅ Successfully created new report in Salesforce with ID: {result['id']}")
    except Exception as e:
        print(f"❌ Error creating/updating record in Salesforce: {e}")

# ==========================
# Function to Upload Video to Salesforce
# ==========================
def upload_video_to_salesforce(video_path):
    with open(video_path, 'rb') as file:
        video_data = file.read()

    content_version_data = {
        'Title': 'Safety Video',
        'PathOnClient': 'safety_video.mp4',  # Path of the file on the client's system
        'VersionData': video_data,  # Video file content as binary
        'FirstPublishLocationId': 'your_salesforce_library_id',  # Library ID (if required, or leave blank)
    }

    try:
        # Create a new ContentVersion record to upload the video
        content_version = sf.ContentVersion.create(content_version_data)
        file_id = content_version['Id']  # This is the ContentDocument ID
        print(f"✅ Video uploaded to Salesforce with File ID: {file_id}")
        return file_id
    except Exception as e:
        print(f"❌ Error uploading video to Salesforce: {e}")
        return None

# ==========================
# Gradio Interface
# ==========================
def gradio_interface(video_file):
    if not video_file:
        return "Please upload a video file.", ""

    violations, score = process_video(video_file)
    return violations, f"Safety Score: {score}%"

interface = gr.Interface(
    fn=gradio_interface,
    inputs=gr.Video(label="Upload Site Video"),
    outputs=[
        gr.JSON(label="Detected Safety Violations"),
        gr.Textbox(label="Compliance Score")
    ],
    title="Worksite Safety Violation Analyzer",
    description="Upload short site videos to detect safety violations (e.g., no helmet, no harness, unsafe posture)."
)

if __name__ == "__main__":
    print("🚀 Launching Safety Analyzer App...")
    interface.launch()