from flask import Flask, request, jsonify, render_template
from detectron2.config import get_cfg
from detectron2.engine import DefaultPredictor
from detectron2.data import MetadataCatalog
from detectron2.utils.visualizer import Visualizer, ColorMode
import numpy as np
from PIL import Image
import io
import os
import requests
import gdown
from skimage import io as skio
from torchvision.ops import box_iou
import torch
from roboflow import Roboflow
import supervision as sv
import cv2
import tempfile
import os
import numpy as np
import requests

# Initialize Flask app
app = Flask(__name__)


@app.route("/")
def home():
    return render_template("index.html")


@app.route("/fetch-image", methods=["POST"])
def fetchImage():
    file = None
    if "url" in request.form:
        url = request.form["url"]
        response = requests.get(url)
        file = io.BytesIO(response.content)
    elif "file" in request.files:
        file = request.files["file"]
    # url = "https://firebasestorage.googleapis.com/v0/b/car-damage-detector-s34rrz.firebasestorage.app/o/users%2FYMd99dt33HaktTWpYp5MM5oYeBE3%2Fuploads%2F1737454072124000.jpg?alt=media&token=9eae79fa-4c06-41a5-9f58-236c39efaac0"

    # File name for saving
    file_name = "downloaded_image.jpg"

    # Download the image
    response = requests.get(url)

    # Save the image to the current directory
    if response.status_code == 200:
        with open(file_name, "wb") as file:
            file.write(response.content)
        print(f"Image downloaded and saved as {file_name}")
    else:
        print(f"Failed to download image. Status code: {response.status_code}")
    # Load image
    image = cv2.imread(file_name)

    rf = Roboflow(api_key="LqD8Cs4OsoK8seO3CPkf")

    project_parts = rf.workspace().project("car-parts-segmentation")
    model_parts = project_parts.version(2).model

    project_damage = rf.workspace().project("car-damage-detection-ha5mm")
    model_damage = project_damage.version(1).model

    # Run the damage detection model
    result_damage = model_damage.predict(
        file_name,
        confidence=40,
    ).json()

    # Extract detections from the result
    detections_damage = sv.Detections.from_inference(result_damage)

    # Read the input image

    # Annotate damaged areas of the car
    mask_annotator = sv.MaskAnnotator()
    annotated_image_damage = mask_annotator.annotate(
        scene=image, detections=detections_damage
    )

    # Create a temporary directory to save outputs
    temp_dir = tempfile.mkdtemp()

    # Define a repair cost dictionary (per part)
    repair_cost_dict = {
        "wheel": 100,  # Base cost for wheel
        "door": 200,  # Base cost for door
        "hood": 300,  # Base cost for hood
        "front_bumper": 250,  # Base cost for bumper
        "trunk": 200,
        "front_glass": 150,
        "back_left_door": 200,
        "left_mirror": 20,
        "back_glass": 150,
    }

    # Initialize total cost
    total_cost = 0

    # Ensure coordinate processing is done in chunks of 4
    coordinates = list(map(int, detections_damage.xyxy.flatten()))
    num_damages = (
        len(coordinates) // 4
    )  # Each damage has 4 coordinates (x1, y1, x2, y2)

    # Iterate through damages
    for i in range(num_damages):
        x1, y1, x2, y2 = coordinates[i * 4 : (i + 1) * 4]

        # Ensure the coordinates are within image bounds
        x1, y1 = max(0, x1), max(0, y1)
        x2, y2 = min(image.shape[1], x2), min(image.shape[0], y2)

        # Crop the damaged region
        cropped_damage = image[y1:y2, x1:x2]

        # Check if the cropped region is valid
        if cropped_damage.size == 0:
            print(f"Skipping empty crop for damage region {i + 1}")
            continue

        # Save the cropped damaged area
        damage_image_path = os.path.join(temp_dir, f"damage_image_{i}.png")
        cv2.imwrite(damage_image_path, cropped_damage)

        # Run the parts detection model on the cropped damage
        result_parts = model_parts.predict(damage_image_path, confidence=15).json()
        detections_parts = sv.Detections.from_inference(result_parts)

        # Calculate repair cost for each detected part
        for part in result_parts["predictions"]:
            part_name = part["class"]
            damage_area = part["width"] * part["height"]
            cropped_area = (x2 - x1) * (y2 - y1)
            damage_percentage = (damage_area / cropped_area) * 100

            # Lookup cost and add to total
            base_cost = repair_cost_dict.get(
                part_name, 0
            )  # Default to 0 if part not in dict
            repair_cost = (damage_percentage / 100) * base_cost
            total_cost += repair_cost

            print(
                f"Damage {i + 1} - {part_name}: {damage_percentage:.2f}% damaged, Cost: ${repair_cost:.2f}"
            )

        # Annotate and save the result
        part_annotator = sv.LabelAnnotator()
        annotated_parts_image = part_annotator.annotate(
            scene=cropped_damage, detections=detections_parts
        )
        annotated_parts_path = os.path.join(temp_dir, f"annotated_parts_{i}.png")
        cv2.imwrite(annotated_parts_path, annotated_parts_image)

    # Save the overall annotated image
    annotated_image_path = os.path.join(temp_dir, "annotated_image_damage.png")
    cv2.imwrite(annotated_image_path, annotated_image_damage)

    # Return the total cost in the specified format
    result = {"total_cost": total_cost}
    print(result)

    return jsonify(result)


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