# -*- coding: utf-8 -*-
"""
Created on Fri Feb 20 00:20:10 2026
@author: Logan
"""
import os
from fastapi import FastAPI, UploadFile, File
from fastapi.responses import FileResponse
import io
from transformers import pipeline
from PIL import Image
from ultralytics import YOLO





proc_app = FastAPI(title = "Camspection Damage Detection API") #Initialize and set the title
from fastapi.middleware.cors import CORSMiddleware

proc_app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"], # Allows any website to call the API
    allow_credentials = True,
    allow_methods=["*"],
    allow_headers=["*"],
)

DamageClassifier = pipeline("image-classification", model = "Cpope3/DamCat")
SeverityClassifier = pipeline("image-classification", model="Cpope3/Camspection_Model") #Create the pipeline for image classification using our checkpoint (necessary for transformerse models)

@proc_app.get("/")
def home(): 
    return FileResponse("index.html") 

@proc_app.post("/detect")
async def detect(image: UploadFile = File(...)):
    scan = await image.read()
    image_data = Image.open(io.BytesIO(scan)).convert("RGB") 
    
    detector = YOLO('yolov8n.pt')
    carCheck = detector(image_data, conf = 0.5)
    YOLOClasses = [2,5,7]
    detectorClasses = carCheck[0].boxes.cls.cpu().numpy()
    carFound = any(cls in YOLOClasses for cls in detectorClasses)
    
    damCheck = DamageClassifier(image_data)
    damaged = damCheck[0]['label'] == "Damaged"
    
    if not carFound:
        return {
            "level": "N/A",
            "score": "0%",
            "debug": "No vehicle detected. Please ensure the vehicle is visible in the frame."
        }
        
    if damaged: #Classify only if car is both detected -and- found to be damaged
        
        result = SeverityClassifier(image_data) 
        best_match = result[0] 
        alt_match = result[1] 
        
        label = best_match['label']
        alt_label = alt_match['label']
        best_score = best_match['score']
        alt_score = alt_match['score']
        
        if (best_score - alt_score <= 0.10): 
            return {
                "warning": "Classification within margin of error!",
                "level": label,
                "alternate": alt_label,
                "score": f"{best_score * 100:.2f}%",
                "alternate_score": f"{alt_score * 100:.2f}%"
            }
        else:
            return {
                "warning": None,
                "level": label,
                "score": f"{best_score * 100:.2f}%"
            }
    else:
        # If car is not damaged according to model
        return {
            "level": "Whole",
            "score": f"{damCheck[0]['score'] * 100:.2f}%",
            "debug": "No damage detected"
        }
        
if __name__ == "__main__":
        import uvicorn
        uvicorn.run(proc_app, host="0.0.0.0", port=7860)                 # on the actual site