app.py

from fastapi import FastAPI, File, UploadFile, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from inference import ObjectDetector
import numpy as np
import cv2
import socket
import uvicorn

# Configuration
MODEL_ONNX_PATH = "model.onnx"
CLASS_NAMES = [
    'Butter_Dukat_Maslac_Stick_250g',
    'Butter_Zbregov_Maslac_Stick_250g',
    'Butter_Zdenka_Maslac_Stick_250g',
    'Cheese_President_Gouda_Cube_250g',
    'Chicken_Cekin_Pileca_Prsa_500g',
    'Coffee_Franch_Crema_Bag_175g',
    'Coffee_Franch_Crema_Box_250g',
    'Coffee_Franch_Instant_Crema_80g',
    'Coffee_Franch_Intense_Box_250g',
    'Coffee_Franch_Original_Box_250g',
    'Coffee_Franch_Sensual_Box_250g',
    'Drink_CocaCola_Original_Bottle_1l',
    'Flour_Mlineta_Brasno_Ostro_1kg',
    'Juice_Vindi_Naranca_Nektar_1l',
    'Ketchup_Zvijezda_Mild_Bottle_500g',
    'Mayonnaise_Zvijezda_Delicate_Bottle_400g',
    'Milk_Zbregov_Trajno_28_1l',
    'Oil_Dijamant_Suncokretovo_Bottle_1l',
    'Oil_Zvijezda_Suncokretovo_Ulje_1l',
    'Pasta_Barilla_Fusilli_Box_500g',
    'Rice_Gallo_Long_Grain_900g',
    'Rice_Kplus_Arborio_BijeliDugi_1kg',
    'Salt_SolanaPag_Sitna_Box_1kg',
    'Spaghetti_PastaZara_Spaghettini_Bag_500g',
    'Tuna_RioMare_Tonno_Oliva'
]
INPUT_SIZE = 640

# Initialize detector
detector = ObjectDetector(
    model_path=MODEL_ONNX_PATH,
    class_names=CLASS_NAMES,
    input_size=INPUT_SIZE
)

# Initialize FastAPI
app = FastAPI()

# Enhanced CORS configuration
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
    expose_headers=["*"]
)

def get_base_url():
    hostname = socket.gethostname()
    port = 7860  # Hugging Face Spaces uses port 7860
    return f"https://{hostname}.hf.space"

@app.options("/detect")
async def detect_options():
    return {"Allow": "POST"}

@app.get("/")
def health_check():
    return {"status": "OK", "model": "Object Detection API"}

@app.post("/detect")
async def detect_objects(file: UploadFile = File(...)):
    try:
        if not file.content_type.startswith("image/"):
            raise HTTPException(400, "File must be an image")

        image_data = await file.read()
        image = cv2.imdecode(np.frombuffer(image_data, np.uint8), cv2.IMREAD_COLOR)
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)  # <<< ADD THIS LINE
        if image is None:
            raise HTTPException(400, "Invalid image data")

        # Remove RGB conversion - models expect BGR from OpenCV
        # image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)  # DELETE THIS LINE
        
        # Fix variable reference
        detections = detector.predict(image)  # Add this line
        
        return {
            "status": "success",
            "detections": detections,  # Use the variable
            "count": len(detections)   # Now properly defined
        }

    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(500, f"Processing error: {str(e)}")

if __name__ == "__main__":
    base_url = get_base_url()
    print(f"Base URL: {base_url}")
    print(f"API endpoint: {base_url}/detect")
    uvicorn.run(app, host="0.0.0.0", port=7860)