app/main.py

# app/main.py

from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel, HttpUrl
from PIL import Image
import requests
import io

from app.models.animal_vision import predict_animal
from app.models.plant_vision import predict_plant



app = FastAPI(
    title="BIONEXUS Image Intelligence API",
    version="1.0.0"
)

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)




class ImageURLRequest(BaseModel):
    image_url: HttpUrl


def load_image_from_url(url) -> Image.Image:
    url = str(url)  

    headers = {
        "User-Agent": (
            "Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
            "AppleWebKit/537.36 (KHTML, like Gecko) "
            "Chrome/121.0.0.0 Safari/537.36"
        ),
        "Accept": "image/avif,image/webp,image/apng,image/*,*/*;q=0.8",
        "Accept-Language": "en-US,en;q=0.9",
        "Referer": url
    }

    try:
        response = requests.get(
            url,
            headers=headers,
            timeout=10
        )
        response.raise_for_status()
    except requests.RequestException as e:
        raise HTTPException(
            status_code=400,
            detail=f"Failed to download image: {str(e)}"
        )

    try:
        image = Image.open(io.BytesIO(response.content)).convert("RGB")
    except Exception:
        raise HTTPException(
            status_code=400,
            detail="Invalid or unsupported image format"
        )

    return image






@app.post("/animal/predict")
async def animal_predict(payload: ImageURLRequest):
    image = load_image_from_url(payload.image_url)
    return predict_animal(image)


@app.post("/plant/predict")
async def plant_predict(payload: ImageURLRequest):
    image = load_image_from_url(payload.image_url)
    return predict_plant(image)