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# 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)
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