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from fastapi import FastAPI, File, UploadFile, HTTPException
import numpy as np
from io import BytesIO
from PIL import Image, UnidentifiedImageError
import tensorflow as tf
from fastapi.middleware.cors import CORSMiddleware
app = FastAPI()
origins = ["*"]
app.add_middleware(
CORSMiddleware,
allow_origins=origins,
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
MODEL = tf.keras.models.load_model("CLAHE_ODIR-ORJ-512_inception_v3.h5")
# Sınıf isimleri
class_names = [
'Age related Macular Degeneration', 'Cataract', 'Diabetes', 'Glaucoma',
'Hypertension', 'Normal', 'Others', 'Pathological Myopia'
]
@app.get("/ping")
async def ping():
return {"message": "Hello, I am alive"}
def read_file_as_image(data) -> np.ndarray:
try:
image = Image.open(BytesIO(data))
return np.array(image)
except UnidentifiedImageError:
raise HTTPException(status_code=400, detail="Invalid image format")
except Exception as e:
raise HTTPException(status_code=400, detail=f"Image processing error: {str(e)}")
@app.post("/predict/")
async def predict(file: UploadFile = File(...)):
image_data = await file.read()
# Dosya tipini kontrol et
if file.content_type not in ["image/jpeg", "image/png", "image/jpg"]:
raise HTTPException(status_code=400, detail="Invalid file type")
try:
image = read_file_as_image(image_data)
except HTTPException as e:
return e
# Görüntüyü modelin beklediği boyuta getirme
image = tf.image.resize(image, (299, 299)) # InceptionV3 için 299x299 olmalı
# Görüntüyü normalize etme
image = tf.cast(image, tf.float32) / 255.0
# Batch haline getirme
img_batch = np.expand_dims(image, 0)
# Model üzerinde tahmin yapma
predictions = MODEL.predict(img_batch)
predicted_class = class_names[np.argmax(predictions[0])]
confidence = np.max(predictions[0])
return {
'class': predicted_class,
'confidence': float(confidence)
}
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