from fastapi import FastAPI, File, UploadFile
import numpy as np
from io import BytesIO
from PIL import Image
import tensorflow as tf

app = FastAPI()

# Model yükleniyor, yüklenen modelin yolunu kontrol ediniz.
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 "Hello, I am alive"

def read_file_as_image(data) -> np.ndarray:
    image = np.array(Image.open(BytesIO(data)))
    return image

@app.post("/predict")
async def predict(file: UploadFile = File(...)):
    image_data = await file.read()
    try:
        image = read_file_as_image(image_data)
    except IOError:
        return {"error": "Invalid image format"}

    # Görüntüyü modelin beklediği boyuta getirme
    image = tf.image.resize(image, (229, 229))
    # 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)
    }