api.py

import cv2
import tensorflow as tf
import numpy as np
from fastapi import FastAPI, UploadFile, File
from pydantic import BaseModel
from huggingface_hub import from_pretrained_keras

app = FastAPI()

def load_ben_color(image, sigmaX=10):
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    image = cv2.resize(image, (224, 224))
    image = cv2.addWeighted(image, 4, cv2.GaussianBlur(image, (0, 0), sigmaX), -4, 128)
    return image

def clahe(image):
    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
    r, g, b = cv2.split(image)
    r = clahe.apply(r)
    g = clahe.apply(g)
    b = clahe.apply(b)
    result = cv2.merge((r, g, b))
    return result

def filter1(image):
    image = load_ben_color(image)
    return image

def filter2(image):
    image = clahe(image)
    image = cv2.resize(image, (224, 224))
    return image

def predict(image, model, filter_func):
    model_image = filter_func(image)
    model_image = np.array([model_image], dtype=np.float32) / 255.0
    predictions = model(tf.constant(model_image))
    return predictions.numpy()

def result(predictions):
    class_labels = ["Age related Macular Degeneration", "Cataract", "Diabetes", "Glaucoma", "Hypertension", "Normal", "Others", "Pathological Myopia"]
    predictions = np.array(predictions)
    predictions = predictions.tolist()[0]
    predictions_index = np.argmax(predictions)
    return class_labels[predictions_index]

# Model tanımlamaları
models_names = ["ODIR-B-2K-5Class-LastTrain-Xception", "ODIR-B-2K-6Class-LastTrain-Xception"]
model_paths = ["ODIR-B-2K-5Class-LastTrain-Xception", "ODIR-B-2K-6Class-LastTrain-Xception"]

filters = [filter1, filter2]  # İhtiyacınıza göre filtre fonksiyonları burada tanımlandı

models = []
for model_path in model_paths:
    model = tf.saved_model.load(model_path)
    models.append(model)

class PredictionResponse(BaseModel):
    predictions: dict

@app.post("/predict", response_model=PredictionResponse)
async def predict_endpoint(file: UploadFile = File(...)):
    contents = await file.read()
    nparr = np.frombuffer(contents, np.uint8)
    image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)

    result_json = {}
    for i in range(len(models)):
        model = models[i]
        prediction = predict(image, model, filters[i])
        result_json[models_names[i]] = result(prediction)

    return {"predictions": result_json}