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from fastapi import FastAPI, File, UploadFile
import tensorflow as tf
from tensorflow.keras.preprocessing.image import load_img, img_to_array
import numpy as np
from PIL import Image, UnidentifiedImageError
# Model yükleme
try:
model = tf.keras.models.load_model("face_shape_model.h5")
except Exception as e:
raise RuntimeError(f"Model loading failed: {str(e)}")
# Sınıf isimleri
class_names = ['Heart', 'Oblong', 'Oval', 'Round', 'Square']
# FastAPI uygulamasını başlat
app = FastAPI()
# Resmi yükle ve ön işle
def load_and_preprocess_image(image):
try:
# Eğer resim RGBA veya diğer modda ise RGB'ye çevir
if image.mode != "RGB":
image = image.convert("RGB")
# Resmi yeniden boyutlandır
img = image.resize((224, 224))
# Array'e çevir ve normalize et
img_array = img_to_array(img) / 255.0
# Batch boyutunu ekle
img_array = np.expand_dims(img_array, axis=0)
return img_array
except Exception as e:
raise ValueError(f"Preprocessing error: {str(e)}")
@app.post("/predict/")
async def predict(file: UploadFile = File(...)):
try:
# Yüklenen dosyayı aç
try:
image = Image.open(file.file)
except UnidentifiedImageError as e:
return {"error": f"Invalid image file: {str(e)}"}
# Resmi yükle ve ön işle
img_array = load_and_preprocess_image(image)
# Tahmin yap
predictions = model.predict(img_array, verbose=0)
predicted_class = class_names[np.argmax(predictions[0])]
confidence = np.max(predictions[0]) * 100
# Tüm sınıfların olasılıklarını hesapla
class_probabilities = {
class_names[i]: float(predictions[0][i] * 100)
for i in range(len(class_names))
}
return {
"predicted_class": predicted_class,
"confidence": f"{confidence:.2f}%",
"class_probabilities": class_probabilities
}
except Exception as e:
return {"error": f"Prediction failed: {str(e)}"}
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