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app.py

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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)}"}

face_shape_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:69b280ddee29554842a41ca899870e2bf81e7f3981a6f18860049c9c66371d1e
+size 243366704