fix ini

app.py

@@ -1,12 +1,9 @@
 from fastapi import FastAPI, UploadFile, File
-
 import numpy as np
-import cv2
 import tensorflow as tf
 from PIL import Image
 import io
 
-
 app = FastAPI()
 
 # Load model Keras
@@ -28,79 +25,16 @@ labels = [
 def home():
     return {"message": "Aksara Lontara API is running"}
 
-
-def preprocess_image(image: np.ndarray):
-    """ Melakukan segmentasi karakter menggunakan OpenCV """
-
-    # **1️⃣ Edge Detection (Canny)**
-    edges = cv2.Canny(image, 50, 150)
-
-    # **2️⃣ Morphological Cleaning**
-    kernel = np.ones((3, 3), np.uint8)
-    edges_cleaned = cv2.morphologyEx(edges, cv2.MORPH_CLOSE, kernel, iterations=2)
-
-    # **3️⃣ Connected Component Analysis (CCA)**
-    num_labels, labels, stats, _ = cv2.connectedComponentsWithStats(edges_cleaned, connectivity=8)
-
-    # **4️⃣ Filter huruf berdasarkan area**
-    min_area = 500
-    bounding_boxes = []
-
-    for i in range(1, num_labels):  # Skip background
-        x, y, w, h, area = stats[i]
-        if area > min_area:
-            bounding_boxes.append((x, y, w, h))
-
-    # **5️⃣ Urutkan huruf berdasarkan posisi X**
-    bounding_boxes.sort(key=lambda b: b[0])
-
-    # **6️⃣ Gabungkan Bounding Box yang Berdekatan**
-    merged_boxes = []
-    merge_threshold = 20
-
-    for i in range(len(bounding_boxes)):
-        x, y, w, h = bounding_boxes[i]
-
-        if merged_boxes and (x - (merged_boxes[-1][0] + merged_boxes[-1][2])) < merge_threshold:
-            x_prev, y_prev, w_prev, h_prev = merged_boxes.pop()
-            x_new = min(x_prev, x)
-            y_new = min(y_prev, y)
-            w_new = max(x_prev + w_prev, x + w) - x_new
-            h_new = max(y_prev + h_prev, y + h) - y_new
-            merged_boxes.append((x_new, y_new, w_new, h_new))
-        else:
-            merged_boxes.append((x, y, w, h))
-
-    # **7️⃣ Potong dan proses karakter**
-    segmented_chars = []
-    for (x, y, w, h) in merged_boxes:
-        char_segment = image[y:y+h, x:x+w]
-        char_segment = cv2.resize(char_segment, (128, 128), interpolation=cv2.INTER_AREA)
-        segmented_chars.append(char_segment)
-
-    return segmented_chars
-
 @app.post("/predict")
 async def predict(file: UploadFile = File(...)):
-    # Baca gambar dari file upload
-    image = Image.open(io.BytesIO(await file.read())).convert("L")
-    image = np.array(image)
-
-    # **Segmentasi huruf**
-    segmented_chars = preprocess_image(image)
-
-    # Jika tidak ada huruf terdeteksi
-    if not segmented_chars:
-        return {"prediction": "No characters detected"}
-
-    # **Prediksi untuk setiap karakter**
-    predictions = []
-    for char in segmented_chars:
-        char_norm = np.array(char) / 255.0  # Normalisasi
-        char_norm = char_norm.reshape(1, 128, 128, 1)  # Reshape untuk model
+    # **Baca gambar dari file upload tanpa pre-processing tambahan**
+    image = Image.open(io.BytesIO(await file.read())).convert("L")  # Convert ke grayscale
+    image = image.resize((128, 128))  # Resize ke 128x128 sesuai model
+    image = np.array(image) / 255.0  # Normalisasi
+    image = image.reshape(1, 128, 128, 1)  # Reshape untuk model
 
-        prediction = model.predict(char_norm)
-        predicted_label = labels[np.argmax(prediction)]
-        predictions.append(predicted_label)
+    # **Lakukan prediksi**
+    prediction = model.predict(image)
+    predicted_label = labels[np.argmax(prediction)]
 
-    return {"predictions": predictions}
+    return {"prediction": predicted_label}