from fastapi import FastAPI, UploadFile, File
import tensorflow as tf
import numpy as np
from PIL import Image
import cv2
import io
import uvicorn

app = FastAPI()

# Load model Keras
model = tf.keras.models.load_model("lontara_model_finetuning.keras")

# Label kelas sesuai model
labels = [
    "Tu", "He", "We", "No", "Mu", "Bu", "Ji", "Jo", "I", "Nro", "Cu", "Na", "Bo", "Yi", "Se", "Nyi",
    "So", "Wa", "Ko", "Ge", "E", "Yo", "Ngu", "Ra", "Wo", "Ta", "Pe", "Nra", "Da", "Ci", "Lo", "Nci",
    "U", "Ro", "Mo", "Nre", "Du", "Be", "Mpu", "Hu", "Ne", "Nyo", "Ncu", "Su", "Ju", "Gu", "Nu", "Di",
    "Nri", "Gi", "Co", "Nca", "Ri", "Si", "Ja", "Bi", "Ke", "Wu", "Nki", "Te", "Go", "Ya", "Nku", "Pu",
    "Nka", "Ba", "Mpe", "A", "Nya", "Me", "Nge", "Mpa", "Ma", "Mpi", "O", "Mi", "Re", "Po", "Ti", "Je",
    "Nco", "Pa", "Ho", "Nko", "Ce", "Li", "Nke", "Ru", "Ca", "Ke_", "Do", "Ga", "Mpo", "Nye", "Nru", "Nga",
    "Lu", "Pi", "Ku", "Ni", "Nce", "Le", "Ngo", "De", "Ki", "Wi", "Hi", "Ye", "Ngi", "Ka", "Nyu", "La",
    "Ha", "Sa"
]

@app.get("/")
def home():
    return {"message": "Aksara Lontara API is running"}

def preprocess_image(image: np.array):
    """Mengolah gambar untuk segmentasi huruf sebelum prediksi"""
    
    # **1️⃣ Edge Detection (Canny)**
    edges = cv2.Canny(image, 50, 150)  # Menangkap batas huruf
    
    # **2️⃣ Morphological Operations untuk membersihkan noise**
    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  # Sesuaikan berdasarkan ukuran huruf
    bounding_boxes = []

    for i in range(1, num_labels):  # Skip background
        x, y, w, h, area = stats[i]
        if area > min_area:  # Filter noise kecil
            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  # Jika jarak antar bounding box < 20 piksel, gabungkan

    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:
            # Gabungkan bounding box terakhir dengan yang sekarang
            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:
            # Tambahkan sebagai bounding box baru
            merged_boxes.append((x, y, w, h))

    # **7️⃣ Potong karakter hasil segmentasi**
    segmented_chars = []
    for (x, y, w, h) in merged_boxes:
        char_segment = image[y:y+h, x:x+w]  # Potong area karakter
        segmented_chars.append(char_segment)

    return segmented_chars

@app.post("/predict")
async def predict(file: UploadFile = File(...)):
    # Baca gambar
    image = Image.open(io.BytesIO(await file.read())).convert("L")  # Konversi ke grayscale
    image = np.array(image)

    # Preprocessing: Segmentasi huruf
    segmented_chars = preprocess_image(image)

    predictions = []
    for char_img in segmented_chars:
        # Resize ke ukuran yang diharapkan oleh model
        char_img_resized = cv2.resize(char_img, (128, 128))
        char_img_resized = char_img_resized / 255.0  # Normalisasi
        char_img_resized = char_img_resized.reshape(1, 128, 128, 1)  # Ubah ke bentuk input model

        # Prediksi
        prediction = model.predict(char_img_resized)
        label = labels[np.argmax(prediction)]

        predictions.append(label)

    return {"predictions": predictions}

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)