from fastapi import FastAPI, File, UploadFile
from fastapi.responses import JSONResponse
from PIL import Image
import tensorflow as tf
import numpy as np
import requests
import io
import os

app = FastAPI()

MODEL_URL = "https://huggingface.co/faturbbx/htr/resolve/main/htr_crnn_final_model.keras"
MODEL_PATH = "htr_crnn_final_model.keras"

def download_model():
    if not os.path.exists(MODEL_PATH):
        print("📥 Downloading model...")
        r = requests.get(MODEL_URL)
        with open(MODEL_PATH, "wb") as f:
            f.write(r.content)
        print("✅ Model downloaded.")

download_model()
model = tf.keras.models.load_model(MODEL_PATH)

def preprocess(img):
    img = img.convert('L').resize((256, 64))
    img = np.array(img) / 255.0
    img = np.transpose(img, (1, 0))
    img = np.expand_dims(img, axis=(0, -1))
    return img

def decode_prediction(pred):
    # Ganti sesuai kebutuhan
    return "prediksi-hasil"

@app.post("/predict")
async def predict(image: UploadFile = File(...)):
    contents = await image.read()
    img = Image.open(io.BytesIO(contents))
    processed = preprocess(img)
    pred = model.predict(processed)
    result = decode_prediction(pred)
    return JSONResponse({"result": result})