main.py

from fastapi import FastAPI
import pickle
import uvicorn
import pandas as pd
import shutil
import cv2
import tensorflow as tf
import os
from flask import Flask, jsonify, request, flash, redirect, url_for
from pyngrok import ngrok
from fastapi import FastAPI, HTTPException, File, UploadFile, Request
from fastapi.staticfiles import StaticFiles
from fastapi.responses import JSONResponse


from file_processing import FileProcess
from get_load_data import GetLoadData
from data_preprocess import DataProcessing
from train_pred import TrainPred

app = FastAPI()
public_url = "https://lambang0902-test-space.hf.space"
app.mount("/static", StaticFiles(directory="static"), name="static")

# Tempat deklarasi variabel-variabel penting
filepath = ""
list_class = ['Diamond','Oblong','Oval','Round','Square','Triangle']
list_folder = ['Training', 'Testing']
face_crop_img = True
face_landmark_img = True
landmark_extraction_img = True
# -----------------------------------------------------

# -----------------------------------------------------
# Tempat deklarasi model dan sejenisnya
selected_model = tf.keras.models.load_model(f'models/fc_model_1.h5', compile=False)
# face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_alt2.xml')
# mp_drawing = mp.solutions.drawing_utils
# mp_face_mesh = mp.solutions.face_mesh
# drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)
# -----------------------------------------------------


# -----------------------------------------------------
# Tempat setting server
UPLOAD_FOLDER = './upload'
UPLOAD_MODEL = './models'
ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg','zip','h5'}
# -----------------------------------------------------
#Endpoints 
#Root endpoints
@app.get("/")
async def root():
    # Dapatkan URL publik dari ngrok
    ngrok_url = "Tidak Ada URL Publik (ngrok belum selesai memulai)"

    return {"message": "Hello, World!", "ngrok_url": ngrok_url}


#-----------------------------------------------------

data_processor = DataProcessing()
data_train_pred = TrainPred()

import random
def preprocessing(filepath):
    folder_path = './static/temporary'

    shutil.rmtree(folder_path)
    os.mkdir(folder_path)

    # data_processor.detect_landmark(data_processor.face_cropping_pred(filepath))
    data_processor.enhance_contrast_histeq(data_processor.face_cropping_pred(filepath))

    files = os.listdir(folder_path)
    index = 0
    for file_name in files:
        file_ext = os.path.splitext(file_name)[1]
        new_file_name = str(index) + "_" + str(random.randint(1, 100000)) + file_ext
        os.rename(os.path.join(folder_path, file_name), os.path.join(folder_path, new_file_name))
        index += 1

#     print("Tungu sampai selesaiii")

# train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1 / 255.)
# test_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1 / 255.)

# ## -------------------------------------------------------------------------
# ##                   API UNTUK MELAKUKAN PROSES PREDIKSI
# ## -------------------------------------------------------------------------

@app.post('/upload/file',tags=["Predicting"])
async def upload_file(picture: UploadFile):
    file_extension = picture.filename.split('.')[-1].lower()


    if file_extension not in ALLOWED_EXTENSIONS:
        raise HTTPException(status_code=400, detail='Invalid file extension')

    os.makedirs(UPLOAD_FOLDER, exist_ok=True)
    file_path = os.path.join(UPLOAD_FOLDER, secure_filename(picture.filename))
    with open(file_path, 'wb') as f:
        f.write(picture.file.read())
    try:
        processed_img = preprocessing(cv2.imread(file_path))
    except Exception as e:
        os.remove(file_path)
        raise HTTPException(status_code=500, detail=f'Error processing image: {str(e)}')

    return JSONResponse(content={'message': 'File successfully uploaded'}, status_code=200)

# @app.get('/get_images', tags=["Predicting"])
# def get_images():
#     folder_path = "./static/temporary"
#     files = [f for f in os.listdir(folder_path) if os.path.isfile(os.path.join(folder_path, f))]
#     urls = []
#     for i in range(0, 3):
#         url = f'{public_url}/static/temporary/{files[i]}'
#         urls.append(url)
#         bentuk, persentase = data_train_pred.prediction(selected_model)
#     return {'urls': urls, 'bentuk_wajah':bentuk[0], 'persen':persentase}