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import gradio as gr | |
from dotenv import load_dotenv | |
import os | |
import torch | |
import warnings | |
from PIL import Image | |
from util import file_helper | |
from inference.ocr import prepare_batch_for_inference | |
from inference.inference_handler import handle | |
os.system('sudo apt install -y -q tesseract-ocr') | |
os.system('sudo apt install -y -q libtesseract-dev') | |
load_dotenv() | |
def get_model(): | |
model_dir = "tmp" | |
model_filename= 'receipt.pth' | |
full_path = os.path.join(model_dir, model_filename) | |
if os.path.isfile(full_path): | |
return full_path | |
return file_helper.download_gdrive(os.getenv('MODEL_ID'), model_dir, model_filename) | |
def run_inference(model_path, images_path): | |
try: | |
inference_batch = prepare_batch_for_inference(images_path) | |
context = {"model_dir": model_path} | |
print('handle....') | |
handle(inference_batch,context) | |
except Exception as err: | |
print('err...', err) | |
def run(img_path): | |
print('img path: ', img_path) | |
model_path = get_model() | |
run_inference(model_path, [img_path]) | |
return Image.open(img_path) | |
gr.Markdown('Upload Foto Wajah Kamu (Pastikan hanya terdapat SATU wajah pada)') | |
iface = gr.Interface(fn=run, inputs=gr.Image(type="filepath"), outputs="image") | |
iface.launch() | |