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import streamlit as st
import os
import time
from PIL import Image
import math
from streamlit_sparrow_labeling import st_sparrow_labeling
import requests
from config import settings
import json
class DataInference:
class Model:
# pageTitle = "Data Inference"
subheader_2 = "Upload"
initial_msg = "Please upload a file for inference"
upload_help = "Upload a file to extract data from it"
upload_button_text = "Upload"
upload_button_text_desc = "Choose a file"
extract_data = "Extract Data"
model_in_use = "donut"
img_file = None
def set_image_file(self, img_file):
st.session_state['img_file'] = img_file
def get_image_file(self):
if 'img_file' not in st.session_state:
return None
return st.session_state['img_file']
data_result = None
def set_data_result(self, data_result):
st.session_state['data_result'] = data_result
def get_data_result(self):
if 'data_result' not in st.session_state:
return None
return st.session_state['data_result']
def view(self, model, ui_width, device_type, device_width):
# st.title(model.pageTitle)
with st.sidebar:
st.markdown("---")
st.subheader(model.subheader_2)
with st.form("upload-form", clear_on_submit=True):
uploaded_file = st.file_uploader(model.upload_button_text_desc, accept_multiple_files=False,
type=['png', 'jpg', 'jpeg'],
help=model.upload_help, disabled=True)
submitted = st.form_submit_button(model.upload_button_text, disabled=True)
if submitted and uploaded_file is not None:
ret = self.upload_file(uploaded_file)
if ret is not False:
model.set_image_file(ret)
model.set_data_result(None)
if model.get_image_file() is not None:
doc_img = Image.open(model.get_image_file())
doc_height = doc_img.height
doc_width = doc_img.width
canvas_width, number_of_columns = self.canvas_available_width(ui_width, doc_width, device_type,
device_width)
if number_of_columns > 1:
col1, col2 = st.columns([number_of_columns, 10 - number_of_columns])
with col1:
self.render_doc(model, doc_img, canvas_width, doc_height, doc_width)
with col2:
self.render_results(model)
else:
self.render_doc(model, doc_img, canvas_width, doc_height, doc_width)
self.render_results(model)
else:
st.title(model.initial_msg)
def upload_file(self, uploaded_file):
timestamp = str(time.time())
timestamp = timestamp.replace(".", "")
file_name, file_extension = os.path.splitext(uploaded_file.name)
uploaded_file.name = file_name + "_" + timestamp + file_extension
if os.path.exists(os.path.join("docs/inference/", uploaded_file.name)):
st.write("File already exists")
return False
if len(uploaded_file.name) > 500:
st.write("File name too long")
return False
with open(os.path.join("docs/inference/", uploaded_file.name), "wb") as f:
f.write(uploaded_file.getbuffer())
st.success("File uploaded successfully")
return os.path.join("docs/inference/", uploaded_file.name)
def canvas_available_width(self, ui_width, doc_width, device_type, device_width):
doc_width_pct = (doc_width * 100) / ui_width
if doc_width_pct < 45:
canvas_width_pct = 37
elif doc_width_pct < 55:
canvas_width_pct = 49
else:
canvas_width_pct = 60
if ui_width > 700 and canvas_width_pct == 37 and device_type == "desktop":
return math.floor(canvas_width_pct * ui_width / 100), 4
elif ui_width > 700 and canvas_width_pct == 49 and device_type == "desktop":
return math.floor(canvas_width_pct * ui_width / 100), 5
elif ui_width > 700 and canvas_width_pct == 60 and device_type == "desktop":
return math.floor(canvas_width_pct * ui_width / 100), 6
else:
if device_type == "desktop":
ui_width = device_width - math.floor((device_width * 22) / 100)
elif device_type == "mobile":
ui_width = device_width - math.floor((device_width * 13) / 100)
return ui_width, 1
def render_doc(self, model, doc_img, canvas_width, doc_height, doc_width):
height = 1296
width = 864
annotations_json = {
"meta": {
"version": "v0.1",
"split": "train",
"image_id": 0,
"image_size": {
"width": doc_width,
"height": doc_height
}
},
"words": []
}
st_sparrow_labeling(
fill_color="rgba(0, 151, 255, 0.3)",
stroke_width=2,
stroke_color="rgba(0, 50, 255, 0.7)",
background_image=doc_img,
initial_rects=annotations_json,
height=height,
width=width,
drawing_mode="transform",
display_toolbar=False,
update_streamlit=False,
canvas_width=canvas_width,
doc_height=doc_height,
doc_width=doc_width,
image_rescale=True,
key="doc_annotation" + model.get_image_file()
)
def render_results(self, model):
with st.form(key="results_form"):
button_placeholder = st.empty()
submit = button_placeholder.form_submit_button(model.extract_data, type="primary")
if 'inference_error' in st.session_state:
st.error(st.session_state.inference_error)
del st.session_state.inference_error
if submit:
button_placeholder.empty()
api_url = "https://katanaml-org-sparrow-ml.hf.space/api-inference/v1/sparrow-ml/inference"
file_path = model.get_image_file()
with open(file_path, "rb") as file:
model_in_use = model.model_in_use
sparrow_key = settings.sparrow_key
# Prepare the payload
files = {
'file': (file.name, file, 'image/jpeg')
}
data = {
'image_url': '',
'model_in_use': model_in_use,
'sparrow_key': sparrow_key
}
with st.spinner("Extracting data from document..."):
response = requests.post(api_url, data=data, files=files, timeout=180)
if response.status_code != 200:
print('Request failed with status code:', response.status_code)
print('Response:', response.text)
st.session_state["inference_error"] = "Error extracting data from document"
st.experimental_rerun()
model.set_data_result(response.text)
# Display JSON data in Streamlit
st.markdown("---")
st.json(response.text)
# replace file extension to json
file_path = file_path.replace(".jpg", ".json")
with open(file_path, "w") as f:
json.dump(response.text, f, indent=2)
st.experimental_rerun()
else:
if model.get_data_result() is not None:
st.markdown("---")
st.json(model.get_data_result()) |