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import glob
import gradio as gr
import gspread
import numpy as np
import pandas as pd
import tqdm
from google.cloud import documentai_v1 as documentai
from google.oauth2 import service_account
credentials = None
def process_document_form_parser(credentials_file, images_folder):
"""Processes a document using the Form Parser."""
global credentials
credentials = service_account.Credentials.from_service_account_file(
credentials_file,
scopes=[
"https://www.googleapis.com/auth/cloud-platform",
"https://www.googleapis.com/auth/spreadsheets",
"https://www.googleapis.com/auth/drive",
],
)
project_id = "cru-ocr"
location = "us"
processor_id = "26630ebbc76345a1"
image_paths = glob.glob(f"{images_folder}/*.pdf")[:3]
all_fields = {
"Name": np.empty(len(image_paths), dtype=object),
"Phone": np.empty(len(image_paths), dtype=object),
"Email": np.empty(len(image_paths), dtype=object),
"Cadet": np.empty(len(image_paths), dtype=object),
"Greek or Going Greek": np.empty(len(image_paths), dtype=object),
"Transfer Student": np.empty(len(image_paths), dtype=object),
"Military Veteran": np.empty(len(image_paths), dtype=object),
"International Student": np.empty(len(image_paths), dtype=object),
"Res Hall": np.empty(len(image_paths), dtype=object),
"Room #": np.empty(len(image_paths), dtype=object),
"Off Campus": np.empty(len(image_paths), dtype=object),
"Fr": np.empty(len(image_paths), dtype=object),
"So": np.empty(len(image_paths), dtype=object),
"Jr": np.empty(len(image_paths), dtype=object),
"Sr": np.empty(len(image_paths), dtype=object),
"Grad Student": np.empty(len(image_paths), dtype=object),
"Male": np.empty(len(image_paths), dtype=object),
"Female": np.empty(len(image_paths), dtype=object),
"Non Binary": np.empty(len(image_paths), dtype=object),
}
client = documentai.DocumentProcessorServiceClient(credentials=credentials)
name = client.processor_path(project_id, location, processor_id)
for file_idx, file_path in enumerate(tqdm.tqdm(image_paths)):
with open(file_path, "rb") as image_file:
image_content = image_file.read()
raw_document = documentai.RawDocument(
content=image_content, mime_type="application/pdf"
)
request = documentai.ProcessRequest(name=name, raw_document=raw_document)
result = client.process_document(request=request)
document = result.document
for page in document.pages:
for form_field in page.form_fields:
field_name = (
form_field.field_name.text_anchor.content
if form_field.field_name
else "Unnamed Field"
)
field_value = (
form_field.field_value.text_anchor.content
if form_field.field_value
else "No Value"
)
field_name = field_name.strip().replace(":", "")
field_value = field_value.strip().replace(":", "")
if field_name == "Name" and "\n" in field_value:
field_value = " ".join(field_value.split("\n")[1:])
# Check if the field is a checkbox and set its value accordingly
if field_name in all_fields.keys():
if field_name == "Email":
# Replace "ut.edu" with "vt.edu" in email addresses
field_value = field_value.replace("ut.edu", "vt.edu")
# Make email addresses lowercase
field_value = field_value.lower()
# Remove spaces from email addresses
field_value = field_value.replace(" ", "")
if field_name == "Phone":
# Remove non-numeric characters from phone numbers
field_value = "".join(filter(str.isdigit, field_value))
# Parse checkboxes
if field_value == "☑":
field_value = "Yes"
# elif field_value == "☐":
# field_value = ""
all_fields[field_name][file_idx] = field_value
else:
print(
f"Unused field name: {field_name}, field value: {field_value}"
)
df = pd.DataFrame(all_fields, columns=all_fields.keys())
df["Year"] = df.apply(condense_year, axis=1)
df = df.drop(columns=["Fr", "So", "Jr", "Sr", "Grad Student"])
df = df.replace({"☐": "", None: ""})
return df
def condense_year(row):
"""
Handles logic to condense year values in the DataFrame row.
"""
years = ["Fr", "So", "Jr", "Sr", "Grad Student"]
year_vals = [row[year] for year in years]
# Box was checked
if "Yes" in year_vals:
return years[year_vals.index("Yes")]
# Four boxes were unchecked (meaning one box was likely checked and not recognized)
elif year_vals.count("☐") == 4:
for val in year_vals:
if not val or "☐" not in val:
return years[year_vals.index(val)]
return ""
def upload_to_google_sheets(df):
"""Uploads the edited DataFrame to a Google Sheet."""
global credentials
spreadsheet_name = "Cru Connect Cards"
worksheet_name = "Sheet1"
# Authenticate with Google Sheets
gc = gspread.authorize(credentials)
# Open the Google Sheet
try:
spreadsheet = gc.open(spreadsheet_name)
except gspread.SpreadsheetNotFound:
spreadsheet = gc.create(spreadsheet_name)
# Select the worksheet
try:
worksheet = spreadsheet.worksheet(worksheet_name)
except gspread.WorksheetNotFound:
worksheet = spreadsheet.add_worksheet(
title=worksheet_name, rows="100", cols="20"
)
# Clear the worksheet before uploading new data
worksheet.clear()
# Upload the DataFrame to the worksheet
worksheet.update([df.columns.values.tolist()] + df.values.tolist())
return f"Data uploaded successfully to {spreadsheet_name} - {worksheet_name}."
# Gradio Interface
def gradio_interface(credentials_file, images_folder):
return process_document_form_parser(credentials_file.name, images_folder)
def upload_handler(df_data):
df = pd.DataFrame(df_data)
result = upload_to_google_sheets(df)
return result
with gr.Blocks() as iface:
gr.Markdown("# Document Processing and Upload to Google Sheets")
credentials_file = gr.File(
label="Credentials File"#, value="cru-ocr-ee936d111292.json"
)
images_folder_path = gr.Textbox(label="Path to Images Folder", value="images_v2")
process_button = gr.Button("Process Documents")
output_dataframe = gr.Dataframe(label="Output", interactive=True)
upload_button = gr.Button("Upload to Google Sheets")
upload_status = gr.Textbox(label="Upload Status")
process_button.click(
fn=gradio_interface,
inputs=[credentials_file, images_folder_path],
outputs=[output_dataframe],
)
upload_button.click(
fn=upload_handler, inputs=[output_dataframe], outputs=[upload_status]
)
iface.launch()
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