182 lines
6.6 KiB
Python
182 lines
6.6 KiB
Python
import os
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import firebase_admin
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from firebase_admin import credentials, firestore
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import pandas as pd
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from urllib.request import urlopen
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import datetime
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import tkinter as tk
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from tkinter import filedialog
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import openpyxl
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import xlsxwriter
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# Initialize Firebase Admin SDK
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file_path = os.getcwd() + "\hpos-prod-firebase-adminsdk-bionp-5486f7becd.json"
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cred = credentials.Certificate(fr'{file_path}') # Replace with your own service account key path
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# Replace with your own service account key path
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firebase_admin.initialize_app(cred)
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# Get a reference to the Firestore database
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db = firestore.client()
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# Specify the collections
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patient_collection = db.collection("patientData")
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test_collection = db.collection("testData")
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# Create a tkinter root window (hidden)
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root = tk.Tk()
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root.withdraw()
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# Ask the user to select the base folder where data will be saved
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base_output_folder = filedialog.askdirectory(title="Select Base Output Folder")
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# Get user-defined date ranges
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start_date = "2023-08-01" # input("Please enter the start date (yyyy-mm-dd): ")
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end_date = "2023-08-02" # input("Please enter the end date (yyyy-mm-dd): ")
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# Parse the date range to get a list of days
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start_datetime = datetime.datetime.strptime(start_date, "%Y-%m-%d")
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end_datetime = datetime.datetime.strptime(end_date, "%Y-%m-%d")
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date_range = [start_datetime + datetime.timedelta(days=x) for x in range((end_datetime - start_datetime).days + 1)]
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# Iterate through each day
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for date in date_range:
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current_date = date.strftime("%Y-%m-%d")
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formatted_date = date.strftime("%d%b%Y") # Format as "10Aug2023"
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output_folder = os.path.join(base_output_folder, formatted_date)
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os.makedirs(output_folder, exist_ok=True)
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# Create a subfolder for images
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images_folder = os.path.join(output_folder, "images")
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os.makedirs(images_folder, exist_ok=True)
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print(f"Processing data for {current_date}...")
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# Query Firestore for patient data
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query = patient_collection.where("createdAt", ">=", current_date).where("createdAt", "<", (date + datetime.timedelta(days=1)).strftime("%Y-%m-%d"))
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patient_docs = query.stream()
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# Initialize a list to hold the combined data
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data = []
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# Iterate through patient documents
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for patient_doc in patient_docs:
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patient_data = patient_doc.to_dict()
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patient_id = patient_data["_id"]
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# Query the document from testData collection based on the common _id
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test_docs = test_collection.where("_id", "==", patient_id).stream()
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# Iterate through test documents
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for test_doc in test_docs:
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test_data = test_doc.to_dict()
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# Combine the data from both collections into a single dictionary
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combined_data = {**patient_data, **test_data}
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# Access specific attributes from the combined data
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csv_path = combined_data.get("csvPath", "")
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if csv_path and not csv_path.startswith("http"):
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# Perform the desired action
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print("CSV path does not start with 'http'.")
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# Append the combined_data dictionary to the list
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data.append(combined_data)
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# Download images separately and store them in the images folder
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user_image_url = combined_data.get("userImageURL", "")
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if user_image_url and user_image_url.startswith("http"):
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image_data = urlopen(user_image_url).read()
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image_filename = f"{patient_id}.jpg"
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image_path = os.path.join(images_folder, image_filename)
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with open(image_path, "wb") as image_file:
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image_file.write(image_data)
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# Add the image URL to the DataFrame
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combined_data["userImageUrl"] = os.path.relpath(image_path, output_folder)
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# Convert the data to a DataFrame
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df = pd.DataFrame(data)
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# Rename columns and select specific columns
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df.rename(columns={
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"name": "Name",
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"abhaId": "ABHA ID",
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"birthYear": "Age",
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"gender": "Gender",
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"category": "Category",
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"maritalStatus": "Marital Status",
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"careOf": "Care-of",
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"city": "Address",
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"district": "District",
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"state": "State",
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"pinCode": "Pincode",
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"phoneNumber": "Mobile Number",
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"bloodGroup": "Blood group",
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"result": "Test Result",
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"userImageUrl": "Image URL" # Rename userImageUrl column
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}, inplace=True)
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selected_columns = [
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"Name", "ABHA ID", "Age", "Gender", "Category", "Marital Status",
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"Care-of", "Address", "District", "State", "Pincode", "Mobile Number",
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"Blood group", "Test Result", "Image URL"
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]
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df = df[selected_columns]
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# Save the DataFrame to a CSV file
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csv_output_filename = f"data_{current_date}.csv"
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csv_output_path = os.path.join(output_folder, csv_output_filename)
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# df.to_csv(csv_output_path, index=False)
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print(f"CSV file created: '{csv_output_path}'")
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# Save the DataFrame to an Excel file with hyperlinks
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excel_output_filename = f"data_{current_date}.xlsx"
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excel_output_path = os.path.join(output_folder, excel_output_filename)
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# Create an Excel writer using XlsxWriter
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excel_writer = pd.ExcelWriter(excel_output_path, engine='xlsxwriter')
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df.to_excel(excel_writer, sheet_name='Data', index=False)
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workbook = excel_writer.book
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worksheet = excel_writer.sheets['Data']
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for idx, row in df.iterrows():
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image_url = f"external:{row['Image URL']}"
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worksheet.write_url(idx + 1, df.columns.get_loc('Image URL'), image_url, string=row['Image URL'])
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# Lock the worksheet with a password
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worksheet.protect('your_password_here')
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# Adjust column width to fit the longest value in each column
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for i, col in enumerate(df.columns):
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max_len = max(df[col].astype(str).apply(len).max(), len(col)) # max length in column
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worksheet.set_column(i, i, max_len) # set column width
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# Close the Excel writer
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excel_writer._save()
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print(f"Excel file with hyperlinks and locked sheet created: '{excel_output_path}'")
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# ... (previous code)
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# Unlock the Excel sheet using openpyxl
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with pd.ExcelWriter(excel_output_path, engine='openpyxl') as writer:
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writer.book = openpyxl.load_workbook(excel_output_path)
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writer.sheets = dict((ws.title, ws) for ws in writer.book.worksheets)
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sheet = writer.sheets['Data']
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sheet.protection.sheet = False # Disable protection
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# Remove the password from the worksheet protection
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sheet.protection.password = None
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print(f"Excel sheet unlocked: '{excel_output_path}'")
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# Close the Firebase Admin SDK
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firebase_admin.delete_app(firebase_admin.get_app())
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