hpos-data/DownloadImagesandData.py

import os
import firebase_admin
from firebase_admin import credentials, firestore
import pandas as pd
from urllib.request import urlopen
import datetime
import tkinter as tk
from tkinter import filedialog
import openpyxl
import xlsxwriter

# Initialize Firebase Admin SDK
file_path = os.getcwd() + "\hpos-prod-firebase-adminsdk-bionp-5486f7becd.json"
cred = credentials.Certificate(fr'{file_path}')  # Replace with your own service account key path
 # Replace with your own service account key path
firebase_admin.initialize_app(cred)

# Get a reference to the Firestore database
db = firestore.client()

# Specify the collections
patient_collection = db.collection("patientData")
test_collection = db.collection("testData")

# Create a tkinter root window (hidden)
root = tk.Tk()
root.withdraw()

# Ask the user to select the base folder where data will be saved
base_output_folder = filedialog.askdirectory(title="Select Base Output Folder")

# Get user-defined date ranges
start_date = "2023-08-01"  # input("Please enter the start date (yyyy-mm-dd): ")
end_date = "2023-08-02"  # input("Please enter the end date (yyyy-mm-dd): ")

# Parse the date range to get a list of days
start_datetime = datetime.datetime.strptime(start_date, "%Y-%m-%d")
end_datetime = datetime.datetime.strptime(end_date, "%Y-%m-%d")
date_range = [start_datetime + datetime.timedelta(days=x) for x in range((end_datetime - start_datetime).days + 1)]

# Iterate through each day
for date in date_range:
    current_date = date.strftime("%Y-%m-%d")
    formatted_date = date.strftime("%d%b%Y")  # Format as "10Aug2023"
    output_folder = os.path.join(base_output_folder, formatted_date)
    os.makedirs(output_folder, exist_ok=True)

    # Create a subfolder for images
    images_folder = os.path.join(output_folder, "images")
    os.makedirs(images_folder, exist_ok=True)

    print(f"Processing data for {current_date}...")

    # Query Firestore for patient data
    query = patient_collection.where("createdAt", ">=", current_date).where("createdAt", "<", (date + datetime.timedelta(days=1)).strftime("%Y-%m-%d"))
    patient_docs = query.stream()

    # Initialize a list to hold the combined data
    data = []

    # Iterate through patient documents
    for patient_doc in patient_docs:
        patient_data = patient_doc.to_dict()
        patient_id = patient_data["_id"]

        # Query the document from testData collection based on the common _id
        test_docs = test_collection.where("_id", "==", patient_id).stream()

        # Iterate through test documents
        for test_doc in test_docs:
            test_data = test_doc.to_dict()

            # Combine the data from both collections into a single dictionary
            combined_data = {**patient_data, **test_data}

            # Access specific attributes from the combined data
            csv_path = combined_data.get("csvPath", "")
            if csv_path and not csv_path.startswith("http"):
                # Perform the desired action
                print("CSV path does not start with 'http'.")

            # Append the combined_data dictionary to the list
            data.append(combined_data)

            # Download images separately and store them in the images folder
            user_image_url = combined_data.get("userImageURL", "")
            if user_image_url and user_image_url.startswith("http"):
                image_data = urlopen(user_image_url).read()
                image_filename = f"{patient_id}.jpg"
                image_path = os.path.join(images_folder, image_filename)
                with open(image_path, "wb") as image_file:
                    image_file.write(image_data)

                # Add the image URL to the DataFrame
                combined_data["userImageUrl"] = os.path.relpath(image_path, output_folder)

    # Convert the data to a DataFrame
    df = pd.DataFrame(data)

    # Rename columns and select specific columns
    df.rename(columns={
        "name": "Name",
        "abhaId": "ABHA ID",
        "birthYear": "Age",
        "gender": "Gender",
        "category": "Category",
        "maritalStatus": "Marital Status",
        "careOf": "Care-of",
        "city": "Address",
        "district": "District",
        "state": "State",
        "pinCode": "Pincode",
        "phoneNumber": "Mobile Number",
        "bloodGroup": "Blood group",
        "result": "Test Result",
        "userImageUrl": "Image URL"  # Rename userImageUrl column
    }, inplace=True)

    selected_columns = [
        "Name", "ABHA ID", "Age", "Gender", "Category", "Marital Status",
        "Care-of", "Address", "District", "State", "Pincode", "Mobile Number",
        "Blood group", "Test Result", "Image URL"
    ]

    df = df[selected_columns]

    # Save the DataFrame to a CSV file
    csv_output_filename = f"data_{current_date}.csv"
    csv_output_path = os.path.join(output_folder, csv_output_filename)
    # df.to_csv(csv_output_path, index=False)

    print(f"CSV file created: '{csv_output_path}'")

    # Save the DataFrame to an Excel file with hyperlinks
    excel_output_filename = f"data_{current_date}.xlsx"
    excel_output_path = os.path.join(output_folder, excel_output_filename)


    # Create an Excel writer using XlsxWriter
    excel_writer = pd.ExcelWriter(excel_output_path, engine='xlsxwriter')

    df.to_excel(excel_writer, sheet_name='Data', index=False)

    workbook = excel_writer.book
    worksheet = excel_writer.sheets['Data']

    for idx, row in df.iterrows():
        image_url = f"external:{row['Image URL']}"
        worksheet.write_url(idx + 1, df.columns.get_loc('Image URL'), image_url, string=row['Image URL'])

    # Lock the worksheet with a password
    worksheet.protect('your_password_here')

    # Adjust column width to fit the longest value in each column
    for i, col in enumerate(df.columns):
        max_len = max(df[col].astype(str).apply(len).max(), len(col))  # max length in column
        worksheet.set_column(i, i, max_len)  # set column width

    # Close the Excel writer
    excel_writer._save()

    print(f"Excel file with hyperlinks and locked sheet created: '{excel_output_path}'")


# ... (previous code)

# Unlock the Excel sheet using openpyxl
with pd.ExcelWriter(excel_output_path, engine='openpyxl') as writer:
    writer.book = openpyxl.load_workbook(excel_output_path)
    writer.sheets = dict((ws.title, ws) for ws in writer.book.worksheets)
    sheet = writer.sheets['Data']
    sheet.protection.sheet = False  # Disable protection

    # Remove the password from the worksheet protection
    sheet.protection.password = None

print(f"Excel sheet unlocked: '{excel_output_path}'")

# Close the Firebase Admin SDK
firebase_admin.delete_app(firebase_admin.get_app())