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

# Initialize Firebase Admin SDK
cred = credentials.Certificate(r'C:\Users\smila\PycharmProjects\pythonProject\hpos-prod-firebase-adminsdk-bionp-5486f7becd.json')  # 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 images and data will be saved
base_output_folder = filedialog.askdirectory(title="Select Base Output Folder")

# Get user-defined date ranges
start_date = "2023-08-01"
end_date = "2023-08-10"

# 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")
    output_folder = os.path.join(base_output_folder, current_date)
    os.makedirs(output_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 specified 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"  # Use only patient ID as the filename
                image_path = os.path.join(output_folder, image_filename)
                with open(image_path, "wb") as image_file:
                    image_file.write(image_data)

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

    # Rename 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"
    }, inplace=True)

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

    # Check if selected columns are present in the DataFrame
    missing_columns = [col for col in selected_columns if col not in df.columns]
    if missing_columns:
        print(f"Missing columns: {missing_columns}")
    else:
        # Create a new DataFrame with selected columns
        df = df[selected_columns]

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

        print(f"Data and images for {current_date} saved to '{output_folder}'")

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