Automated Financial Data Analysis Pipeline

from google.oauth2 import service_account
from googleapiclient.discovery import build
import pandas as pd
import requests
from io import StringIO
from io import BytesIO  # For handling Excel files

# Replace with the path to your service account credentials
SERVICE_ACCOUNT_FILE = "C:\\Users\\aly98\\Downloads\\myfirstproject-397107-12e2d187a167.json"
SCOPES = ['https://www.googleapis.com/auth/drive.readonly']

# Authenticate and create the service
credentials = service_account.Credentials.from_service_account_file(
    SERVICE_ACCOUNT_FILE, scopes=SCOPES)
service = build('drive', 'v3', credentials=credentials)

# Replace with your Google Drive folder ID
FOLDER_ID = 'put your folder id'

def list_files(service, folder_id):
    results = service.files().list(
        q=f"'{folder_id}' in parents",
        fields="files(id, name, mimeType)"
    ).execute()
    return results.get('files', [])

# Fetch the files
files = list_files(service, FOLDER_ID)

# List to store dataframes for file content
file_dataframes = []

for file in files:
    file_id = file['id']
    file_name = file['name']
    mime_type = file['mimeType']
    
    # Check if the file is Google Sheets, CSV, or Excel
    if mime_type == 'application/vnd.google-apps.spreadsheet':
        download_url = f"https://docs.google.com/spreadsheets/d/{file_id}/export?format=csv"
    elif mime_type == 'text/csv' or mime_type == 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet':
        download_url = f"https://drive.google.com/uc?export=download&id={file_id}"
    else:
        continue

    response = requests.get(download_url)
    if response.status_code == 200:
        if mime_type == 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet':
            df = pd.read_excel(BytesIO(response.content))
        else:
            df = pd.read_csv(StringIO(response.content.decode('utf-8')))
        file_dataframes.append(df)
    else:
        print(f"Error downloading file: {file_name}")

if file_dataframes:
    combined_df = pd.concat(file_dataframes, ignore_index=True)