SalesAI / google_sheets.py
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import uuid
from google.oauth2 import service_account
from googleapiclient.discovery import build
from env_setup import config
import pandas as pd
SCOPES = ['https://www.googleapis.com/auth/spreadsheets']
def authenticate_google_account():
service_account_file = config["google_creds"]
if not service_account_file:
raise ValueError("Service account credentials path is missing in env_setup.py.")
return service_account.Credentials.from_service_account_file(service_account_file, scopes=SCOPES)
def store_data_in_sheet(sheet_id, chunks, summary, overall_sentiment):
creds = authenticate_google_account()
service = build('sheets', 'v4', credentials=creds)
sheet = service.spreadsheets()
call_id = str(uuid.uuid4())
print(f"Call ID: {call_id}")
values = []
if chunks:
first_chunk, first_sentiment, _ = chunks[0]
values.append([call_id, first_chunk, first_sentiment, summary, overall_sentiment])
for chunk, sentiment, _ in chunks[1:]:
values.append(["", chunk, sentiment, "", ""])
header = ["Call ID", "Chunk", "Sentiment", "Summary", "Overall Sentiment"]
all_values = [header] + values
body = {'values': all_values}
try:
result = sheet.values().append(
spreadsheetId=sheet_id,
range="Sheet1!A1",
valueInputOption="RAW",
body=body
).execute()
print(f"{result.get('updates').get('updatedCells')} cells updated in Google Sheets.")
except Exception as e:
print(f"Error updating Google Sheets: {e}")
def fetch_call_data(sheet_id, sheet_range="Sheet1!A1:E"):
"""
Fetches data from the specified Google Sheet and returns a pandas DataFrame.
:param sheet_id: The ID of the Google Sheet to fetch data from.
:param sheet_range: The range in A1 notation to fetch data from.
:return: pandas DataFrame with the sheet data.
"""
try:
# Authenticate and get credentials
creds = authenticate_google_account()
service = build('sheets', 'v4', credentials=creds)
sheet = service.spreadsheets()
# Fetch the data
result = sheet.values().get(
spreadsheetId=sheet_id,
range=sheet_range
).execute()
# Get the rows
rows = result.get("values", [])
# If rows exist, convert to DataFrame
if rows:
# Use the first row as column headers
headers = rows[0]
data = rows[1:]
# Create DataFrame
df = pd.DataFrame(data, columns=headers)
return df
else:
# Return an empty DataFrame if no data
return pd.DataFrame()
except Exception as e:
print(f"Error fetching data from Google Sheets: {e}")
# Return an empty DataFrame in case of error
return pd.DataFrame()