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Added error handling lines #12

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121 changes: 77 additions & 44 deletions src/utils/gcp_models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -499,46 +499,49 @@ def run_arima_plus_model():

response = requests.get(f'{os.environ.get("FLASK_SERVER_ADDR", "http://localhost:5000")}/run_arima_plus', verify=os.environ.get("CERTIFICATE_PATH", False), params=params)
if response.ok:
forecast_df = pd.DataFrame(response.json())
try:
forecast_df = pd.DataFrame(response.json())

# Separate historical and forecast data
forecast_only_df = forecast_df[forecast_df['time_series_type'] == 'forecast']
history_only_df = forecast_df[forecast_df['time_series_type'] == 'history']
# Separate historical and forecast data
forecast_only_df = forecast_df[forecast_df['time_series_type'] == 'forecast']
history_only_df = forecast_df[forecast_df['time_series_type'] == 'history']

# Rename columns to align with data
forecast_only_df = forecast_only_df.rename(columns={'time_series_timestamp': date_column, 'time_series_data': 'value'})
history_only_df = history_only_df.rename(columns={'time_series_timestamp': date_column, 'time_series_data': 'value'})
last_train_date = train_df[date_column].max()
forecast_only_df[date_column] = pd.date_range(start=last_train_date + pd.Timedelta(days=1), periods=len(forecast_only_df), freq='D')
# Rename columns to align with data
forecast_only_df = forecast_only_df.rename(columns={'time_series_timestamp': date_column, 'time_series_data': 'value'})
history_only_df = history_only_df.rename(columns={'time_series_timestamp': date_column, 'time_series_data': 'value'})
last_train_date = train_df[date_column].max()
forecast_only_df[date_column] = pd.date_range(start=last_train_date + pd.Timedelta(days=1), periods=len(forecast_only_df), freq='D')

st.subheader("Forecast Results:")
st.dataframe(forecast_only_df[[date_column, 'value']], use_container_width=True)
st.subheader("Forecast Results:")
st.dataframe(forecast_only_df[[date_column, 'value']], use_container_width=True)

st.divider()
plot_forecast(train_df, test_df, forecast_only_df, date_column, target_column)

st.divider()
trimmed_forecast_df = forecast_only_df.iloc[:len(test_df)].copy()
y_true = test_df[target_column].values
y_pred = trimmed_forecast_df['value'].values
calculate_accuracy(y_true,y_pred, flag=True)
st.divider()
decompose_time_series2(df, target_column, date_column, duplicated_flag)
st.divider()
st.divider()
plot_forecast(train_df, test_df, forecast_only_df, date_column, target_column)
st.divider()
trimmed_forecast_df = forecast_only_df.iloc[:len(test_df)].copy()
y_true = test_df[target_column].values
y_pred = trimmed_forecast_df['value'].values
calculate_accuracy(y_true,y_pred, flag=True)
st.divider()
decompose_time_series2(df, target_column, date_column, duplicated_flag)
st.divider()

# Download forecast as CSV
csv_forecast = forecast_only_df[[date_column, 'value']].to_csv(index=False)
st.download_button(
label="Download data as CSV",
data=csv_forecast,
file_name='predictions.csv',
mime='text/csv'
)
# Download forecast as CSV
csv_forecast = forecast_only_df[[date_column, 'value']].to_csv(index=False)
st.download_button(
label="Download data as CSV",
data=csv_forecast,
file_name='predictions.csv',
mime='text/csv'
)

# After the model finishes, delete the file from BigQuery and local directory
requests.delete(f'{os.environ.get("FLASK_SERVER_ADDR", "http://localhost:5000")}/delete_bigquery_table', verify=os.environ.get("CERTIFICATE_PATH", False), json={'train_file_path': train_file_path, 'user_email': st.query_params["user_email"]})
os.remove(train_file_path)
os.remove(clean_file_path)
# After the model finishes, delete the file from BigQuery and local directory
requests.delete(f'{os.environ.get("FLASK_SERVER_ADDR", "http://localhost:5000")}/delete_bigquery_table', verify=os.environ.get("CERTIFICATE_PATH", False), json={'train_file_path': train_file_path, 'user_email': st.query_params["user_email"]})
os.remove(train_file_path)
os.remove(clean_file_path)
except Exception as e:
st.error(f'Error forcasting using ARIMA Plus: {e}')
elif response.status_code == 401:
error_message = response.json().get('error')
st.error(error_message)
Expand Down Expand Up @@ -817,15 +820,34 @@ def run_times_fm():
times_fm_train_csv_buffer.seek(0)

# Send training data to the model
response = requests.post(f'{os.environ.get("FLASK_SERVER_ADDR", "http://localhost:5000")}/model', verify=os.environ.get("CERTIFICATE_PATH", False), json={
'csv_data': times_fm_train_csv_buffer.getvalue(),
'date_column': times_fm_date_column,
'target_column': times_fm_actual_column,
'user_email': st.query_params["user_email"]
})

print(f'[DEBUG] POST Request sent to {os.environ.get("FLASK_SERVER_ADDR", "http://localhost:5000")}/model')

try:
response = requests.post(f'{os.environ.get("FLASK_SERVER_ADDR", "http://localhost:5000")}/model', verify=os.environ.get("CERTIFICATE_PATH", False), json={
'csv_data': times_fm_train_csv_buffer.getvalue(),
'date_column': times_fm_date_column,
'target_column': times_fm_actual_column,
'user_email': st.query_params["user_email"]
})

print(f'[DEBUG] POST Request sent to {os.environ.get("FLASK_SERVER_ADDR", "http://localhost:5000")}/model')

# Check if the response is successful
response.raise_for_status() # Raise an error for bad responses (4xx or 5xx)

# Handle HTTP error (e.g., 404, 500)
except requests.exceptions.HTTPError as http_err:
print(f'[ERROR] HTTP error occurred while sending training data to the tinesfm: {http_err}')
# Handle connection errors (e.g., server not reachable)
except requests.exceptions.ConnectionError as conn_err:
print(f'[ERROR] Connection error occurred while sending training data to the tinesfm: {conn_err}')
# Handle timeout errors
except requests.exceptions.Timeout as timeout_err:
print(f'[ERROR] Timeout error occurred while sending training data to the tinesfm: {timeout_err}')
# Handle any other request-related exceptions
except requests.exceptions.RequestException as req_err:
print(f'[ERROR] An error occurred while sending training data to the tinesfm: {req_err}')
# Handle any other exceptions
except Exception as e:
print(f'[ERROR] An unexpected error occurred while sending training data to the tinesfm: {e}')

if response.status_code == 200:
query_string_params = {"user_email": st.query_params["user_email"]}
Expand Down Expand Up @@ -987,7 +1009,18 @@ def run_auto_ml():
}

# st.write("Fetching forecast data... you can deploy it and use it from History Page")

else:
error_message = f"Training automl failed with status code {response.status_code}."
if response.status_code == 400:
error_detail = response.json().get("error", "Invalid input data.")
error_message += f" Error details: {error_detail}"
elif response.status_code == 404:
error_message += " The model endpoint was not found."
elif response.status_code == 500:
error_message += " Internal server error occurred. Please try again later."
else:
error_message += " An unexpected error occurred."
st.error(f"[ERROR] {error_message}")

def fetch_get_data_from_flask(endpoint):
try:
Expand Down