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import streamlit as st
import pandas as pd
from st_aggrid import AgGrid, GridOptionsBuilder, GridUpdateMode
from test import predict_hoax, evaluate_model_performance
from load_model import load_model
from styles import COMMON_CSS
from google.cloud import storage
from io import StringIO
import os
from datetime import datetime
import pytz
# Set environment variable for Google Cloud credentials using secrets
with open("credentials.json", "w") as f:
f.write(st.secrets["GOOGLE_APPLICATION_CREDENTIALS"])
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "credentials.json"
def save_corrections_to_gcs(bucket_name, file_name, correction_data):
client = storage.Client()
bucket = client.bucket("dashboardhoax-bucket")
blob = bucket.blob("koreksi_pengguna_file.csv")
# Check if the blob (file) exists
if blob.exists():
# Download existing CSV from GCS
existing_data = blob.download_as_string().decode('utf-8')
existing_df = pd.read_csv(StringIO(existing_data))
else:
# Create a new DataFrame if the file does not exist
existing_df = pd.DataFrame(columns=['Timestamp', 'Label_id', 'Label', 'Title', 'Content', 'Fact', 'References', 'Classification', 'Datasource', 'Result_Detection', 'Result_Correction'])
# Append the new data to the existing data
new_data_df = pd.DataFrame(correction_data)
updated_df = pd.concat([existing_df, new_data_df], ignore_index=True)
# Convert the DataFrame back to CSV and upload
updated_csv_data = updated_df.to_csv(index=False)
blob.upload_from_string(updated_csv_data, content_type='text/csv')
def load_data(file):
return pd.read_csv(file)
def show_deteksi_upload():
st.markdown(COMMON_CSS, unsafe_allow_html=True)
st.markdown("<h6 style='font-size: 14px; margin-bottom: 0;'>Pilih Model</h6>", unsafe_allow_html=True)
selected_model = st.selectbox(
"",
[
"cahya/bert-base-indonesian-522M",
"indobenchmark/indobert-base-p2",
"indolem/indobert-base-uncased",
"mdhugol/indonesia-bert-sentiment-classification"
],
key="model_selector_upload"
)
tokenizer, model = load_model(selected_model)
st.markdown("<h6 style='font-size: 14px; margin-bottom: -200px;'>Unggah File Disini</h6>", unsafe_allow_html=True)
uploaded_file = st.file_uploader("", type="csv")
if 'df' not in st.session_state:
st.session_state.df = None
if uploaded_file is not None:
df = load_data(uploaded_file)
df.index = df.index + 1
st.markdown("<h6 style='font-size: 16px; margin-bottom: 0;'>Data yang Diunggah</h6>", unsafe_allow_html=True)
grid_options = GridOptionsBuilder.from_dataframe(df)
grid_options.configure_pagination(paginationAutoPageSize=False, paginationPageSize=10)
gridOptions = grid_options.build()
gridOptions['defaultColDef'] = {'fontSize': 10}
AgGrid(
df,
gridOptions=gridOptions,
update_mode=GridUpdateMode.VALUE_CHANGED,
use_container_width=True
)
if st.button("Deteksi", key="detect_upload"):
try:
df['Result_Detection'] = df.apply(lambda row: predict_hoax(row['Title'], row['Content']), axis=1)
df['Correction'] = False
st.session_state.df = df.copy()
except Exception as e:
st.error(f"Terjadi kesalahan saat deteksi: {e}")
if st.session_state.df is not None:
accuracy, precision, recall, f1 = evaluate_model_performance(st.session_state.df, tokenizer, model)
performance_text = (
f"*Performansi Model*\n\n"
f"*Accuracy:* {round(accuracy, 2)} "
f"*Precision:* {round(precision, 2)} "
f"*Recall:* {round(recall, 2)} "
f"*F1 Score:* {round(f1, 2)}"
)
st.success(performance_text)
st.markdown("<h6 style='font-size: 16px; margin-bottom: 0;'>Hasil Deteksi</h6>", unsafe_allow_html=True)
cols = ['Correction', 'Result_Detection'] + [col for col in st.session_state.df.columns if col not in ['Correction', 'Result_Detection', 'Label_id']]
df_reordered = st.session_state.df[cols]
grid_options = GridOptionsBuilder.from_dataframe(df_reordered)
grid_options.configure_pagination(paginationAutoPageSize=False, paginationPageSize=10)
grid_options.configure_default_column(editable=True, groupable=True)
gridOptions = grid_options.build()
grid_response = AgGrid(
st.session_state.df,
gridOptions=gridOptions,
update_mode=GridUpdateMode.VALUE_CHANGED
)
if grid_response['data'] is not None:
edited_df = pd.DataFrame(grid_response['data'])
st.session_state.df = edited_df.copy()
corrected_df = edited_df[edited_df['Correction']].copy()
edited_df['Result_Correction'] = edited_df.apply(lambda row:
'HOAX' if (row['Result_Detection'] == 'NON-HOAX' and row['Correction']) else
('NON-HOAX' if (row['Result_Detection'] == 'HOAX' and row['Correction']) else row['Result_Detection']),
axis=1
)
st.session_state.df = edited_df.copy()
if not corrected_df.empty:
corrected_df['Result_Correction'] = corrected_df.apply(lambda row:
'HOAX' if (row['Result_Detection'] == 'NON-HOAX' and row['Correction']) else
('NON-HOAX' if (row['Result_Detection'] == 'HOAX' and row['Correction']) else row['Result_Detection']),
axis=1
)
# Add Timestamp only for saving
wib = pytz.timezone('Asia/Jakarta')
corrected_df['Timestamp'] = datetime.now(wib).strftime('%Y-%m-%d %H:%M:%S')
cols = ['Timestamp', 'Result_Correction', 'Result_Detection', 'Label_id', 'Label', 'Title', 'Content', 'Fact', 'References', 'Classification', 'Datasource']
corrected_df_to_display = corrected_df[cols]
st.markdown("<h6 style='font-size: 16px; margin-bottom: 0;'>Data yang Dikoreksi</h6>", unsafe_allow_html=True)
st.dataframe(corrected_df_to_display, use_container_width=True, hide_index=True)
else:
st.write("Tidak ada data yang dikoreksi.")
if st.button("Simpan", key="corrected_data"):
if 'df' in st.session_state:
corrected_df = st.session_state.df[st.session_state.df['Correction']].copy()
wib = pytz.timezone('Asia/Jakarta')
corrected_df['Timestamp'] = datetime.now(wib).strftime('%Y-%m-%d %H:%M:%S')
corrected_df = corrected_df.drop(columns=['Correction'])
if not corrected_df.empty:
# Define GCS bucket and file name
bucket_name = "your-bucket-name"
file_name = "corrected_upload_data.csv"
# Convert DataFrame to list of dicts for GCS
correction_data = corrected_df.to_dict(orient='records')
# Save corrected data to GCS
save_corrections_to_gcs(bucket_name, file_name, correction_data)
st.success("Data telah disimpan.")
st.session_state.corrected_df = corrected_df
else:
st.warning("Tidak ada data yang dikoreksi untuk disimpan.")
else:
st.warning("Data deteksi tidak ditemukan.")
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