Update deteksi_content.py

deteksi_content.py

@@ -1,149 +1,149 @@
-import streamlit as st
-from datetime import datetime
-import pandas as pd
-from lime.lime_text import LimeTextExplainer
-from test import predict_hoax, predict_proba_for_lime
-import streamlit.components.v1 as components
-from load_model import load_model
-from st_aggrid import AgGrid, GridOptionsBuilder, GridUpdateMode
-from styles import COMMON_CSS
-from google.cloud import storage
-import os
-from io import StringIO
-
-# Set environment variable for Google Cloud credentials
-os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "D:\DashboardHoax\inbound-source-431806-g7-e49e388ce0be.json"
-
-def save_corrections_to_gcs(bucket_name, file_name, correction_data):
-    client = storage.Client()  # Uses the credentials set by the environment variable
-    bucket = client.bucket("dashboardhoax-bucket")
-    blob = bucket.blob("koreksi_pengguna_content.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', 'Title', 'Content', 'Prediction', '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 show_deteksi_kontengcs():
-    st.markdown(COMMON_CSS, unsafe_allow_html=True)
-
-    if 'correction' not in st.session_state:
-        st.session_state.correction = None
-    if 'detection_result' not in st.session_state:
-        st.session_state.detection_result = None
-    if 'lime_explanation' not in st.session_state:
-        st.session_state.lime_explanation = None
-    if 'headline' not in st.session_state:
-        st.session_state.headline = ""
-    if 'content' not in st.session_state:
-        st.session_state.content = ""
-    if 'is_correct' not in st.session_state:
-        st.session_state.is_correct = None
-
-    # Dropdown for selecting a model
-    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_content"
-    )
-
-    # Load the selected model
-    tokenizer, model = load_model(selected_model)
-
-    st.markdown("<h6 style='font-size: 14px; margin-bottom: 0;'>Masukkan Judul Berita :</h6>", unsafe_allow_html=True)
-    st.session_state.headline = st.text_input("", value=st.session_state.headline)
-
-    st.markdown("<h6 style='font-size: 14px; margin-bottom: 0;'>Masukkan Konten Berita :</h6>", unsafe_allow_html=True)
-    st.session_state.content = st.text_area("", value=st.session_state.content)
-
-    # Detection button
-    if st.button("Deteksi", key="detect_content"):
-        st.session_state.detection_result = predict_hoax(st.session_state.headline, st.session_state.content)
-        st.success(f"Prediksi: {st.session_state.detection_result}")
-
-        # Prepare the text for LIME
-        lime_texts = [f"{st.session_state.headline} [SEP] {st.session_state.content}"]
-
-        # Add a spinner and progress bar to indicate processing
-        with st.spinner("Sedang memproses LIME, harap tunggu..."):
-            # Explain the prediction
-            explainer = LimeTextExplainer(class_names=['NON-HOAX', 'HOAX'])
-            explanation = explainer.explain_instance(lime_texts[0], predict_proba_for_lime, num_features=5, num_samples=1000)
-
-            # Save the LIME explanation in session state
-            st.session_state.lime_explanation = explanation.as_html()
-
-    # Display the detection result and LIME explanation if available
-    if st.session_state.lime_explanation:
-        lime_html = st.session_state.lime_explanation
-
-        # Inject CSS for font size adjustment
-        lime_html = f"""
-        <style>
-        .lime-text-explanation, .lime-highlight, .lime-classification, 
-        .lime-text-explanation * {{
-            font-size: 14px !important;
-        }}
-        </style>
-        <div class="lime-text-explanation">
-            {lime_html}
-        </div>
-        """
-        components.html(lime_html, height=200, scrolling=True)
-
-    # Display a radio button asking if the detection result is correct
-    if st.session_state.detection_result is not None:
-        st.markdown("<h6 style='font-size: 16px; margin-bottom: -150px;'>Apakah hasil deteksi sudah benar?</h6>", unsafe_allow_html=True)
-        st.session_state.is_correct = st.radio("", ("Ya", "Tidak"))
-
-        if st.session_state.is_correct == "Ya":
-            st.success("Deteksi sudah benar.")
-        else:
-            # Determine the correction based on the prediction
-            st.session_state.correction = "HOAX" if st.session_state.detection_result == "NON-HOAX" else "NON-HOAX"
-
-            # Display the correction DataFrame
-            correction_data = [{
-                'Title': st.session_state.headline,
-                'Content': st.session_state.content,
-                'Prediction': st.session_state.detection_result,
-                'Correction': st.session_state.correction,
-                'Timestamp': datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-            }]
-
-            # Save button
-            if st.button("Simpan"):
-                # Save the correction data to GCS
-                save_corrections_to_gcs("your-bucket-name", "koreksi_pengguna.csv", correction_data)
-                
-                # Create a formatted string with CSS for alignment and multi-line content handling
-                formatted_text = f"""
-                <div style='font-size: 14px;'>
-                    <p style='margin: 0;'><span style='display: inline-block; width: 120px; font-weight: bold;'>Title</span> : <span style='white-space: pre-wrap;'>{st.session_state.headline}</span></p>
-                    <p style='margin: 0;'><span style='display: inline-block; width: 120px; font-weight: bold;'>Content</span> : <span style='white-space: pre-wrap;'>{st.session_state.content}</span></p>
-                    <p style='margin: 0;'><span style='display: inline-block; width: 120px; font-weight: bold;'>Prediction</span> : {st.session_state.detection_result}</p>
-                    <p style='margin: 0;'><span style='display: inline-block; width: 120px; font-weight: bold;'>Correction</span> : {st.session_state.correction}</p>
-                </div>
-                """
-                
-                # Display the correction as text
-                st.markdown(formatted_text, unsafe_allow_html=True)
-                st.success("Koreksi telah disimpan.")
+import streamlit as st
+from datetime import datetime
+import pandas as pd
+from lime.lime_text import LimeTextExplainer
+from test import predict_hoax, predict_proba_for_lime
+import streamlit.components.v1 as components
+from load_model import load_model
+from st_aggrid import AgGrid, GridOptionsBuilder, GridUpdateMode
+from styles import COMMON_CSS
+from google.cloud import storage
+import os
+from io import StringIO
+
+# Set environment variable for Google Cloud credentials
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "D:\DashboardHoax\inbound-source-431806-g7-e49e388ce0be.json"
+
+def save_corrections_to_gcs(bucket_name, file_name, correction_data):
+    client = storage.Client()  # Uses the credentials set by the environment variable
+    bucket = client.bucket("dashboardhoax-bucket")
+    blob = bucket.blob("koreksi_pengguna_content.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', 'Title', 'Content', 'Prediction', '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 show_deteksi_konten():
+    st.markdown(COMMON_CSS, unsafe_allow_html=True)
+
+    if 'correction' not in st.session_state:
+        st.session_state.correction = None
+    if 'detection_result' not in st.session_state:
+        st.session_state.detection_result = None
+    if 'lime_explanation' not in st.session_state:
+        st.session_state.lime_explanation = None
+    if 'headline' not in st.session_state:
+        st.session_state.headline = ""
+    if 'content' not in st.session_state:
+        st.session_state.content = ""
+    if 'is_correct' not in st.session_state:
+        st.session_state.is_correct = None
+
+    # Dropdown for selecting a model
+    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_content"
+    )
+
+    # Load the selected model
+    tokenizer, model = load_model(selected_model)
+
+    st.markdown("<h6 style='font-size: 14px; margin-bottom: 0;'>Masukkan Judul Berita :</h6>", unsafe_allow_html=True)
+    st.session_state.headline = st.text_input("", value=st.session_state.headline)
+
+    st.markdown("<h6 style='font-size: 14px; margin-bottom: 0;'>Masukkan Konten Berita :</h6>", unsafe_allow_html=True)
+    st.session_state.content = st.text_area("", value=st.session_state.content)
+
+    # Detection button
+    if st.button("Deteksi", key="detect_content"):
+        st.session_state.detection_result = predict_hoax(st.session_state.headline, st.session_state.content)
+        st.success(f"Prediksi: {st.session_state.detection_result}")
+
+        # Prepare the text for LIME
+        lime_texts = [f"{st.session_state.headline} [SEP] {st.session_state.content}"]
+
+        # Add a spinner and progress bar to indicate processing
+        with st.spinner("Sedang memproses LIME, harap tunggu..."):
+            # Explain the prediction
+            explainer = LimeTextExplainer(class_names=['NON-HOAX', 'HOAX'])
+            explanation = explainer.explain_instance(lime_texts[0], predict_proba_for_lime, num_features=5, num_samples=1000)
+
+            # Save the LIME explanation in session state
+            st.session_state.lime_explanation = explanation.as_html()
+
+    # Display the detection result and LIME explanation if available
+    if st.session_state.lime_explanation:
+        lime_html = st.session_state.lime_explanation
+
+        # Inject CSS for font size adjustment
+        lime_html = f"""
+        <style>
+        .lime-text-explanation, .lime-highlight, .lime-classification, 
+        .lime-text-explanation * {{
+            font-size: 14px !important;
+        }}
+        </style>
+        <div class="lime-text-explanation">
+            {lime_html}
+        </div>
+        """
+        components.html(lime_html, height=200, scrolling=True)
+
+    # Display a radio button asking if the detection result is correct
+    if st.session_state.detection_result is not None:
+        st.markdown("<h6 style='font-size: 16px; margin-bottom: -150px;'>Apakah hasil deteksi sudah benar?</h6>", unsafe_allow_html=True)
+        st.session_state.is_correct = st.radio("", ("Ya", "Tidak"))
+
+        if st.session_state.is_correct == "Ya":
+            st.success("Deteksi sudah benar.")
+        else:
+            # Determine the correction based on the prediction
+            st.session_state.correction = "HOAX" if st.session_state.detection_result == "NON-HOAX" else "NON-HOAX"
+
+            # Display the correction DataFrame
+            correction_data = [{
+                'Title': st.session_state.headline,
+                'Content': st.session_state.content,
+                'Prediction': st.session_state.detection_result,
+                'Correction': st.session_state.correction,
+                'Timestamp': datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+            }]
+
+            # Save button
+            if st.button("Simpan"):
+                # Save the correction data to GCS
+                save_corrections_to_gcs("your-bucket-name", "koreksi_pengguna.csv", correction_data)
+                
+                # Create a formatted string with CSS for alignment and multi-line content handling
+                formatted_text = f"""
+                <div style='font-size: 14px;'>
+                    <p style='margin: 0;'><span style='display: inline-block; width: 120px; font-weight: bold;'>Title</span> : <span style='white-space: pre-wrap;'>{st.session_state.headline}</span></p>
+                    <p style='margin: 0;'><span style='display: inline-block; width: 120px; font-weight: bold;'>Content</span> : <span style='white-space: pre-wrap;'>{st.session_state.content}</span></p>
+                    <p style='margin: 0;'><span style='display: inline-block; width: 120px; font-weight: bold;'>Prediction</span> : {st.session_state.detection_result}</p>
+                    <p style='margin: 0;'><span style='display: inline-block; width: 120px; font-weight: bold;'>Correction</span> : {st.session_state.correction}</p>
+                </div>
+                """
+                
+                # Display the correction as text
+                st.markdown(formatted_text, unsafe_allow_html=True)
+                st.success("Koreksi telah disimpan.")