update title

app.py

@@ -1,414 +1,414 @@
-#!/usr/bin/env python3
-"""
-JuaKazi Gender Bias Detection and Correction - Testing Interface
-User-friendly web UI for non-technical experts to test the bias detection and correction model
-"""
-
-import streamlit as st
-import pandas as pd
-import sys
-from pathlib import Path
-from io import StringIO
-
-# Add parent directory to path for imports
-BASE_DIR = Path(__file__).resolve().parent.parent
-sys.path.insert(0, str(BASE_DIR))
-
-from eval.bias_detector import BiasDetector
-from eval.models import Language
-
-# Page configuration
-st.set_page_config(
-    page_title="JuaKazi Bias Detection and Correction Testing",
-    layout="wide",
-    initial_sidebar_state="collapsed"
-)
-
-# Language mapping for dropdown
-LANGUAGE_MAP = {
-    "English": Language.ENGLISH,
-    "Swahili": Language.SWAHILI,
-    "French": Language.FRENCH,
-    "Gikuyu (Kikuyu)": Language.GIKUYU
-}
-
-LANGUAGE_CODES = {
-    "English": "en",
-    "Swahili": "sw",
-    "French": "fr",
-    "Gikuyu (Kikuyu)": "ki"
-}
-
-# Initialize detector with caching
-@st.cache_resource
-def get_detector():
-    """Initialize BiasDetector once and cache it"""
-    return BiasDetector()
-
-# Main title
-st.title("JuaKazi Gender Bias Detection and Correction - Testing Interface")
-st.markdown("**For non-technical experts:** Test individual texts or batch process files to detect and correct gender bias")
-st.markdown("---")
-
-# Initialize detector
-try:
-    detector = get_detector()
-except Exception as e:
-    st.error(f"Failed to initialize bias detector: {e}")
-    st.stop()
-
-# Create tabs
-tab1, tab2, tab3 = st.tabs(["Single Text Test", "Batch Testing", "Statistics"])
-
-# ===================================
-# TAB 1: SINGLE TEXT TESTING
-# ===================================
-with tab1:
-    st.header("Test Individual Text")
-    st.markdown("Enter text below and select a language to check for gender bias.")
-
-    # Language selector
-    col1, col2 = st.columns([1, 3])
-    with col1:
-        selected_lang_name = st.selectbox(
-            "Select Language",
-            list(LANGUAGE_MAP.keys()),
-            index=0,
-            help="Choose the language of your text"
-        )
-
-    language = LANGUAGE_MAP[selected_lang_name]
-
-    # Text input
-    text_input = st.text_area(
-        "Enter text to analyze:",
-        height=150,
-        placeholder="e.g., The chairman will lead the meeting today.",
-        help="Paste or type the text you want to check for gender bias"
-    )
-
-    # Detect button
-    col1, col2, col3 = st.columns([1, 2, 1])
-    with col1:
-        detect_button = st.button("Detect Bias", type="primary", use_container_width=True)
-
-    # Process detection
-    if detect_button:
-        if not text_input.strip():
-            st.warning("Please enter some text to analyze.")
-        else:
-            with st.spinner("Analyzing text..."):
-                try:
-                    result = detector.detect_bias(text_input, language)
-
-                    # Display results
-                    st.markdown("---")
-                    st.subheader("Detection Results")
-
-                    # Status indicator
-                    if result.has_bias_detected:
-                        st.error("**Bias Detected**")
-                    else:
-                        st.success("**No Bias Detected** - Text appears bias-free")
-
-                    # Create two columns for original vs corrected
-                    if result.has_bias_detected and result.detected_edits:
-                        col1, col2 = st.columns(2)
-
-                        with col1:
-                            st.markdown("**Original Text:**")
-                            st.info(text_input)
-
-                        with col2:
-                            st.markdown("**Corrected Text:**")
-                            corrected_text = text_input
-                            for edit in result.detected_edits:
-                                corrected_text = corrected_text.replace(edit["from"], edit["to"])
-                            st.success(corrected_text)
-
-                        # Show detected edits
-                        st.markdown("**Detected Edits:**")
-                        edits_data = []
-                        for i, edit in enumerate(result.detected_edits, 1):
-                            edits_data.append({
-                                "#": i,
-                                "Original": edit["from"],
-                                "Replacement": edit["to"],
-                                "Severity": edit.get("severity", "replace"),
-                                "Tags": edit.get("tags", "")
-                            })
-
-                        st.dataframe(pd.DataFrame(edits_data), use_container_width=True)
-
-                        # Additional metadata
-                        st.markdown("**Detection Metadata:**")
-                        meta_col1, meta_col2, meta_col3 = st.columns(3)
-                        with meta_col1:
-                            st.metric("Source", "Rules-based")
-                        with meta_col2:
-                            st.metric("Edits Found", len(result.detected_edits))
-                        with meta_col3:
-                            st.metric("Language", selected_lang_name)
-
-                except Exception as e:
-                    st.error(f"Error during detection: {e}")
-                    st.exception(e)
-
-# ===================================
-# TAB 2: BATCH TESTING
-# ===================================
-with tab2:
-    st.header("Batch Testing from CSV")
-    st.markdown("Upload a CSV file with columns: `id`, `language`, `text`")
-
-    # Show example format
-    with st.expander("CSV Format Example"):
-        example_df = pd.DataFrame({
-            "id": ["1", "2", "3"],
-            "language": ["en", "sw", "fr"],
-            "text": [
-                "The chairman will lead the meeting",
-                "Daktari anaangalia wagonjwa",
-                "Le président dirigera la réunion"
-            ]
-        })
-        st.dataframe(example_df, use_container_width=True)
-        st.markdown("**Language codes:** `en` (English), `sw` (Swahili), `fr` (French), `ki` (Gikuyu)")
-
-        # Download template
-        csv_template = example_df.to_csv(index=False)
-        st.download_button(
-            "Download Template CSV",
-            csv_template,
-            "batch_template.csv",
-            "text/csv",
-            help="Download this template and fill it with your data"
-        )
-
-    # File uploader
-    uploaded_file = st.file_uploader(
-        "Upload CSV File",
-        type=['csv'],
-        help="Max 1000 rows, 10MB file size limit"
-    )
-
-    if uploaded_file is not None:
-        try:
-            # Read CSV
-            df = pd.read_csv(uploaded_file)
-
-            # Validate columns
-            required_cols = ['id', 'language', 'text']
-            missing_cols = [col for col in required_cols if col not in df.columns]
-
-            if missing_cols:
-                st.error(f"Missing required columns: {', '.join(missing_cols)}")
-            else:
-                st.success(f"Loaded {len(df)} rows from CSV")
-
-                # Show preview
-                with st.expander("Preview Data (first 5 rows)"):
-                    st.dataframe(df.head(), use_container_width=True)
-
-                # Row limit check
-                if len(df) > 1000:
-                    st.warning("File has more than 1000 rows. Only first 1000 will be processed.")
-                    df = df.head(1000)
-
-                # Process button
-                col1, col2, col3 = st.columns([1, 2, 1])
-                with col1:
-                    process_button = st.button("Process All", type="primary", use_container_width=True)
-
-                if process_button:
-                    results = []
-                    progress_bar = st.progress(0)
-                    status_text = st.empty()
-
-                    # Language code mapping
-                    lang_code_map = {
-                        'en': Language.ENGLISH,
-                        'sw': Language.SWAHILI,
-                        'fr': Language.FRENCH,
-                        'ki': Language.GIKUYU
-                    }
-
-                    for idx, row in df.iterrows():
-                        status_text.text(f"Processing {idx + 1}/{len(df)}...")
-
-                        try:
-                            lang_code = row['language'].lower()
-                            if lang_code not in lang_code_map:
-                                results.append({
-                                    'id': row['id'],
-                                    'original_text': row['text'],
-                                    'corrected_text': row['text'],
-                                    'bias_detected': False,
-                                    'edits_count': 0,
-                                    'status': f'Invalid language code: {lang_code}'
-                                })
-                                continue
-
-                            language = lang_code_map[lang_code]
-                            result = detector.detect_bias(row['text'], language)
-
-                            corrected_text = row['text']
-                            if result.detected_edits:
-                                for edit in result.detected_edits:
-                                    corrected_text = corrected_text.replace(edit["from"], edit["to"])
-
-                            results.append({
-                                'id': row['id'],
-                                'language': row['language'],
-                                'original_text': row['text'],
-                                'corrected_text': corrected_text,
-                                'bias_detected': result.has_bias_detected,
-                                'edits_count': len(result.detected_edits),
-                                'edits': "; ".join([f"{e['from']}→{e['to']}" for e in result.detected_edits]),
-                                'status': 'Success'
-                            })
-
-                        except Exception as e:
-                            results.append({
-                                'id': row['id'],
-                                'original_text': row['text'],
-                                'corrected_text': row['text'],
-                                'bias_detected': False,
-                                'edits_count': 0,
-                                'status': f'Error: {str(e)}'
-                            })
-
-                        progress_bar.progress((idx + 1) / len(df))
-
-                    status_text.text("Processing complete!")
-
-                    # Display results
-                    results_df = pd.DataFrame(results)
-                    st.subheader("Batch Processing Results")
-
-                    # Summary metrics
-                    col1, col2, col3, col4 = st.columns(4)
-                    with col1:
-                        st.metric("Total Processed", len(results_df))
-                    with col2:
-                        bias_count = results_df['bias_detected'].sum()
-                        st.metric("Bias Detected", bias_count)
-                    with col3:
-                        success_count = (results_df['status'] == 'Success').sum()
-                        st.metric("Successful", success_count)
-                    with col4:
-                        total_edits = results_df['edits_count'].sum()
-                        st.metric("Total Edits", total_edits)
-
-                    # Results table
-                    st.dataframe(results_df, use_container_width=True)
-
-                    # Download results
-                    csv_output = results_df.to_csv(index=False)
-                    st.download_button(
-                        "Download Results as CSV",
-                        csv_output,
-                        "bias_detection_results.csv",
-                        "text/csv",
-                        help="Download the complete results with all columns"
-                    )
-
-        except Exception as e:
-            st.error(f"Error reading CSV file: {e}")
-            st.exception(e)
-
-# ===================================
-# TAB 3: STATISTICS
-# ===================================
-with tab3:
-    st.header("Language Statistics & System Information")
-
-    # System info
-    st.subheader("Detection System")
-    st.markdown("""
-    - **Engine:** Rules-based bias detection with lexicon matching
-    - **Approach:** Regular expression pattern matching with word boundaries
-    - **Case Handling:** Case-preserving replacement
-    - **Precision:** 1.000 (zero false positives) across all languages
-    """)
-
-    st.markdown("---")
-
-    # Language statistics
-    st.subheader("Supported Languages")
-
-    lang_stats = {
-        "Language": ["English", "Swahili", "French", "Gikuyu"],
-        "F1 Score": [0.786, 0.708, 0.571, 0.260],
-        "Precision": [1.000, 1.000, 1.000, 0.814],
-        "Recall": [0.647, 0.548, 0.400, 0.155],
-        "Lexicon Size": ["515 terms", "151 terms", "51 terms", "1,209 terms"],
-        "Ground Truth": ["67 samples", "64 samples", "51 samples", "5,254 samples"],
-        "Status": ["Production", "Foundation", "Beta", "Beta"]
-    }
-
-    stats_df = pd.DataFrame(lang_stats)
-    st.dataframe(stats_df, use_container_width=True, hide_index=True)
-
-    st.markdown("---")
-
-    # Bias categories
-    st.subheader("Detected Bias Categories")
-
-    categories = {
-        "Category": [
-            "Occupation",
-            "Pronoun Assumption",
-            "Generic Pronoun",
-            "Honorific",
-            "Morphology"
-        ],
-        "Description": [
-            "Gendered job titles (chairman, policeman)",
-            "Assumed pronouns (he/she when gender unknown)",
-            "Generic male pronouns (he as universal)",
-            "Gendered titles (Mr./Mrs., Mzee/Bi)",
-            "Gender markers in word structure (wa kike/wa kiume)"
-        ],
-        "Example": [
-            "chairman → chair",
-            "yeye ni → ni",
-            "his → their",
-            "Mzee → Mheshimiwa",
-            "wa kike → [removed]"
-        ]
-    }
-
-    categories_df = pd.DataFrame(categories)
-    st.dataframe(categories_df, use_container_width=True, hide_index=True)
-
-    st.markdown("---")
-
-    # Usage tips
-    st.subheader("Usage Tips")
-    st.markdown("""
-    **Best Practices:**
-    - Always review suggested corrections before accepting them
-    - Consider cultural and contextual appropriateness
-    - Test with various sentence structures
-    - Use batch processing for large datasets
-    - Export results for further analysis
-
-    **Limitations:**
-    - Detection is lexicon-based (limited to known patterns)
-    - Context-dependent bias may be missed
-    - Some languages have smaller lexicons (ongoing expansion)
-    - Review all ML-flagged items carefully
-    """)
-
-    st.markdown("---")
-
-    # Footer
-    st.markdown("""
-    <div style='text-align: center; color: gray; padding: 20px;'>
-    JuaKazi Gender Sensitization Engine | Version 0.3<br>
-    Perfect Precision: 1.000 (Zero False Positives)<br>
-    Culturally Adapted for African Languages
-    </div>
-    """, unsafe_allow_html=True)
-
+#!/usr/bin/env python3
+"""
+JuaKazi Gender Bias Detection and Correction - Testing Interface
+User-friendly web UI for non-technical experts to test the bias detection and correction model
+"""
+
+import streamlit as st
+import pandas as pd
+import sys
+from pathlib import Path
+from io import StringIO
+
+# Add parent directory to path for imports
+BASE_DIR = Path(__file__).resolve().parent.parent
+sys.path.insert(0, str(BASE_DIR))
+
+from eval.bias_detector import BiasDetector
+from eval.models import Language
+
+# Page configuration
+st.set_page_config(
+    page_title="JuaKazi Bias Detection and Correction Testing",
+    layout="wide",
+    initial_sidebar_state="collapsed"
+)
+
+# Language mapping for dropdown
+LANGUAGE_MAP = {
+    "English": Language.ENGLISH,
+    "Swahili": Language.SWAHILI,
+    "French": Language.FRENCH,
+    "Gikuyu (Kikuyu)": Language.GIKUYU
+}
+
+LANGUAGE_CODES = {
+    "English": "en",
+    "Swahili": "sw",
+    "French": "fr",
+    "Gikuyu (Kikuyu)": "ki"
+}
+
+# Initialize detector with caching
+@st.cache_resource
+def get_detector():
+    """Initialize BiasDetector once and cache it"""
+    return BiasDetector()
+
+# Main title
+st.title("JuaKazi Detection and Correction - Testing Interface")
+st.markdown("Test individual texts or batch process files to detect and correct gender bias")
+st.markdown("---")
+
+# Initialize detector
+try:
+    detector = get_detector()
+except Exception as e:
+    st.error(f"Failed to initialize bias detector: {e}")
+    st.stop()
+
+# Create tabs
+tab1, tab2, tab3 = st.tabs(["Single Text Test", "Batch Testing", "Statistics"])
+
+# ===================================
+# TAB 1: SINGLE TEXT TESTING
+# ===================================
+with tab1:
+    st.header("Test Individual Text")
+    st.markdown("Enter text below and select a language to check for gender bias.")
+
+    # Language selector
+    col1, col2 = st.columns([1, 3])
+    with col1:
+        selected_lang_name = st.selectbox(
+            "Select Language",
+            list(LANGUAGE_MAP.keys()),
+            index=0,
+            help="Choose the language of your text"
+        )
+
+    language = LANGUAGE_MAP[selected_lang_name]
+
+    # Text input
+    text_input = st.text_area(
+        "Enter text to analyze:",
+        height=150,
+        placeholder="e.g., The chairman will lead the meeting today.",
+        help="Paste or type the text you want to check for gender bias"
+    )
+
+    # Detect button
+    col1, col2, col3 = st.columns([1, 2, 1])
+    with col1:
+        detect_button = st.button("Detect Bias", type="primary", use_container_width=True)
+
+    # Process detection
+    if detect_button:
+        if not text_input.strip():
+            st.warning("Please enter some text to analyze.")
+        else:
+            with st.spinner("Analyzing text..."):
+                try:
+                    result = detector.detect_bias(text_input, language)
+
+                    # Display results
+                    st.markdown("---")
+                    st.subheader("Detection Results")
+
+                    # Status indicator
+                    if result.has_bias_detected:
+                        st.error("**Bias Detected**")
+                    else:
+                        st.success("**No Bias Detected** - Text appears bias-free")
+
+                    # Create two columns for original vs corrected
+                    if result.has_bias_detected and result.detected_edits:
+                        col1, col2 = st.columns(2)
+
+                        with col1:
+                            st.markdown("**Original Text:**")
+                            st.info(text_input)
+
+                        with col2:
+                            st.markdown("**Corrected Text:**")
+                            corrected_text = text_input
+                            for edit in result.detected_edits:
+                                corrected_text = corrected_text.replace(edit["from"], edit["to"])
+                            st.success(corrected_text)
+
+                        # Show detected edits
+                        st.markdown("**Detected Edits:**")
+                        edits_data = []
+                        for i, edit in enumerate(result.detected_edits, 1):
+                            edits_data.append({
+                                "#": i,
+                                "Original": edit["from"],
+                                "Replacement": edit["to"],
+                                "Severity": edit.get("severity", "replace"),
+                                "Tags": edit.get("tags", "")
+                            })
+
+                        st.dataframe(pd.DataFrame(edits_data), use_container_width=True)
+
+                        # Additional metadata
+                        st.markdown("**Detection Metadata:**")
+                        meta_col1, meta_col2, meta_col3 = st.columns(3)
+                        with meta_col1:
+                            st.metric("Source", "Rules-based")
+                        with meta_col2:
+                            st.metric("Edits Found", len(result.detected_edits))
+                        with meta_col3:
+                            st.metric("Language", selected_lang_name)
+
+                except Exception as e:
+                    st.error(f"Error during detection: {e}")
+                    st.exception(e)
+
+# ===================================
+# TAB 2: BATCH TESTING
+# ===================================
+with tab2:
+    st.header("Batch Testing from CSV")
+    st.markdown("Upload a CSV file with columns: `id`, `language`, `text`")
+
+    # Show example format
+    with st.expander("CSV Format Example"):
+        example_df = pd.DataFrame({
+            "id": ["1", "2", "3"],
+            "language": ["en", "sw", "fr"],
+            "text": [
+                "The chairman will lead the meeting",
+                "Daktari anaangalia wagonjwa",
+                "Le président dirigera la réunion"
+            ]
+        })
+        st.dataframe(example_df, use_container_width=True)
+        st.markdown("**Language codes:** `en` (English), `sw` (Swahili), `fr` (French), `ki` (Gikuyu)")
+
+        # Download template
+        csv_template = example_df.to_csv(index=False)
+        st.download_button(
+            "Download Template CSV",
+            csv_template,
+            "batch_template.csv",
+            "text/csv",
+            help="Download this template and fill it with your data"
+        )
+
+    # File uploader
+    uploaded_file = st.file_uploader(
+        "Upload CSV File",
+        type=['csv'],
+        help="Max 1000 rows, 10MB file size limit"
+    )
+
+    if uploaded_file is not None:
+        try:
+            # Read CSV
+            df = pd.read_csv(uploaded_file)
+
+            # Validate columns
+            required_cols = ['id', 'language', 'text']
+            missing_cols = [col for col in required_cols if col not in df.columns]
+
+            if missing_cols:
+                st.error(f"Missing required columns: {', '.join(missing_cols)}")
+            else:
+                st.success(f"Loaded {len(df)} rows from CSV")
+
+                # Show preview
+                with st.expander("Preview Data (first 5 rows)"):
+                    st.dataframe(df.head(), use_container_width=True)
+
+                # Row limit check
+                if len(df) > 1000:
+                    st.warning("File has more than 1000 rows. Only first 1000 will be processed.")
+                    df = df.head(1000)
+
+                # Process button
+                col1, col2, col3 = st.columns([1, 2, 1])
+                with col1:
+                    process_button = st.button("Process All", type="primary", use_container_width=True)
+
+                if process_button:
+                    results = []
+                    progress_bar = st.progress(0)
+                    status_text = st.empty()
+
+                    # Language code mapping
+                    lang_code_map = {
+                        'en': Language.ENGLISH,
+                        'sw': Language.SWAHILI,
+                        'fr': Language.FRENCH,
+                        'ki': Language.GIKUYU
+                    }
+
+                    for idx, row in df.iterrows():
+                        status_text.text(f"Processing {idx + 1}/{len(df)}...")
+
+                        try:
+                            lang_code = row['language'].lower()
+                            if lang_code not in lang_code_map:
+                                results.append({
+                                    'id': row['id'],
+                                    'original_text': row['text'],
+                                    'corrected_text': row['text'],
+                                    'bias_detected': False,
+                                    'edits_count': 0,
+                                    'status': f'Invalid language code: {lang_code}'
+                                })
+                                continue
+
+                            language = lang_code_map[lang_code]
+                            result = detector.detect_bias(row['text'], language)
+
+                            corrected_text = row['text']
+                            if result.detected_edits:
+                                for edit in result.detected_edits:
+                                    corrected_text = corrected_text.replace(edit["from"], edit["to"])
+
+                            results.append({
+                                'id': row['id'],
+                                'language': row['language'],
+                                'original_text': row['text'],
+                                'corrected_text': corrected_text,
+                                'bias_detected': result.has_bias_detected,
+                                'edits_count': len(result.detected_edits),
+                                'edits': "; ".join([f"{e['from']}→{e['to']}" for e in result.detected_edits]),
+                                'status': 'Success'
+                            })
+
+                        except Exception as e:
+                            results.append({
+                                'id': row['id'],
+                                'original_text': row['text'],
+                                'corrected_text': row['text'],
+                                'bias_detected': False,
+                                'edits_count': 0,
+                                'status': f'Error: {str(e)}'
+                            })
+
+                        progress_bar.progress((idx + 1) / len(df))
+
+                    status_text.text("Processing complete!")
+
+                    # Display results
+                    results_df = pd.DataFrame(results)
+                    st.subheader("Batch Processing Results")
+
+                    # Summary metrics
+                    col1, col2, col3, col4 = st.columns(4)
+                    with col1:
+                        st.metric("Total Processed", len(results_df))
+                    with col2:
+                        bias_count = results_df['bias_detected'].sum()
+                        st.metric("Bias Detected", bias_count)
+                    with col3:
+                        success_count = (results_df['status'] == 'Success').sum()
+                        st.metric("Successful", success_count)
+                    with col4:
+                        total_edits = results_df['edits_count'].sum()
+                        st.metric("Total Edits", total_edits)
+
+                    # Results table
+                    st.dataframe(results_df, use_container_width=True)
+
+                    # Download results
+                    csv_output = results_df.to_csv(index=False)
+                    st.download_button(
+                        "Download Results as CSV",
+                        csv_output,
+                        "bias_detection_results.csv",
+                        "text/csv",
+                        help="Download the complete results with all columns"
+                    )
+
+        except Exception as e:
+            st.error(f"Error reading CSV file: {e}")
+            st.exception(e)
+
+# ===================================
+# TAB 3: STATISTICS
+# ===================================
+with tab3:
+    st.header("Language Statistics & System Information")
+
+    # System info
+    st.subheader("Detection System")
+    st.markdown("""
+    - **Engine:** Rules-based bias detection with lexicon matching
+    - **Approach:** Regular expression pattern matching with word boundaries
+    - **Case Handling:** Case-preserving replacement
+    - **Precision:** 1.000 (zero false positives) across all languages
+    """)
+
+    st.markdown("---")
+
+    # Language statistics
+    st.subheader("Supported Languages")
+
+    lang_stats = {
+        "Language": ["English", "Swahili", "French", "Gikuyu"],
+        "F1 Score": [0.786, 0.708, 0.571, 0.260],
+        "Precision": [1.000, 1.000, 1.000, 0.814],
+        "Recall": [0.647, 0.548, 0.400, 0.155],
+        "Lexicon Size": ["515 terms", "151 terms", "51 terms", "1,209 terms"],
+        "Ground Truth": ["67 samples", "64 samples", "51 samples", "5,254 samples"],
+        "Status": ["Production", "Foundation", "Beta", "Beta"]
+    }
+
+    stats_df = pd.DataFrame(lang_stats)
+    st.dataframe(stats_df, use_container_width=True, hide_index=True)
+
+    st.markdown("---")
+
+    # Bias categories
+    st.subheader("Detected Bias Categories")
+
+    categories = {
+        "Category": [
+            "Occupation",
+            "Pronoun Assumption",
+            "Generic Pronoun",
+            "Honorific",
+            "Morphology"
+        ],
+        "Description": [
+            "Gendered job titles (chairman, policeman)",
+            "Assumed pronouns (he/she when gender unknown)",
+            "Generic male pronouns (he as universal)",
+            "Gendered titles (Mr./Mrs., Mzee/Bi)",
+            "Gender markers in word structure (wa kike/wa kiume)"
+        ],
+        "Example": [
+            "chairman → chair",
+            "yeye ni → ni",
+            "his → their",
+            "Mzee → Mheshimiwa",
+            "wa kike → [removed]"
+        ]
+    }
+
+    categories_df = pd.DataFrame(categories)
+    st.dataframe(categories_df, use_container_width=True, hide_index=True)
+
+    st.markdown("---")
+
+    # Usage tips
+    st.subheader("Usage Tips")
+    st.markdown("""
+    **Best Practices:**
+    - Always review suggested corrections before accepting them
+    - Consider cultural and contextual appropriateness
+    - Test with various sentence structures
+    - Use batch processing for large datasets
+    - Export results for further analysis
+
+    **Limitations:**
+    - Detection is lexicon-based (limited to known patterns)
+    - Context-dependent bias may be missed
+    - Some languages have smaller lexicons (ongoing expansion)
+    - Review all ML-flagged items carefully
+    """)
+
+    st.markdown("---")
+
+    # Footer
+    st.markdown("""
+    <div style='text-align: center; color: gray; padding: 20px;'>
+    JuaKazi Gender Sensitization Engine | Version 0.3<br>
+    Perfect Precision: 1.000 (Zero False Positives)<br>
+    Culturally Adapted for African Languages
+    </div>
+    """, unsafe_allow_html=True)
+