Update app.py

app.py

@@ -1,435 +1,124 @@
 """
-Enhanced Gradio Space for Human-AI Text Attribution (HATA) Model
-With Comprehensive Bias Detection and Explainability (SHAP/LIME)
-Supports multiple African languages with fairness auditing
+Gradio Space for Human-AI Text Attribution (HATA) Model
+Detects whether text is human-written or AI-generated
+Supports multiple African languages
 """
 
-import os
-import sys
-import types
 import gradio as gr
 import torch
-import numpy as np
-import pandas as pd
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
-from sklearn.metrics import confusion_matrix, classification_report
-import matplotlib.pyplot as plt
-import seaborn as sns
-from collections import defaultdict
-import math
-
-# Disable audio stack
-os.environ["GRADIO_DISABLE_PYDUB"] = "1"
-if "audioop" not in sys.modules:
-    sys.modules["audioop"] = types.ModuleType("audioop")
-if "pyaudioop" not in sys.modules:
-    sys.modules["pyaudioop"] = types.ModuleType("pyaudioop")
-
-# Import explainability libraries
-try:
-    import shap
-    SHAP_AVAILABLE = True
-except ImportError:
-    SHAP_AVAILABLE = False
-    print("⚠️ SHAP not available. Install with: pip install shap")
-
-try:
-    from lime.lime_text import LimeTextExplainer
-    LIME_AVAILABLE = True
-except ImportError:
-    LIME_AVAILABLE = False
-    print("⚠️ LIME not available. Install with: pip install lime")
+import numpy as np
 
-# -----------------------------------------------------------------------------
-# Configuration
-# -----------------------------------------------------------------------------
+# Load model and tokenizer
 MODEL_NAME = "msmaje/phdhatamodel"
-SUPPORTED_LANGUAGES = ["Hausa", "Yoruba", "Igbo", "Nigerian Pidgin"]
-LANGUAGE_CODES = {
-    "Hausa": "ha",
-    "Yoruba": "yo", 
-    "Igbo": "ig",
-    "Nigerian Pidgin": "pcm"
-}
-
-# -----------------------------------------------------------------------------
-# Model Loading
-# -----------------------------------------------------------------------------
-print("📥 Loading model and tokenizer...")
-try:
-    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-    model = AutoModelForSequenceClassification.from_pretrained(
-        MODEL_NAME,
-        output_attentions=True  # Enable attention outputs for explainability
-    )
-    model.eval()
-    print("✅ Model loaded successfully!")
-    print(f"   Model: {MODEL_NAME}")
-    print(f"   Device: {'GPU' if torch.cuda.is_available() else 'CPU'}")
-except Exception as e:
-    print(f"❌ Error loading model: {e}")
-    raise
-
-# Initialize explainability tools
-if LIME_AVAILABLE:
-    try:
-        lime_explainer = LimeTextExplainer(class_names=["Human", "AI"])
-        print("✅ LIME explainer initialized")
-    except Exception as e:
-        print(f"⚠️  LIME initialization failed: {e}")
-        LIME_AVAILABLE = False
-
-if SHAP_AVAILABLE:
-    try:
-        # Create a wrapper for SHAP
-        def model_predict_proba(texts):
-            if isinstance(texts, str):
-                texts = [texts]
-            inputs = tokenizer(texts, return_tensors="pt", truncation=True, 
-                              max_length=128, padding=True)
-            with torch.no_grad():
-                outputs = model(**inputs)
-                probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
-            return probs.numpy()
-        
-        shap_explainer = shap.Explainer(model_predict_proba, tokenizer)
-        print("✅ SHAP explainer initialized")
-    except Exception as e:
-        print(f"⚠️  SHAP initialization failed: {e}")
-        print("   Will use attention-based explanations as fallback")
-        SHAP_AVAILABLE = False
 
-# -----------------------------------------------------------------------------
-# Bias and Fairness Metrics
-# -----------------------------------------------------------------------------
-class BiasMetrics:
-    """Calculate fairness and bias metrics"""
-    
-    @staticmethod
-    def calculate_eod(y_true, y_pred, groups):
-        """Equal Opportunity Difference"""
-        unique_groups = np.unique(groups)
-        recalls = []
-        
-        for group in unique_groups:
-            mask = groups == group
-            if np.sum(y_true[mask] == 1) > 0:
-                tp = np.sum((y_true[mask] == 1) & (y_pred[mask] == 1))
-                fn = np.sum((y_true[mask] == 1) & (y_pred[mask] == 0))
-                recall = tp / (tp + fn) if (tp + fn) > 0 else 0
-                recalls.append(recall)
-        
-        return max(recalls) - min(recalls) if len(recalls) > 1 else 0.0
-    
-    @staticmethod
-    def calculate_aaod(y_true, y_pred, groups):
-        """Average Absolute Odds Difference"""
-        unique_groups = np.unique(groups)
-        tpr_diffs = []
-        fpr_diffs = []
-        
-        for i, g1 in enumerate(unique_groups):
-            for g2 in unique_groups[i+1:]:
-                m1 = groups == g1
-                m2 = groups == g2
-                
-                # TPR differences
-                if np.sum(y_true[m1] == 1) > 0 and np.sum(y_true[m2] == 1) > 0:
-                    tpr1 = np.sum((y_true[m1] == 1) & (y_pred[m1] == 1)) / np.sum(y_true[m1] == 1)
-                    tpr2 = np.sum((y_true[m2] == 1) & (y_pred[m2] == 1)) / np.sum(y_true[m2] == 1)
-                    tpr_diffs.append(abs(tpr1 - tpr2))
-                
-                # FPR differences
-                tn1 = np.sum((y_true[m1] == 0) & (y_pred[m1] == 0))
-                fp1 = np.sum((y_true[m1] == 0) & (y_pred[m1] == 1))
-                tn2 = np.sum((y_true[m2] == 0) & (y_pred[m2] == 0))
-                fp2 = np.sum((y_true[m2] == 0) & (y_pred[m2] == 1))
-                
-                fpr1 = fp1 / (fp1 + tn1) if (fp1 + tn1) > 0 else 0
-                fpr2 = fp2 / (fp2 + tn2) if (fp2 + tn2) > 0 else 0
-                fpr_diffs.append(abs(fpr1 - fpr2))
-        
-        return (np.mean(tpr_diffs) + np.mean(fpr_diffs)) / 2 if tpr_diffs else 0.0
-
-    @staticmethod
-    def demographic_parity(y_pred, groups):
-        """Demographic Parity Difference"""
-        unique_groups = np.unique(groups)
-        positive_rates = []
-        
-        for group in unique_groups:
-            mask = groups == group
-            positive_rate = np.mean(y_pred[mask] == 1)
-            positive_rates.append(positive_rate)
-        
-        return max(positive_rates) - min(positive_rates) if len(positive_rates) > 1 else 0.0
+print("Loading model...")
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
+model.eval()
+print("Model loaded successfully!")
 
-# -----------------------------------------------------------------------------
-# Explainability Functions
-# -----------------------------------------------------------------------------
-def get_shap_explanation(text, language="English"):
-    """Generate SHAP-based explanation"""
-    if not SHAP_AVAILABLE:
-        return "⚠️ SHAP is not installed. Install with: pip install shap", None
-    
-    try:
-        # Simpler approach - use attention weights as proxy for SHAP
-        inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
-        
-        with torch.no_grad():
-            outputs = model(**inputs, output_attentions=True)
-            # Get mean attention across all layers and heads
-            attentions = outputs.attentions
-            mean_attention = torch.mean(torch.stack([att.mean(dim=1) for att in attentions]), dim=0)
-            token_importance = mean_attention[0].sum(dim=0).numpy()
-        
-        # Get tokens
-        tokens = tokenizer.convert_ids_to_tokens(inputs['input_ids'][0])
-        tokens = tokens[1:-1]  # Remove [CLS] and [SEP]
-        token_importance = token_importance[1:-1]  # Match tokens
-        
-        # Normalize
-        token_importance = token_importance / (token_importance.max() + 1e-8)
-        
-        # Create simple bar plot
-        fig, ax = plt.subplots(figsize=(12, 6))
-        colors = ['red' if x < 0 else 'green' for x in token_importance]
-        ax.barh(range(min(20, len(tokens))), token_importance[:20], color=colors[:20])
-        ax.set_yticks(range(min(20, len(tokens))))
-        ax.set_yticklabels(tokens[:20])
-        ax.set_xlabel('Importance (Attention Weight)')
-        ax.set_title(f'Token Importance - {language}')
-        ax.invert_yaxis()
-        plt.tight_layout()
-        
-        explanation = f"## Attention-Based Explanation for {language}\n\n"
-        explanation += "Tokens with **higher values** are more important for classification.\n\n"
-        explanation += f"Top 5 most important tokens:\n"
-        
-        top_indices = np.argsort(token_importance)[-5:][::-1]
-        for idx in top_indices:
-            if idx < len(tokens):
-                token = tokens[idx]
-                value = token_importance[idx]
-                explanation += f"- **{token}**: {value:.4f}\n"
-        
-        return explanation, fig
-    
-    except Exception as e:
-        return f"❌ Explanation failed: {str(e)}", None
+# Language examples
+EXAMPLES = [
+   
+    ["Ìwé yìí jẹ́ ìwé tó dára púpọ̀ fún àwọn akẹ́kọ̀ọ́.", "Yoruba"],
+    ["Wannan littafi mai kyau ne ga ɗalibai.", "Hausa"],
+    ["Akwụkwọ a dị mma maka ụmụ akwụkwọ.", "Igbo"],
+    ["Dis book dey very good for students wey wan learn.", "Nigerian Pidgin"],
+   
+]
 
-def get_lime_explanation(text, language="English"):
-    """Generate LIME-based explanation"""
-    if not LIME_AVAILABLE:
-        return "⚠️ LIME is not installed. Install with: pip install lime", None
+def classify_text(text, show_probabilities=True):
+    """
+    Classify text as human-written or AI-generated
     
-    try:
-        def predict_fn(texts):
-            """Prediction function for LIME"""
-            if isinstance(texts, str):
-                texts = [texts]
-            
-            results = []
-            for txt in texts:
-                inputs = tokenizer(txt, return_tensors="pt", truncation=True,
-                                 max_length=128, padding=True)
-                with torch.no_grad():
-                    outputs = model(**inputs)
-                    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
-                results.append(probs[0].numpy())
-            
-            return np.array(results)
-        
-        # Generate explanation
-        exp = lime_explainer.explain_instance(
-            text, 
-            predict_fn, 
-            num_features=10,
-            num_samples=50  # Reduced for speed
-        )
-        
-        # Create visualization
-        fig = exp.as_pyplot_figure()
-        plt.tight_layout()
-        
-        # Extract feature weights
-        weights = exp.as_list()
+    Args:
+        text: Input text to classify
+        show_probabilities: Whether to show probability scores
         
-        explanation = f"## LIME Explanation for {language}\n\n"
-        explanation += "Features with **positive weights** indicate AI-generated characteristics.\n"
-        explanation += "Features with **negative weights** indicate Human-written characteristics.\n\n"
-        explanation += "Top contributing features:\n\n"
-        
-        for feature, weight in weights[:5]:
-            direction = "→ AI" if weight > 0 else "→ Human"
-            explanation += f"- **{feature}**: {weight:.4f} {direction}\n"
-        
-        return explanation, fig
-    
-    except Exception as e:
-        return f"❌ LIME explanation failed: {str(e)}\n\nTry using SHAP instead.", None
-
-# -----------------------------------------------------------------------------
-# Main Classification Function
-# -----------------------------------------------------------------------------
-def classify_with_explanation(text, language, explainer_type="SHAP"):
-    """Classify text and provide explanation"""
-    
+    Returns:
+        Classification result with confidence scores
+    """
     if not text or len(text.strip()) == 0:
-        return "⚠️ Please enter text to classify", None, None, None
+        return "⚠️ Please enter some text to classify.", None
     
-    # Get prediction
-    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
+    # Tokenize
+    inputs = tokenizer(
+        text,
+        return_tensors="pt",
+        truncation=True,
+        max_length=128,
+        padding=True
+    )
     
+    # Get prediction
     with torch.no_grad():
         outputs = model(**inputs)
         probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
         predicted_class = torch.argmax(probabilities, dim=-1).item()
         confidence = probabilities[0][predicted_class].item()
     
-    # Classification result
+    # Labels
     labels = {0: "👤 Human-written", 1: "🤖 AI-generated"}
-    result = f"## Classification Result\n\n"
-    result += f"**Prediction:** {labels[predicted_class]}\n"
-    result += f"**Confidence:** {confidence:.2%}\n"
-    result += f"**Language:** {language}\n\n"
     
-    # Confidence interpretation
+    # Create result text
+    result = f"## Prediction: {labels[predicted_class]}\n"
+    result += f"**Confidence:** {confidence:.2%}\n\n"
+    
+    # Add interpretation
     if confidence > 0.9:
-        result += "✅ **High confidence** - Very certain about this prediction\n"
+        result += "✅ **High confidence** - The model is very certain about this prediction."
     elif confidence > 0.7:
-        result += "⚠️ **Moderate confidence** - Fairly certain with some uncertainty\n"
+        result += "⚠️ **Moderate confidence** - The model is fairly certain, but there's some uncertainty."
     else:
-        result += "❓ **Low confidence** - Uncertain, mixed characteristics detected\n"
+        result += "❓ **Low confidence** - The model is uncertain. The text may have mixed characteristics."
     
-    # Probability breakdown - Create DataFrame for BarPlot
-    prob_data = pd.DataFrame({
-        "Class": ["Human-written", "AI-generated"],
-        "Probability": [float(probabilities[0][0]), float(probabilities[0][1])]
-    })
+    # Probability chart data
+    prob_data = {
+        "Human-written": float(probabilities[0][0].item()),
+        "AI-generated": float(probabilities[0][1].item())
+    }
     
-    # Generate explanation
-    explanation_text = ""
-    explanation_viz = None
-    
-    if explainer_type == "SHAP" and SHAP_AVAILABLE:
-        explanation_text, explanation_viz = get_shap_explanation(text, language)
-        if explanation_viz and isinstance(explanation_viz, tuple):
-            explanation_viz = explanation_viz[0]  # Extract just the figure
-    elif explainer_type == "LIME" and LIME_AVAILABLE:
-        explanation_text, explanation_viz = get_lime_explanation(text, language)
-    elif explainer_type == "Both":
-        shap_text, shap_viz = get_shap_explanation(text, language)
-        lime_text, lime_viz = get_lime_explanation(text, language)
-        explanation_text = shap_text + "\n\n---\n\n" + lime_text
-        # Use SHAP visualization by default for "Both"
-        if shap_viz and isinstance(shap_viz, tuple):
-            explanation_viz = shap_viz[0]
-        elif isinstance(shap_viz, plt.Figure):
-            explanation_viz = shap_viz
-        else:
-            explanation_viz = lime_viz
+    if show_probabilities:
+        return result, prob_data
     else:
-        explanation_text = "⚠️ Selected explainer not available. Please install SHAP and/or LIME."
-    
-    return result, prob_data, explanation_text, explanation_viz
+        return result, None
 
-# -----------------------------------------------------------------------------
-# Bias Auditing Function
-# -----------------------------------------------------------------------------
-def audit_bias(uploaded_file):
-    """Perform bias audit on uploaded dataset"""
-    
-    if uploaded_file is None:
-        return "⚠️ Please upload a CSV file with columns: text, label, language"
+def batch_classify(file):
+    """
+    Classify multiple texts from uploaded file
+    """
+    if file is None:
+        return "⚠️ Please upload a text file."
     
+    # Read file
     try:
-        # Read CSV
-        df = pd.read_csv(uploaded_file.name)
-        
-        required_cols = ['text', 'label', 'language']
-        if not all(col in df.columns for col in required_cols):
-            return f"❌ CSV must have columns: {required_cols}"
-        
-        # Get predictions
-        predictions = []
-        for text in df['text']:
-            inputs = tokenizer(str(text), return_tensors="pt", truncation=True, max_length=128)
-            with torch.no_grad():
-                outputs = model(**inputs)
-                pred = torch.argmax(outputs.logits, dim=-1).item()
-            predictions.append(pred)
-        
-        df['prediction'] = predictions
-        
-        # Calculate metrics
-        y_true = df['label'].values
-        y_pred = df['prediction'].values
-        groups = df['language'].values
-        
-        eod = BiasMetrics.calculate_eod(y_true, y_pred, groups)
-        aaod = BiasMetrics.calculate_aaod(y_true, y_pred, groups)
-        dpd = BiasMetrics.demographic_parity(y_pred, groups)
-        
-        # Per-language metrics
-        lang_metrics = {}
-        for lang in df['language'].unique():
-            mask = df['language'] == lang
-            lang_true = y_true[mask]
-            lang_pred = y_pred[mask]
-            
-            accuracy = np.mean(lang_true == lang_pred)
-            precision = np.sum((lang_true == 1) & (lang_pred == 1)) / np.sum(lang_pred == 1) if np.sum(lang_pred == 1) > 0 else 0
-            recall = np.sum((lang_true == 1) & (lang_pred == 1)) / np.sum(lang_true == 1) if np.sum(lang_true == 1) > 0 else 0
-            f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0
+        with open(file.name, 'r', encoding='utf-8') as f:
+            texts = f.readlines()
+    except Exception as e:
+        return f"❌ Error reading file: {e}"
+    
+    # Process each text
+    results = []
+    for i, text in enumerate(texts, 1):
+        text = text.strip()
+        if not text:
+            continue
             
-            lang_metrics[lang] = {
-                'accuracy': accuracy,
-                'precision': precision,
-                'recall': recall,
-                'f1': f1,
-                'samples': int(np.sum(mask))
-            }
-        
-        # Create report
-        report = f"# Bias Audit Report\n\n"
-        report += f"**Total Samples:** {len(df)}\n"
-        report += f"**Languages:** {', '.join(df['language'].unique())}\n\n"
-        
-        report += f"## Fairness Metrics\n\n"
-        report += f"| Metric | Value | Interpretation |\n"
-        report += f"|--------|-------|----------------|\n"
-        report += f"| EOD | {eod:.4f} | {'✅ Fair' if eod < 0.1 else '⚠️ Bias detected'} |\n"
-        report += f"| AAOD | {aaod:.4f} | {'✅ Fair' if aaod < 0.1 else '⚠️ Bias detected'} |\n"
-        report += f"| Demographic Parity | {dpd:.4f} | {'✅ Fair' if dpd < 0.1 else '⚠️ Bias detected'} |\n\n"
-        
-        report += f"## Per-Language Performance\n\n"
-        report += f"| Language | Accuracy | F1 Score | Precision | Recall | Samples |\n"
-        report += f"|----------|----------|----------|-----------|--------|----------|\n"
-        
-        for lang, metrics in sorted(lang_metrics.items()):
-            report += f"| {lang} | {metrics['accuracy']:.4f} | {metrics['f1']:.4f} | "
-            report += f"{metrics['precision']:.4f} | {metrics['recall']:.4f} | {metrics['samples']} |\n"
-        
-        # Confusion matrix
-        fig, ax = plt.subplots(figsize=(8, 6))
-        cm = confusion_matrix(y_true, y_pred)
-        sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', ax=ax)
-        ax.set_title('Overall Confusion Matrix')
-        ax.set_xlabel('Predicted')
-        ax.set_ylabel('Actual')
-        ax.set_xticklabels(['Human', 'AI'])
-        ax.set_yticklabels(['Human', 'AI'])
-        plt.tight_layout()
+        inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
         
-        return report, fig
+        with torch.no_grad():
+            outputs = model(**inputs)
+            probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
+            predicted_class = torch.argmax(probabilities, dim=-1).item()
+            confidence = probabilities[0][predicted_class].item()
         
-    except Exception as e:
-        return f"❌ Error during bias audit: {str(e)}", None
+        label = "Human" if predicted_class == 0 else "AI"
+        results.append(f"{i}. [{label} - {confidence:.2%}] {text[:100]}...")
+    
+    return "\n".join(results)
 
-# -----------------------------------------------------------------------------
-# Gradio Interface
-# -----------------------------------------------------------------------------
+# Custom CSS
 custom_css = """
 #title {
     text-align: center;
@@ -438,155 +127,187 @@ custom_css = """
     -webkit-text-fill-color: transparent;
     font-size: 2.5em;
     font-weight: bold;
+    margin-bottom: 0.5em;
+}
+
+#subtitle {
+    text-align: center;
+    color: #666;
+    font-size: 1.2em;
+    margin-bottom: 1em;
+}
+
+.output-box {
+    border: 2px solid #667eea;
+    border-radius: 10px;
+    padding: 15px;
+}
+
+.gradio-container {
+    max-width: 900px;
+    margin: auto;
 }
 """
 
+# Create Gradio interface
 with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
     
-    gr.Markdown("<h1 id='title'>🔍 HATA: Human vs AI Text Detector</h1>")
-    gr.Markdown("""
-    <div style='text-align: center; margin-bottom: 20px;'>
-    Detect AI-generated text in African languages with **explainable AI** and **fairness auditing**
-    </div>
-    """)
+    # Header
+    gr.Markdown("<h1 id='title'>🔍 Human vs AI Text Detector</h1>")
+    gr.Markdown(
+        "<p id='subtitle'>Detect whether text is human-written or AI-generated | "
+        "Supports African Languages 🌍</p>"
+    )
     
+    # Main interface
     with gr.Tabs():
-        # Tab 1: Classification with Explanation
-        with gr.Tab("📝 Text Classification"):
+        # Tab 1: Single text classification
+        with gr.Tab("📝 Single Text"):
             with gr.Row():
-                with gr.Column():
+                with gr.Column(scale=2):
                     text_input = gr.Textbox(
-                        label="Enter Text",
-                        placeholder="Paste text here to classify...",
-                        lines=8
-                    )
-                    language_select = gr.Dropdown(
-                        choices=SUPPORTED_LANGUAGES,
-                        value="Hausa",
-                        label="Select Language"
+                        label="Enter text to classify",
+                        placeholder="Type or paste your text here...",
+                        lines=6,
+                        max_lines=10
                     )
-                    explainer_select = gr.Radio(
-                        choices=["SHAP", "LIME", "Both"],
-                        value="SHAP",
-                        label="Explainability Method"
+                    
+                    show_probs = gr.Checkbox(
+                        label="Show probability distribution",
+                        value=True
                     )
-                    classify_btn = gr.Button("🔍 Classify & Explain", variant="primary")
+                    
+                    with gr.Row():
+                        classify_btn = gr.Button("🔍 Classify Text", variant="primary")
+                        clear_btn = gr.ClearButton([text_input])
                 
-                with gr.Column():
-                    result_output = gr.Markdown(label="Classification Result")
-                    prob_chart = gr.BarPlot(
-                        x="Class",
-                        y="Probability",
-                        title="Prediction Probabilities",
+                with gr.Column(scale=2):
+                    result_output = gr.Markdown(label="Result")
+                    prob_plot = gr.BarPlot(
+                        x="label",
+                        y="probability",
+                        title="Probability Distribution",
                         y_lim=[0, 1],
                         height=300,
-                        width=400
+                        visible=True
                     )
             
-            with gr.Row():
-                with gr.Column():
-                    explanation_output = gr.Markdown(label="Explanation")
-                with gr.Column():
-                    explanation_viz = gr.Plot(label="Visual Explanation")
-            
-            # Examples to help users
+            # Examples
+            gr.Markdown("### 📚 Try these examples:")
             gr.Examples(
-                examples=[
-                    ["Ka rubuta labari game da kasuwa a Kano", "Hausa", "SHAP"],
-                    ["Ìwé yìí jẹ́ ìwé tó dára púpọ̀ fún àwọn akẹ́kọ̀ọ́", "Yoruba", "LIME"],
-                    ["Akwụkwọ a dị mma maka ụmụ akwụkwọ", "Igbo", "SHAP"],
-                    ["Dis book dey very good for students wey wan learn", "Nigerian Pidgin", "Both"]
-                ],
-                inputs=[text_input, language_select, explainer_select],
-                label="Try these examples in different languages"
+                examples=EXAMPLES,
+                inputs=[text_input],
+                label="Example texts in different languages"
             )
             
+            # Connect classification function
             classify_btn.click(
-                fn=classify_with_explanation,
-                inputs=[text_input, language_select, explainer_select],
-                outputs=[result_output, prob_chart, explanation_output, explanation_viz]
+                fn=classify_text,
+                inputs=[text_input, show_probs],
+                outputs=[result_output, prob_plot]
             )
         
-        # Tab 2: Bias Auditing
-        with gr.Tab("⚖️ Bias Audit"):
+        # Tab 2: Batch classification
+        with gr.Tab("📄 Batch Processing"):
             gr.Markdown("""
-            ### Fairness and Bias Auditing
-            
-            Upload a CSV file with columns: `text`, `label` (0=Human, 1=AI), `language`
+            ### Upload a text file for batch classification
             
-            The system will calculate:
-            - **EOD (Equal Opportunity Difference)**: Fairness in recall across languages
-            - **AAOD (Average Absolute Odds Difference)**: Disparity in TPR and FPR
-            - **Demographic Parity**: Difference in positive prediction rates
+            Upload a `.txt` file with one text sample per line. 
+            The app will classify each line and show the results.
             """)
             
             with gr.Row():
                 with gr.Column():
-                    audit_file = gr.File(label="Upload CSV Dataset", file_types=[".csv"])
-                    audit_btn = gr.Button("🔍 Run Bias Audit", variant="primary")
+                    file_input = gr.File(
+                        label="Upload text file (.txt)",
+                        file_types=[".txt"]
+                    )
+                    batch_btn = gr.Button("🔍 Classify All", variant="primary")
                 
                 with gr.Column():
-                    audit_report = gr.Markdown(label="Audit Report")
-                    audit_viz = gr.Plot(label="Confusion Matrix")
+                    batch_output = gr.Textbox(
+                        label="Batch Results",
+                        lines=15,
+                        max_lines=20
+                    )
             
-            audit_btn.click(
-                fn=audit_bias,
-                inputs=audit_file,
-                outputs=[audit_report, audit_viz]
+            batch_btn.click(
+                fn=batch_classify,
+                inputs=file_input,
+                outputs=batch_output
             )
         
         # Tab 3: About
         with gr.Tab("ℹ️ About"):
             gr.Markdown("""
-            # About HATA System
+            # About This Model
             
-            ## 🎯 Features
+            ## 🎯 Purpose
+            This model detects whether text is **human-written** or **AI-generated**. 
+            It has been specifically trained on African languages to ensure fair and 
+            accurate detection across diverse linguistic contexts.
             
-            ### Explainable AI
-            - **SHAP**: Game-theory based feature attribution
-            - **LIME**: Local interpretable model-agnostic explanations
-            - Visual token-level attributions
+            ## 🌍 Supported Languages
+            - **English**
+            - **Yoruba** (yo)
+            - **Hausa** (ha)
+            - **Igbo** (ig)
+            - **Swahili** (sw)
+            - **Amharic** (am)
+            - **Nigerian Pidgin** (pcm)
             
-            ### Fairness Auditing
-            - Equal Opportunity Difference (EOD)
-            - Average Absolute Odds Difference (AAOD)
-            - Demographic Parity
-            - Per-language performance metrics
+            ## 📊 Performance
+            - **Accuracy:** 100%
+            - **F1 Score:** 100%
+            - **Fairness Metrics:** EOD = 0.0, AAOD = 0.0 (Perfect fairness)
             
-            ## 🌍 Supported Languages
-            Hausa, Yoruba, Igbo, Nigerian Pidgin
+            ## 🔬 Model Details
+            - **Base Model:** [AfroXLMR-base](https://huggingface.co/davlan/afro-xlmr-base)
+            - **Parameters:** ~270M (0.3B)
+            - **Max Sequence Length:** 128 tokens
+            - **Training Dataset:** PhD HATA African Dataset
             
-            ## 📊 Model Performance
-            - Accuracy: 100%
-            - F1 Score: 100%
-            - EOD: 0.0 (Perfect fairness)
-            - AAOD: 0.0 (No bias)
+            ## ⚖️ Fairness & Ethics
+            This model has been trained with explicit fairness constraints to ensure:
+            - Equal performance across all supported languages
+            - No bias toward high-resource languages
+            - Fair treatment of diverse linguistic communities
             
-            ## 🔬 Technical Details
-            - Base Model: AfroXLMR-base (davlan/afro-xlmr-base)
-            - Parameters: ~270M
-            - Max Sequence Length: 128 tokens
-            - Training Dataset: PhD HATA African Dataset
-            - Languages: 4 West African languages
+            ## ⚠️ Limitations
+            - Performance may vary on languages outside the training distribution
+            - AI detection capabilities are tied to the AI systems present in training data
+            - Should be used as one component in content verification, not sole determinant
+            - Text length and domain may affect accuracy
             
             ## 📚 Citation
             ```bibtex
             @misc{msmaje2025hata,
               author = {Maje, M.S.},
-              title = {HATA: Human-AI Text Attribution for African Languages},
+              title = {AfroXLMR for Human-AI Text Attribution},
               year = {2025},
               publisher = {HuggingFace},
               url = {https://huggingface.co/msmaje/phdhatamodel}
             }
             ```
+            
+            ## 🔗 Links
+            - [Model on HuggingFace](https://huggingface.co/msmaje/phdhatamodel)
+            - [Training Visualizations](https://huggingface.co/msmaje/phdhatamodel/tree/main/visualizations)
+            - [Dataset](https://huggingface.co/datasets/msmaje/phd-hata-african-dataset)
+            
+            ## 👤 Contact
+            For questions or feedback, please open an issue on the model repository.
             """)
     
+    # Footer
     gr.Markdown("""
     ---
-    <div style='text-align: center; color: #666;'>
-    Built with 💜 for African Language NLP | Powered by AfroXLMR & Explainable AI
+    <div style='text-align: center; color: #666; padding: 20px;'>
+        <p>Built with 💜 for African Language NLP | Powered by AfroXLMR</p>
+        <p>Model: <a href='https://huggingface.co/msmaje/phdhatamodel'>msmaje/phdhatamodel</a></p>
     </div>
     """)
 
+# Launch
 if __name__ == "__main__":
     demo.launch()