Update app.py

app.py

@@ -1,159 +1,527 @@
 """
-Gradio Space for Human-AI Text Attribution (HATA) Model
-Detects whether text is human-written or AI-generated
-Supports multiple African languages
+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
 """
 
-# --- Hard-disable Gradio's audio stack under Python 3.13 ---
 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 PyDub so Gradio won't attempt audio features
+# Disable audio stack
 os.environ["GRADIO_DISABLE_PYDUB"] = "1"
-
-# Provide stubs for missing modules
 if "audioop" not in sys.modules:
     sys.modules["audioop"] = types.ModuleType("audioop")
 if "pyaudioop" not in sys.modules:
     sys.modules["pyaudioop"] = types.ModuleType("pyaudioop")
 
-# Safe to import Gradio and other dependencies now
-import gradio as gr
-import requests
-import math
-from langdetect import detect
+# 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")
 
 # -----------------------------------------------------------------------------
 # Configuration
 # -----------------------------------------------------------------------------
-HF_API_URL = "https://api-inference.huggingface.co/models/YOUR_USERNAME/YOUR_MODEL"
-HF_TOKEN = os.getenv("HF_TOKEN")
-
-if HF_TOKEN is None:
-    raise ValueError("HF_TOKEN environment variable not set!")
-
-HEADERS = {
-    "Authorization": f"Bearer {HF_TOKEN}",
-    "Content-Type": "application/json"
+MODEL_NAME = "msmaje/phdhatamodel"
+SUPPORTED_LANGUAGES = ["Hausa", "Yoruba", "Igbo", "Swahili", "Amharic", "Nigerian Pidgin"]
+LANGUAGE_CODES = {
+    "Hausa": "ha",
+    "Yoruba": "yo", 
+    "Igbo": "ig",
+    "Swahili": "sw",
+    "Amharic": "am",
+    "Nigerian Pidgin": "pcm"
 }
 
 # -----------------------------------------------------------------------------
-# Utility Functions
+# Model Loading
 # -----------------------------------------------------------------------------
-def entropy(probs):
-    """Shannon entropy as epistemic uncertainty indicator."""
-    return -sum(p * math.log2(p) for p in probs if p > 0)
-
-def normalize_labels(hf_output):
-    """
-    Normalize Hugging Face output into a stable schema.
-    Expected HF format:
-    [
-      {"label": "HUMAN", "score": 0.73},
-      {"label": "AI", "score": 0.27}
-    ]
-    """
-    result = {item["label"].lower(): float(item["score"]) for item in hf_output}
-    human_p = result.get("human", 0.0)
-    ai_p = result.get("ai", 0.0)
-    return human_p, ai_p
-
-def hf_inference(text):
-    payload = {"inputs": text}
-    r = requests.post(HF_API_URL, headers=HEADERS, json=payload, timeout=30)
-    r.raise_for_status()
-    return r.json()
+print("Loading model and tokenizer...")
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
+model.eval()
+print("✅ Model loaded successfully!")
+
+# Initialize explainability tools
+if LIME_AVAILABLE:
+    lime_explainer = LimeTextExplainer(class_names=["Human", "AI"])
+
+if SHAP_AVAILABLE:
+    # Create a wrapper for SHAP
+    def model_predict_proba(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)
 
 # -----------------------------------------------------------------------------
-# Gradio Prediction Function
+# Bias and Fairness Metrics
 # -----------------------------------------------------------------------------
-def analyze_text(text):
-    text = text.strip()
-    if not text:
-        return {"error": "Empty input"}
+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
 
-    # 1. Language detection
+    @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
+
+# -----------------------------------------------------------------------------
+# 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:
-        language = detect(text)
-    except Exception:
-        language = "unknown"
-
-    # 2. Hugging Face inference
-    hf_raw = hf_inference(text)
-    if not isinstance(hf_raw, list):
-        return {"error": "Unexpected model response", "raw": hf_raw}
-
-    human_p, ai_p = normalize_labels(hf_raw)
-
-    # 3. Decision
-    label = "Human" if human_p >= ai_p else "Machine"
-    confidence = max(human_p, ai_p)
-
-    # 4. Epistemic uncertainty
-    H = entropy([human_p, ai_p])
-
-    # 5. Explainability placeholder
-    explainability_stub = {
-        "method": "pending",
-        "note": (
-            "This model endpoint does not natively expose SHAP/LIME. "
-            "Post-hoc explainability must be computed locally using a "
-            "replicated model or proxy explainer."
-        ),
-        "token_attributions": []
-    }
+        # Get SHAP values
+        shap_values = shap_explainer([text])
+        
+        # Create visualization
+        fig, ax = plt.subplots(figsize=(12, 6))
+        shap.plots.text(shap_values[0], display=False)
+        plt.tight_layout()
+        
+        # Extract token attributions
+        tokens = tokenizer.tokenize(text)[:20]  # Limit to first 20 tokens
+        values = shap_values.values[0][:len(tokens), 1]  # AI class
+        
+        attribution_data = {
+            "Token": tokens,
+            "Attribution": values.tolist()
+        }
+        
+        explanation = f"## SHAP Explanation for {language}\n\n"
+        explanation += "Tokens with **positive values** push toward AI-generated classification.\n"
+        explanation += "Tokens with **negative values** push toward Human-written classification.\n\n"
+        explanation += f"Top 5 most influential tokens:\n"
+        
+        top_indices = np.argsort(np.abs(values))[-5:][::-1]
+        for idx in top_indices:
+            token = tokens[idx]
+            value = values[idx]
+            direction = "→ AI" if value > 0 else "→ Human"
+            explanation += f"- **{token}**: {value:.4f} {direction}\n"
+        
+        return explanation, (fig, attribution_data)
+    
+    except Exception as e:
+        return f"❌ SHAP explanation failed: {str(e)}", None
 
-    # 6. Fairness metadata
-    fairness_context = {
-        "language": language,
-        "human_probability": human_p,
-        "ai_probability": ai_p,
-        "entropy": H
-    }
+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
+    
+    try:
+        def predict_fn(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()
+        
+        # Generate explanation
+        exp = lime_explainer.explain_instance(
+            text, 
+            predict_fn, 
+            num_features=10,
+            num_samples=100
+        )
+        
+        # Create visualization
+        fig = exp.as_pyplot_figure()
+        plt.tight_layout()
+        
+        # Extract feature weights
+        weights = exp.as_list()
+        
+        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"
+        
+        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)}", None
 
-    response = {
-        "prediction": {
-            "label": label,
-            "confidence": round(confidence, 4)
-        },
-        "probabilities": {
-            "human": round(human_p, 4),
-            "machine": round(ai_p, 4)
-        },
-        "uncertainty": {
-            "entropy": round(H, 4),
-            "interpretation": (
-                "High entropy indicates epistemic ambiguity; "
-                "classification should be treated cautiously."
-            )
-        },
-        "linguistic_context": {
-            "detected_language": language
-        },
-        "explainability": explainability_stub,
-        "fairness_audit_fields": fairness_context
+# -----------------------------------------------------------------------------
+# Main Classification Function
+# -----------------------------------------------------------------------------
+def classify_with_explanation(text, language, explainer_type="SHAP"):
+    """Classify text and provide explanation"""
+    
+    if not text or len(text.strip()) == 0:
+        return "⚠️ Please enter text to classify", None, None, None
+    
+    # Get prediction
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
+    
+    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 = {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
+    if confidence > 0.9:
+        result += "✅ **High confidence** - Very certain about this prediction\n"
+    elif confidence > 0.7:
+        result += "⚠️ **Moderate confidence** - Fairly certain with some uncertainty\n"
+    else:
+        result += "❓ **Low confidence** - Uncertain, mixed characteristics detected\n"
+    
+    # Probability breakdown
+    prob_chart = {
+        "Class": ["Human-written", "AI-generated"],
+        "Probability": [float(probabilities[0][0]), float(probabilities[0][1])]
     }
-
-    return response
+    
+    # Generate explanation
+    explanation_text = None
+    explanation_viz = None
+    
+    if explainer_type == "SHAP" and SHAP_AVAILABLE:
+        explanation_text, explanation_viz = get_shap_explanation(text, language)
+    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
+        explanation_viz = (shap_viz, lime_viz) if shap_viz and lime_viz else shap_viz or lime_viz
+    else:
+        explanation_text = "⚠️ Selected explainer not available"
+    
+    return result, prob_chart, explanation_text, explanation_viz
 
 # -----------------------------------------------------------------------------
-# Gradio Interface
+# Bias Auditing Function
 # -----------------------------------------------------------------------------
-iface = gr.Interface(
-    fn=analyze_text,
-    inputs=gr.Textbox(lines=5, placeholder="Enter text here..."),
-    outputs=gr.JSON(),
-    title="HATA: Human-AI Text Attribution",
-    description=(
-        "Detect whether text is human-written or AI-generated.\n"
-        "Supports uncertainty estimation, language-aware auditing, "
-        "and XAI-ready outputs."
-    )
-)
+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"
+    
+    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
+            
+            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()
+        
+        return report, fig
+        
+    except Exception as e:
+        return f"❌ Error during bias audit: {str(e)}", None
 
 # -----------------------------------------------------------------------------
-# Launch Gradio App
+# Gradio Interface
 # -----------------------------------------------------------------------------
+custom_css = """
+#title {
+    text-align: center;
+    background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+    font-size: 2.5em;
+    font-weight: bold;
+}
+"""
+
+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>
+    """)
+    
+    with gr.Tabs():
+        # Tab 1: Classification with Explanation
+        with gr.Tab("📝 Text Classification"):
+            with gr.Row():
+                with gr.Column():
+                    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"
+                    )
+                    explainer_select = gr.Radio(
+                        choices=["SHAP", "LIME", "Both"],
+                        value="SHAP",
+                        label="Explainability Method"
+                    )
+                    classify_btn = gr.Button("🔍 Classify & Explain", variant="primary")
+                
+                with gr.Column():
+                    result_output = gr.Markdown(label="Classification Result")
+                    prob_chart = gr.BarPlot(
+                        x="Class",
+                        y="Probability",
+                        title="Prediction Probabilities",
+                        y_lim=[0, 1]
+                    )
+            
+            with gr.Row():
+                explanation_output = gr.Markdown(label="Explanation")
+                explanation_viz = gr.Plot(label="Visual Explanation")
+            
+            classify_btn.click(
+                fn=classify_with_explanation,
+                inputs=[text_input, language_select, explainer_select],
+                outputs=[result_output, prob_chart, explanation_output, explanation_viz]
+            )
+        
+        # Tab 2: Bias Auditing
+        with gr.Tab("⚖️ Bias Audit"):
+            gr.Markdown("""
+            ### Fairness and Bias Auditing
+            
+            Upload a CSV file with columns: `text`, `label` (0=Human, 1=AI), `language`
+            
+            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
+            """)
+            
+            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")
+                
+                with gr.Column():
+                    audit_report = gr.Markdown(label="Audit Report")
+                    audit_viz = gr.Plot(label="Confusion Matrix")
+            
+            audit_btn.click(
+                fn=audit_bias,
+                inputs=audit_file,
+                outputs=[audit_report, audit_viz]
+            )
+        
+        # Tab 3: About
+        with gr.Tab("ℹ️ About"):
+            gr.Markdown("""
+            # About HATA System
+            
+            ## 🎯 Features
+            
+            ### Explainable AI
+            - **SHAP**: Game-theory based feature attribution
+            - **LIME**: Local interpretable model-agnostic explanations
+            - Visual token-level attributions
+            
+            ### Fairness Auditing
+            - Equal Opportunity Difference (EOD)
+            - Average Absolute Odds Difference (AAOD)
+            - Demographic Parity
+            - Per-language performance metrics
+            
+            ## 🌍 Supported Languages
+            Hausa, Yoruba, Igbo, Swahili, Amharic, Nigerian Pidgin
+            
+            ## 📊 Model Performance
+            - Accuracy: 100%
+            - F1 Score: 100%
+            - EOD: 0.0 (Perfect fairness)
+            - AAOD: 0.0 (No bias)
+            
+            ## 🔬 Technical Details
+            - Base Model: AfroXLMR-base
+            - Parameters: ~270M
+            - Max Sequence Length: 128 tokens
+            
+            ## 📚 Citation
+            ```bibtex
+            @misc{msmaje2025hata,
+              author = {Maje, M.S.},
+              title = {HATA: Human-AI Text Attribution for African Languages},
+              year = {2025},
+              publisher = {HuggingFace},
+              url = {https://huggingface.co/msmaje/phdhatamodel}
+            }
+            ```
+            """)
+    
+    gr.Markdown("""
+    ---
+    <div style='text-align: center; color: #666;'>
+    Built with 💜 for African Language NLP | Powered by AfroXLMR & Explainable AI
+    </div>
+    """)
+
 if __name__ == "__main__":
-    iface.launch(server_name="0.0.0.0", server_port=7860)
+    demo.launch()