model integration and ui imporvments

.gitignore

@@ -8,3 +8,4 @@ wheels/
 
 # Virtual environments
 .venv
+.env

.vscode/settings.json

@@ -0,0 +1,22 @@
+{
+    "workbench.colorCustomizations": {
+        "activityBar.activeBackground": "#2a4a6e",
+        "activityBar.background": "#2a4a6e",
+        "activityBar.foreground": "#e7e7e7",
+        "activityBar.inactiveForeground": "#e7e7e799",
+        "activityBarBadge.background": "#170910",
+        "activityBarBadge.foreground": "#e7e7e7",
+        "commandCenter.border": "#e7e7e799",
+        "sash.hoverBorder": "#2a4a6e",
+        "statusBar.background": "#1c3149",
+        "statusBar.foreground": "#e7e7e7",
+        "statusBarItem.hoverBackground": "#2a4a6e",
+        "statusBarItem.remoteBackground": "#1c3149",
+        "statusBarItem.remoteForeground": "#e7e7e7",
+        "titleBar.activeBackground": "#1c3149",
+        "titleBar.activeForeground": "#e7e7e7",
+        "titleBar.inactiveBackground": "#1c314999",
+        "titleBar.inactiveForeground": "#e7e7e799"
+    },
+    "peacock.color": "#1c3149"
+}

README.md

@@ -0,0 +1,99 @@
+# 🛡️ Hate Speech Detection Streamlit App
+
+A professional web application for detecting hate speech using advanced NLP with explainable AI.
+
+## Features
+
+- **Real-time Hate Speech Detection**: Classify text as hate speech or not
+- **Explainable AI**: See which words influenced the prediction
+- **Token Importance Visualization**: Color-coded highlighting of important tokens
+- **Probability Distribution**: Visual representation of model confidence
+- **Professional UI**: Clean, modern interface with interactive elements
+
+## Installation
+
+1. Install the required packages:
+
+```bash
+pip install -r requirements.txt
+```
+
+## Running the Application
+
+Run the Streamlit app with:
+
+```bash
+streamlit run app.py
+```
+
+The application will open in your default web browser at `http://localhost:8501`
+
+## Usage
+
+1. **Enter Text**: Type or paste text into the main input area
+2. **Optional Context**: Provide additional context or rationale (optional)
+3. **Analyze**: Click the "🔍 Analyze Text" button
+4. **View Results**: 
+   - See the classification (Hate Speech or Not Hate Speech)
+   - View confidence scores and probability distribution
+   - Explore token importance visualization
+   - Check which words influenced the decision
+
+## Model Information
+
+- **Architecture**: HateBERT + Rationale BERT + Multi-Scale CNN + Attention
+- **Model Repository**: [seffyehl/BetterShield](https://huggingface.co/seffyehl/BetterShield)
+- **Training Details**: 
+  - Batch Size: 8
+  - Learning Rate: 1e-5
+  - Weight Decay: 0.05
+  - Best Validation Loss: 0.27
+
+## Files
+
+- `app.py` - Main Streamlit application
+- `hatespeech_model.py` - Model loading and prediction functions
+- `requirements.txt` - Python dependencies
+- `README.md` - This file
+
+## Troubleshooting
+
+### Model Loading Issues
+
+If the model fails to load:
+- Check your internet connection (model downloads from Hugging Face)
+- Ensure you have enough disk space (~500MB for model files)
+- The first run will take longer as it downloads the model
+
+### Memory Issues
+
+If you encounter memory errors:
+- The model requires approximately 2GB of RAM
+- Close other applications to free up memory
+- Use CPU mode if GPU memory is limited
+
+## Configuration
+
+You can modify settings in the sidebar:
+- **Show Token Importance**: Toggle token highlighting
+- **Show Probability Distribution**: Toggle probability chart
+- **Show Technical Details**: View raw model outputs
+
+## Examples
+
+Try the built-in examples:
+- **Hate Speech Example**: Clear example of offensive content
+- **Not Hate Speech Example**: Disagreement expressed respectfully
+- **Borderline Example**: Strong criticism without hate
+
+## Credits
+
+Model trained using best practices:
+- Early stopping to prevent overfitting
+- Batch size optimization (8 vs 16)
+- Proper regularization (weight decay, dropout)
+- Extensive hyperparameter tuning
+
+## License
+
+MIT License

app.py

@@ -1,27 +1,294 @@
 import streamlit as st
-from hatespeech_model import predict_hatespeech
-import random
+from hatespeech_model import predict_hatespeech, load_model_from_hf
+import plotly.graph_objects as go
+import plotly.express as px
+import pandas as pd
+import numpy as np
 
-st.set_page_config(page_title="Hatespeech Classifier", layout="centered")
-st.title("Hatespeech Text Classifier")
-st.write("Enter text below to classify if it is hatespeech or not.")
+# Page configuration
+st.set_page_config(
+    page_title="🛡️ Hate Speech Detector",
+    page_icon="🛡️",
+    layout="wide"
+)
 
-user_input = st.text_area("Text to classify", "")
-input_split = user_input.split(" ")
+# Cached model loading function
+@st.cache_resource
+def load_cached_model(model_type="altered"):
+    """Load and cache the model"""
+    return load_model_from_hf(model_type=model_type)
 
-word_probabilities = {word: round(random.uniform(0, 1), 2) for word in input_split if word}
+# Custom CSS
+st.markdown("""
+    <style>
+    .main-header {
+        font-size: 3rem;
+        font-weight: bold;
+        text-align: center;
+        color: #1f77b4;
+        margin-bottom: 1rem;
+    }
+    .sub-header {
+        font-size: 1.2rem;
+        text-align: center;
+        color: #555;
+        margin-bottom: 2rem;
+    }
+    .prediction-box {
+        padding: 2rem;
+        border-radius: 10px;
+        text-align: center;
+        font-size: 1.5rem;
+        font-weight: bold;
+        margin: 1rem 0;
+    }
+    .hate-speech {
+        background-color: #ffebee;
+        color: #c62828;
+        border: 2px solid #ef5350;
+    }
+    .not-hate-speech {
+        background-color: #e8f5e9;
+        color: #2e7d32;
+        border: 2px solid #66bb6a;
+    }
+    </style>
+""", unsafe_allow_html=True)
 
-if st.button("Classify"):
+# Header
+st.markdown('<div class="main-header">🛡️ Hate Speech Detection System</div>', unsafe_allow_html=True)
+st.markdown('<div class="sub-header">Advanced NLP model with explainable AI for detecting hate speech</div>', unsafe_allow_html=True)
+
+# Model selection
+col_a, col_b, col_c = st.columns([1, 2, 1])
+with col_b:
+    model_type = st.radio(
+        "Select Model:",
+        ["Altered Shield (Advanced)", "Base Shield (Simple)"],
+        horizontal=True,
+        help="Altered Shield uses the full architecture with CNNs and attention. Base Shield is a simpler baseline."
+    )
+    
+model_choice = "altered" if "Altered" in model_type else "base"
+
+# Load model with spinner
+with st.spinner('🔄 Loading model... This may take a moment on first run.'):
+    try:
+        model, tokenizer_hatebert, tokenizer_rationale, config, device = load_cached_model(model_choice)
+        st.success(f'✅ {model_type} loaded successfully!')
+    except Exception as e:
+        st.error(f"❌ Error loading model: {str(e)}")
+        st.stop()
+
+# Sidebar
+with st.sidebar:
+    st.header("⚙️ Settings")
+    st.markdown(f"**Device:** {device.upper()}")
+    st.markdown(f"**Max Length:** {config.get('max_length', 128)}")
+    st.markdown(f"**CNN Filters:** {config.get('cnn_num_filters', 128)}")
+    
+    st.divider()
+    
+    show_rationale_viz = st.checkbox("Show Token Importance", value=True)
+    show_probabilities = st.checkbox("Show Probability Distribution", value=True)
+    show_details = st.checkbox("Show Technical Details", value=False)
+    
+    st.divider()
+    st.subheader("💡 About")
+    st.markdown("""
+    This model uses:
+    - **HateBERT** for hate speech understanding
+    - **Multi-Scale CNN** for feature extraction
+    - **Attention mechanisms** for interpretability
+    """)
+
+# Main interface
+col1, col2 = st.columns([2, 1])
+
+with col1:
+    st.subheader("📝 Input Text")
+    user_input = st.text_area(
+        "Enter text to analyze:",
+        placeholder="Type or paste text here to check for hate speech...",
+        height=150,
+        help="Enter any text and the model will classify it as hate speech or not"
+    )
+    
+    optional_rationale = st.text_area(
+        "Optional: Provide context or rationale (leave empty to use main text):",
+        placeholder="Why might this be hate speech? (optional)",
+        height=80
+    )
+
+with col2:
+    st.subheader("📊 Quick Stats")
+    if user_input:
+        word_count = len(user_input.split())
+        char_count = len(user_input)
+        st.metric("Words", word_count)
+        st.metric("Characters", char_count)
+    else:
+        st.info("Enter text to see statistics")
+
+# Classification button
+classify_button = st.button("🔍 Analyze Text", type="primary", use_container_width=True)
+
+if classify_button:
     if user_input.strip():
-        result = predict_hatespeech(user_input)
-        st.markdown(f"**Result:** {result}")
-        col1, col2 = st.columns(2)
-        col1.title("Shield Model Results")
-        col2.title("Interpretable Shield Model Results")
-        col1.write(f"**Result:** {result} ")
-        col1.write(f"**Probability:** {random.uniform(0, 1)} ")
-        col2.write(f"**Result:** {result}")
-        col2.write(f"**Probability:** {random.uniform(0, 1)} ")
-        col2.table({"Feature": input_split, "Importance": word_probabilities.values()})
+        with st.spinner('🔄 Analyzing text...'):
+            # Get prediction
+            result = predict_hatespeech(
+                text=user_input,
+                rationale=optional_rationale if optional_rationale else None,
+                model=model,
+                tokenizer_hatebert=tokenizer_hatebert,
+                tokenizer_rationale=tokenizer_rationale,
+                config=config,
+                device=device
+            )
+            
+            # Extract results
+            prediction = result['prediction']
+            confidence = result['confidence']
+            probabilities = result['probabilities']
+            rationale_scores = result['rationale_scores']
+            tokens = result['tokens']
+            
+            # Display results
+            st.divider()
+            st.header("📈 Analysis Results")
+            
+            # Prediction box
+            if prediction == 1:
+                st.markdown(f'<div class="prediction-box hate-speech">🚨 HATE SPEECH DETECTED</div>', 
+                           unsafe_allow_html=True)
+            else:
+                st.markdown(f'<div class="prediction-box not-hate-speech">✅ NOT HATE SPEECH</div>', 
+                           unsafe_allow_html=True)
+            
+            # Metrics
+            col1, col2, col3 = st.columns(3)
+            with col1:
+                st.metric("Confidence", f"{confidence:.1%}")
+            with col2:
+                st.metric("Not Hate Speech", f"{probabilities[0]:.1%}")
+            with col3:
+                st.metric("Hate Speech", f"{probabilities[1]:.1%}")
+            
+            # Probability distribution chart
+            if show_probabilities:
+                st.subheader("📊 Probability Distribution")
+                fig = go.Figure(data=[
+                    go.Bar(
+                        x=['Not Hate Speech', 'Hate Speech'],
+                        y=probabilities,
+                        marker_color=['#66bb6a', '#ef5350'],
+                        text=[f"{p:.1%}" for p in probabilities],
+                        textposition='auto',
+                    )
+                ])
+                fig.update_layout(
+                    yaxis_title="Probability",
+                    yaxis_range=[0, 1],
+                    height=300,
+                    showlegend=False
+                )
+                st.plotly_chart(fig, use_container_width=True)
+            
+            # Token importance visualization
+            if show_rationale_viz:
+                st.subheader("🔍 Token Importance Analysis")
+                st.caption("Highlighted words show which parts of the text influenced the prediction")
+                
+                # Filter out special tokens and create visualization
+                token_importance = []
+                html_output = "<div style='font-size: 18px; line-height: 2.5; padding: 20px; background-color: #f8f9fa; border-radius: 10px;'>"
+                
+                for token, score in zip(tokens, rationale_scores):
+                    if token not in ['[CLS]', '[SEP]', '[PAD]']:
+                        # Clean token
+                        display_token = token.replace('##', '')
+                        token_importance.append({'Token': display_token, 'Importance': score})
+                        
+                        # Color intensity based on score
+                        alpha = min(score * 1.5, 1.0)  # Scale up visibility
+                        if prediction == 1:  # Hate speech
+                            color = f"rgba(239, 83, 80, {alpha:.2f})"
+                        else:  # Not hate speech
+                            color = f"rgba(102, 187, 106, {alpha:.2f})"
+                        
+                        html_output += f"<span style='background-color: {color}; padding: 4px 8px; margin: 2px; border-radius: 5px; display: inline-block;'>{display_token}</span> "
+                
+                html_output += "</div>"
+                st.markdown(html_output, unsafe_allow_html=True)
+                
+                if prediction == 1:
+                    st.caption("🔴 Darker red = Higher importance for hate speech detection")
+                else:
+                    st.caption("🟢 Darker green = Higher importance for non-hate speech classification")
+                
+                # Top important tokens
+                st.subheader("📋 Top Important Tokens")
+                df_importance = pd.DataFrame(token_importance)
+                df_importance = df_importance.sort_values('Importance', ascending=False).head(10)
+                df_importance['Importance'] = df_importance['Importance'].apply(lambda x: f"{x:.4f}")
+                
+                st.dataframe(
+                    df_importance,
+                    use_container_width=True,
+                    hide_index=True
+                )
+            
+            # Technical details
+            if show_details:
+                st.subheader("🔧 Technical Details")
+                with st.expander("View Model Outputs"):
+                    st.json({
+                        'prediction': int(prediction),
+                        'confidence': float(confidence),
+                        'probability_not_hate': float(probabilities[0]),
+                        'probability_hate': float(probabilities[1]),
+                        'num_tokens': len([t for t in tokens if t not in ['[CLS]', '[SEP]', '[PAD]']]),
+                        'device': device,
+                        'model_config': {
+                            'max_length': config.get('max_length'),
+                            'cnn_filters': config.get('cnn_num_filters'),
+                        }
+                    })
     else:
-        st.warning("Please enter some text to classify.")
+        st.warning("⚠️ Please enter some text to analyze.")
+
+# Examples section
+st.divider()
+st.subheader("💡 Try Example Texts")
+
+col1, col2, col3 = st.columns(3)
+
+with col1:
+    if st.button("Example: Hate Speech", use_container_width=True):
+        st.session_state.example_text = "You people are worthless and should leave this country!"
+        st.rerun()
+
+with col2:
+    if st.button("Example: Not Hate Speech", use_container_width=True):
+        st.session_state.example_text = "I disagree with your opinion, but I respect your right to express it."
+        st.rerun()
+
+with col3:
+    if st.button("Example: Borderline", use_container_width=True):
+        st.session_state.example_text = "This policy is terrible and will hurt everyone involved."
+        st.rerun()
+
+if 'example_text' in st.session_state:
+    st.info(f"**Example loaded:** {st.session_state.example_text}")
+    st.caption("↑ Copy this text to the input box above and click 'Analyze Text'")
+
+# Footer
+st.divider()
+st.markdown("""
+    <div style='text-align: center; color: gray; padding: 20px;'>
+        <p><b>Hate Speech Detection Model with Rationale Extraction</b></p>
+        <p>Powered by HateBERT + Multi-Scale CNN + Attention Mechanisms</p>
+        <p>Model trained with advanced regularization and early stopping for optimal performance</p>
+    </div>
+""", unsafe_allow_html=True)

check_base_config.py

@@ -0,0 +1,21 @@
+#!/usr/bin/env python
+"""Check the base config file structure."""
+
+from huggingface_hub import hf_hub_download
+import json
+
+repo_id = "seffyehl/BetterShield"
+config_filename = "base_config.json"
+
+print(f"Downloading {config_filename}...")
+config_path = hf_hub_download(repo_id=repo_id, filename=config_filename)
+
+print(f"\nReading config from: {config_path}")
+with open(config_path, 'r') as f:
+    config = json.load(f)
+
+print("\n" + "="*60)
+print("BASE CONFIG CONTENTS:")
+print("="*60)
+print(json.dumps(config, indent=2))
+print("="*60)

check_config.py

@@ -0,0 +1,15 @@
+"""Check the actual model configuration"""
+from huggingface_hub import hf_hub_download
+import json
+
+repo_id = "seffyehl/BetterShield"
+
+# Download and read config
+config_path = hf_hub_download(repo_id=repo_id, filename="alter_config.json")
+
+with open(config_path, 'r') as f:
+    config = json.load(f)
+
+print("Model Configuration:")
+print("="*60)
+print(json.dumps(config, indent=2))

check_hf_files.py

@@ -0,0 +1,15 @@
+"""Check what files are available in the HuggingFace repository"""
+from huggingface_hub import list_repo_files
+
+repo_id = "seffyehl/BetterShield"
+
+print(f"Checking files in repository: {repo_id}")
+print("="*60)
+
+try:
+    files = list_repo_files(repo_id)
+    print("Available files:")
+    for file in files:
+        print(f"  - {file}")
+except Exception as e:
+    print(f"Error: {e}")

hatespeech_model.py

@@ -1,52 +1,386 @@
-import re
-import nltk
-from sklearn.feature_extraction.text import CountVectorizer
-from sklearn.naive_bayes import MultinomialNB
-import pandas as pd
-import os
-
-# Download NLTK data if not already present
-nltk.download('stopwords', quiet=True)
-from nltk.corpus import stopwords
-
-# Example training data (for demonstration)
-data = {
-    'text': [
-        'I hate you',
-        'You are so stupid',
-        'Have a nice day',
-        'I love this',
-        'You are an idiot',
-        'What a wonderful world',
-        'You are disgusting',
-        'Such a pleasant surprise',
-        'I despise your actions',
-        'You are amazing',
-    ],
-    'label': [1, 1, 0, 0, 1, 0, 1, 0, 1, 0]  # 1 = hatespeech, 0 = not
-}
-df = pd.DataFrame(data)
-
-# Preprocessing function
-def preprocess(text):
-    text = text.lower()
-    text = re.sub(r'[^a-zA-Z\s]', '', text)
-    tokens = text.split()
-    tokens = [t for t in tokens if t not in stopwords.words('english')]
-    return ' '.join(tokens)
-
-df['text_clean'] = df['text'].apply(preprocess)
-
-# Vectorizer and model
-vectorizer = CountVectorizer()
-X = vectorizer.fit_transform(df['text_clean'])
-y = df['label']
-
-model = MultinomialNB()
-model.fit(X, y)
-
-def predict_hatespeech(text):
-    text_clean = preprocess(text)
-    X_test = vectorizer.transform([text_clean])
-    pred = model.predict(X_test)[0]
-    return 'Hatespeech' if pred == 1 else 'Not Hatespeech'
+from huggingface_hub import hf_hub_download
+import torch
+import torch.nn as nn
+import json
+from transformers import AutoModel, AutoTokenizer
+
+# Model Architecture Classes
+class TemporalCNN(nn.Module):
+    def __init__(self, input_dim=768, num_filters=128, kernel_sizes=(2,3,4,5,6,7), dropout=0.3):
+        super().__init__()
+        self.convs = nn.ModuleList([
+            nn.Conv1d(input_dim, num_filters, k) for k in kernel_sizes
+        ])
+        self.dropout = nn.Dropout(dropout)
+        # Output size is num_filters * num_kernels * 2 (max + mean pooling)
+        self.output_size = num_filters * len(kernel_sizes) * 2
+        
+    def forward(self, x, mask=None):
+        x = x.transpose(1, 2)  # (B, H, L)
+        conv_outs = []
+        for conv in self.convs:
+            c = torch.relu(conv(x))  # (B, num_filters, L')
+            # Both max and mean pooling
+            max_pool = torch.max(c, dim=2)[0]  # (B, num_filters)
+            mean_pool = torch.mean(c, dim=2)   # (B, num_filters)
+            conv_outs.append(max_pool)
+            conv_outs.append(mean_pool)
+        out = torch.cat(conv_outs, dim=1)  # (B, num_filters * len(kernel_sizes) * 2)
+        out = self.dropout(out)
+        return out
+
+class MultiScaleAttentionCNN(nn.Module):
+    def __init__(self, hidden_size=768, num_filters=128, kernel_sizes=(2,3,4,5,6,7), dropout=0.3):
+        super().__init__()
+        # Convolution layers
+        self.convs = nn.ModuleList([
+            nn.Conv1d(hidden_size, num_filters, k) for k in kernel_sizes
+        ])
+        # Attention layers - output 1 value per filter for attention weighting
+        self.attn = nn.ModuleList([
+            nn.Linear(num_filters, 1) for _ in kernel_sizes
+        ])
+        self.dropout = nn.Dropout(dropout)
+        self.output_size = num_filters * len(kernel_sizes)
+        
+    def forward(self, x, mask=None):
+        x = x.transpose(1, 2)  # (B, H, L)
+        conv_outs = []
+        for conv, attn in zip(self.convs, self.attn):
+            c = torch.relu(conv(x))  # (B, num_filters, L')
+            c_t = c.transpose(1, 2)  # (B, L', num_filters)
+            # Apply attention to get weights
+            w = attn(c_t)  # (B, L', 1)
+            w = torch.softmax(w, dim=1)  # attention weights
+            # Weighted sum pooling
+            pooled = (c_t * w).sum(dim=1)  # (B, num_filters)
+            conv_outs.append(pooled)
+        out = torch.cat(conv_outs, dim=1)  # (B, num_filters * len(kernel_sizes))
+        out = self.dropout(out)
+        return out
+
+class ProjectionMLP(nn.Module):
+    def __init__(self, input_size, hidden_size, num_labels, dropout=0.3):
+        super().__init__()
+        self.layers = nn.Sequential(
+            nn.Linear(input_size, hidden_size),
+            nn.ReLU(),
+            nn.Linear(hidden_size, num_labels)
+        )
+    
+    def forward(self, x):
+        return self.layers(x)
+
+class BaseShield(nn.Module):
+    """
+    Simple base model that concatenates HateBERT and rationale BERT CLS embeddings
+    """
+    def __init__(self, hatebert_model, additional_model, projection_mlp, hidden_size=768,
+                 freeze_additional_model=True):
+        super().__init__()
+        self.hatebert_model = hatebert_model
+        self.additional_model = additional_model
+        self.projection_mlp = projection_mlp
+        self.hidden_size = hidden_size
+        
+        if freeze_additional_model:
+            for param in self.additional_model.parameters():
+                param.requires_grad = False
+    
+    def forward(self, input_ids, attention_mask, additional_input_ids, additional_attention_mask,
+                return_attentions=False):
+        # Main text through HateBERT - get CLS token only
+        hatebert_out = self.hatebert_model(input_ids=input_ids, attention_mask=attention_mask,
+                                           output_attentions=return_attentions, return_dict=True)
+        hatebert_cls = hatebert_out.last_hidden_state[:, 0, :]  # (B, 768)
+        
+        # Rationale text through frozen BERT - get CLS token only
+        with torch.no_grad():
+            add_out = self.additional_model(input_ids=additional_input_ids,
+                                           attention_mask=additional_attention_mask,
+                                           return_dict=True)
+            rationale_cls = add_out.last_hidden_state[:, 0, :]  # (B, 768)
+        
+        # Concatenate CLS embeddings: (B, 1536)
+        concat_emb = torch.cat((hatebert_cls, rationale_cls), dim=1)
+        
+        # Classification
+        logits = self.projection_mlp(concat_emb)
+        
+        # Return dummy rationale_probs and selector_logits for compatibility with app
+        batch_size = input_ids.size(0)
+        seq_len = input_ids.size(1)
+        dummy_rationale_probs = torch.zeros(batch_size, seq_len, device=input_ids.device)
+        dummy_selector_logits = torch.zeros(batch_size, seq_len, device=input_ids.device)
+        
+        attns = hatebert_out.attentions if (return_attentions and hasattr(hatebert_out, "attentions")) else None
+        return logits, dummy_rationale_probs, dummy_selector_logits, attns
+
+
+class ConcatModelWithRationale(nn.Module):
+    def __init__(self, hatebert_model, additional_model, projection_mlp, hidden_size=768,
+                 gumbel_temp=0.5, freeze_additional_model=True, cnn_num_filters=128,
+                 cnn_kernel_sizes=(2,3,4), cnn_dropout=0.3):
+        super().__init__()
+        self.hatebert_model = hatebert_model
+        self.additional_model = additional_model
+        self.projection_mlp = projection_mlp
+        self.gumbel_temp = gumbel_temp
+        self.hidden_size = hidden_size
+        
+        if freeze_additional_model:
+            for param in self.additional_model.parameters():
+                param.requires_grad = False
+        
+        self.selector = nn.Linear(hidden_size, 1)
+        self.temporal_cnn = TemporalCNN(input_dim=hidden_size, num_filters=cnn_num_filters,
+                                        kernel_sizes=cnn_kernel_sizes, dropout=cnn_dropout)
+        self.temporal_out_dim = cnn_num_filters * len(cnn_kernel_sizes) * 2
+        self.msa_cnn = MultiScaleAttentionCNN(hidden_size=hidden_size, num_filters=cnn_num_filters,
+                                              kernel_sizes=cnn_kernel_sizes, dropout=cnn_dropout)
+        self.msa_out_dim = self.msa_cnn.output_size
+    
+    def gumbel_sigmoid_sample(self, logits):
+        noise = -torch.log(-torch.log(torch.rand_like(logits) + 1e-9) + 1e-9)
+        y = logits + noise
+        return torch.sigmoid(y / self.gumbel_temp)
+    
+    def forward(self, input_ids, attention_mask, additional_input_ids, additional_attention_mask,
+                return_attentions=False):
+        hatebert_out = self.hatebert_model(input_ids=input_ids, attention_mask=attention_mask,
+                                           output_attentions=return_attentions, return_dict=True)
+        hatebert_emb = hatebert_out.last_hidden_state
+        cls_emb = hatebert_emb[:, 0, :]
+        
+        with torch.no_grad():
+            add_out = self.additional_model(input_ids=additional_input_ids,
+                                           attention_mask=additional_attention_mask,
+                                           return_dict=True)
+            rationale_emb = add_out.last_hidden_state
+        
+        selector_logits = self.selector(hatebert_emb).squeeze(-1)
+        rationale_probs = self.gumbel_sigmoid_sample(selector_logits)
+        rationale_probs = rationale_probs * attention_mask.float().to(rationale_probs.device)
+        
+        masked_hidden = hatebert_emb * rationale_probs.unsqueeze(-1)
+        denom = rationale_probs.sum(1).unsqueeze(-1).clamp_min(1e-6)
+        pooled_rationale = masked_hidden.sum(1) / denom
+        
+        temporal_features = self.temporal_cnn(hatebert_emb, attention_mask)
+        rationale_features = self.msa_cnn(rationale_emb, additional_attention_mask)
+        
+        concat_emb = torch.cat((cls_emb, temporal_features, rationale_features, pooled_rationale), dim=1)
+        logits = self.projection_mlp(concat_emb)
+        
+        attns = hatebert_out.attentions if (return_attentions and hasattr(hatebert_out, "attentions")) else None
+        return logits, rationale_probs, selector_logits, attns
+
+def load_model_from_hf(model_type="altered"):
+    """
+    Load model from Hugging Face Hub
+    
+    Args:
+        model_type: Either "altered" or "base" to choose which model to load
+    """
+    
+    repo_id = "seffyehl/BetterShield"
+    
+    # Choose model and config files based on model_type
+    if model_type.lower() == "altered":
+        model_filename = "AlteredShield.pth"
+        config_filename = "alter_config.json"
+    elif model_type.lower() == "base":
+        model_filename = "BaseShield.pth"
+        config_filename = "base_config.json"
+    else:
+        raise ValueError(f"model_type must be 'altered' or 'base', got '{model_type}'")
+    
+    # Download files
+    model_path = hf_hub_download(
+        repo_id=repo_id,
+        filename=model_filename
+    )
+    
+    config_path = hf_hub_download(
+        repo_id=repo_id,
+        filename=config_filename
+    )
+    
+    # Load config
+    with open(config_path, 'r') as f:
+        config = json.load(f)
+    
+    # Load checkpoint
+    checkpoint = torch.load(model_path, map_location='cpu')
+    
+    # Handle nested config structure (base model uses model_config, altered uses flat structure)
+    if 'model_config' in config:
+        model_config = config['model_config']
+        training_config = config.get('training_config', {})
+    else:
+        model_config = config
+        training_config = config
+    
+    # Initialize base models
+    hatebert_model = AutoModel.from_pretrained(model_config['hatebert_model'])
+    rationale_model = AutoModel.from_pretrained(model_config['rationale_model'])
+    
+    tokenizer_hatebert = AutoTokenizer.from_pretrained(model_config['hatebert_model'])
+    tokenizer_rationale = AutoTokenizer.from_pretrained(model_config['rationale_model'])
+    
+    # Rebuild architecture based on model type
+    H = hatebert_model.config.hidden_size
+    max_length = training_config.get('max_length', 128)
+    
+    if model_type.lower() == "base":
+        # Base Shield: Simple concatenation model
+        # Input: 768 (HateBERT CLS) + 768 (Rationale BERT CLS) = 1536
+        proj_input_dim = H * 2  # 1536
+        # The saved model uses 512, not what's in projection_config
+        adapter_dim = 512  # hardcoded to match saved weights
+        projection_mlp = ProjectionMLP(input_size=proj_input_dim, hidden_size=adapter_dim, 
+                                      num_labels=2, dropout=0.0)
+        
+        model = BaseShield(
+            hatebert_model=hatebert_model,
+            additional_model=rationale_model,
+            projection_mlp=projection_mlp,
+            hidden_size=H,
+            freeze_additional_model=True
+        )
+    else:
+        # Altered Shield: Complex model with CNN and attention
+        cnn_num_filters = model_config.get('cnn_num_filters', 128)
+        # Use extended kernel sizes to match saved model
+        cnn_kernel_sizes = (2, 3, 4, 5, 6, 7)
+        adapter_dim = model_config.get('adapter_dim', 128)
+        cnn_dropout = model_config.get('cnn_dropout', 0.3)
+        
+        # Calculate dimensions
+        # TemporalCNN: num_filters * len(kernel_sizes) * 2 (max + mean pooling)
+        temporal_out_dim = cnn_num_filters * len(cnn_kernel_sizes) * 2
+        # MultiScaleAttentionCNN: num_filters * len(kernel_sizes)
+        msa_out_dim = cnn_num_filters * len(cnn_kernel_sizes)
+        # Total: CLS (768) + TemporalCNN + MSA + pooled_rationale (768)
+        proj_input_dim = H + temporal_out_dim + msa_out_dim + H
+        projection_mlp = ProjectionMLP(input_size=proj_input_dim, hidden_size=adapter_dim, 
+                                      num_labels=2, dropout=0.0)
+        
+        model = ConcatModelWithRationale(
+            hatebert_model=hatebert_model,
+            additional_model=rationale_model,
+            projection_mlp=projection_mlp,
+            hidden_size=H,
+            freeze_additional_model=True,
+            cnn_num_filters=cnn_num_filters,
+            cnn_kernel_sizes=cnn_kernel_sizes,
+            cnn_dropout=cnn_dropout
+        )
+    
+    model.load_state_dict(checkpoint['model_state_dict'])
+    model.eval()
+    
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    model = model.to(device)
+    
+    # Create a unified config dict with max_length at top level for compatibility
+    unified_config = config.copy()
+    if 'max_length' not in unified_config and 'training_config' in config:
+        unified_config['max_length'] = training_config.get('max_length', 128)
+    
+    return model, tokenizer_hatebert, tokenizer_rationale, unified_config, device
+
+def predict_text(text, rationale, model, tokenizer_hatebert, tokenizer_rationale, 
+                 device='cpu', max_length=128):
+    """
+    Predict hate speech for a given text and rationale
+    
+    Args:
+        text: Input text to classify
+        rationale: Rationale/explanation text
+        model: Loaded model
+        tokenizer_hatebert: HateBERT tokenizer
+        tokenizer_rationale: Rationale model tokenizer
+        device: 'cpu' or 'cuda'
+        max_length: Maximum sequence length
+    
+    Returns:
+        prediction: 0 or 1
+        probability: Confidence score
+        rationale_scores: Token-level rationale scores
+    """
+    model.eval()
+    
+    # Tokenize inputs
+    inputs_main = tokenizer_hatebert(
+        text,
+        max_length=max_length,
+        padding='max_length',
+        truncation=True,
+        return_tensors='pt'
+    )
+    
+    inputs_rationale = tokenizer_rationale(
+        rationale if rationale else text,  # Use text if no rationale provided
+        max_length=max_length,
+        padding='max_length',
+        truncation=True,
+        return_tensors='pt'
+    )
+    
+    # Move to device
+    input_ids = inputs_main['input_ids'].to(device)
+    attention_mask = inputs_main['attention_mask'].to(device)
+    add_input_ids = inputs_rationale['input_ids'].to(device)
+    add_attention_mask = inputs_rationale['attention_mask'].to(device)
+    
+    # Inference
+    with torch.no_grad():
+        logits, rationale_probs, selector_logits, _ = model(
+            input_ids, 
+            attention_mask, 
+            add_input_ids, 
+            add_attention_mask
+        )
+        
+        # Get probabilities
+        probs = torch.softmax(logits, dim=1)
+        prediction = logits.argmax(dim=1).item()
+        confidence = probs[0, prediction].item()
+    
+    return {
+        'prediction': prediction,
+        'confidence': confidence,
+        'probabilities': probs[0].cpu().numpy(),
+        'rationale_scores': rationale_probs[0].cpu().numpy(),
+        'tokens': tokenizer_hatebert.convert_ids_to_tokens(input_ids[0])
+    }
+
+def predict_hatespeech(text, rationale, model, tokenizer_hatebert, tokenizer_rationale, config, device):
+    """
+    Predict hate speech for given text
+    
+    Args:
+        text: Input text to classify
+        rationale: Optional rationale text
+        model: Loaded model
+        tokenizer_hatebert: HateBERT tokenizer
+        tokenizer_rationale: Rationale tokenizer
+        config: Model configuration
+        device: Device to run on
+    
+    Returns:
+        Dictionary with prediction results
+    """
+    # Get prediction
+    result = predict_text(
+        text=text,
+        rationale=rationale,
+        model=model,
+        tokenizer_hatebert=tokenizer_hatebert,
+        tokenizer_rationale=tokenizer_rationale,
+        device=device,
+        max_length=config.get('max_length', 128)
+    )
+    
+    return result

pyproject.toml

@@ -5,8 +5,20 @@ description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.13"
 dependencies = [
+    "filelock>=3.12.0",
+    "huggingface-hub>=0.34.0,<1.0",
     "nltk>=3.9.2",
+    "numpy>=1.24.0",
+    "packaging>=23.0",
     "pandas>=2.3.3",
+    "plotly>=5.14.0",
+    "regex>=2023.5.0",
+    "requests>=2.31.0",
+    "safetensors>=0.3.0",
     "scikit-learn>=1.8.0",
     "streamlit>=1.52.1",
+    "tokenizers>=0.13.0",
+    "torch>=2.9.1",
+    "tqdm>=4.65.0",
+    "transformers>=4.30.0",
 ]

requirements.txt

@@ -0,0 +1,14 @@
+streamlit>=1.28.0
+torch>=2.0.0
+transformers>=4.30.0
+huggingface-hub>=0.34.0,<1.0
+plotly>=5.14.0
+pandas>=2.0.0
+numpy>=1.24.0
+tokenizers>=0.13.0
+safetensors>=0.3.0
+filelock>=3.12.0
+requests>=2.31.0
+tqdm>=4.65.0
+regex>=2023.5.0
+packaging>=23.0

run_app.ps1

@@ -0,0 +1,20 @@
+# Run Streamlit App Script
+Write-Host "🛡️ Starting Hate Speech Detection App..." -ForegroundColor Cyan
+Write-Host ""
+
+# Check if streamlit is installed
+try {
+    $streamlitVersion = streamlit --version 2>&1
+    Write-Host "✓ Streamlit found: $streamlitVersion" -ForegroundColor Green
+} catch {
+    Write-Host "✗ Streamlit not found. Installing requirements..." -ForegroundColor Yellow
+    pip install -r requirements.txt
+}
+
+Write-Host ""
+Write-Host "Starting application..." -ForegroundColor Cyan
+Write-Host "Press Ctrl+C to stop the server" -ForegroundColor Yellow
+Write-Host ""
+
+# Run the Streamlit app
+streamlit run app.py

test_base_model.py

@@ -0,0 +1,41 @@
+#!/usr/bin/env python
+"""Test script to check if Base Shield model loads."""
+
+from hatespeech_model import load_model_from_hf
+import torch
+
+print("Testing Base Shield model load...")
+print("=" * 60)
+
+try:
+    print("\n1. Loading Base Shield model...")
+    base_model, base_tokenizer, _, config, device = load_model_from_hf("base")
+    print("✅ Base Shield loaded successfully!")
+    print(f"   Model type: {type(base_model).__name__}")
+    print(f"   Device: {device}")
+    
+    # Test forward pass with dummy input
+    test_input = torch.randint(0, 1000, (1, 50)).to(device)
+    test_attn_mask = torch.ones(1, 50).to(device)
+    
+    print("\n2. Testing forward pass...")
+    with torch.no_grad():
+        logits, rationale_probs, selector_logits, attns = base_model(
+            input_ids=test_input,
+            attention_mask=test_attn_mask,
+            additional_input_ids=test_input,
+            additional_attention_mask=test_attn_mask
+        )
+    print(f"✅ Forward pass successful!")
+    print(f"   Logits shape: {logits.shape}")
+    print(f"   Output range: [{logits.min():.4f}, {logits.max():.4f}]")
+    
+    print("\n" + "=" * 60)
+    print("✅ All tests passed!")
+    
+except Exception as e:
+    print(f"\n❌ Error: {e}")
+    import traceback
+    traceback.print_exc()
+    print("\n" + "=" * 60)
+    print("❌ Tests failed")

test_imports.py

@@ -0,0 +1,61 @@
+"""Test script to check imports and identify version issues"""
+import sys
+
+print("Python version:", sys.version)
+print("\n" + "="*50)
+print("Testing imports...")
+print("="*50)
+
+try:
+    import torch
+    print(f"✓ torch: {torch.__version__}")
+except Exception as e:
+    print(f"✗ torch: {e}")
+
+try:
+    import transformers
+    print(f"✓ transformers: {transformers.__version__}")
+except Exception as e:
+    print(f"✗ transformers: {e}")
+
+try:
+    import huggingface_hub
+    print(f"✓ huggingface_hub: {huggingface_hub.__version__}")
+except Exception as e:
+    print(f"✗ huggingface_hub: {e}")
+
+try:
+    import streamlit
+    print(f"✓ streamlit: {streamlit.__version__}")
+except Exception as e:
+    print(f"✗ streamlit: {e}")
+
+try:
+    import plotly
+    print(f"✓ plotly: {plotly.__version__}")
+except Exception as e:
+    print(f"✗ plotly: {e}")
+
+try:
+    import pandas
+    print(f"✓ pandas: {pandas.__version__}")
+except Exception as e:
+    print(f"✗ pandas: {e}")
+
+try:
+    import numpy
+    print(f"✓ numpy: {numpy.__version__}")
+except Exception as e:
+    print(f"✗ numpy: {e}")
+
+print("\n" + "="*50)
+print("Checking transformers dependencies...")
+print("="*50)
+
+try:
+    from transformers import AutoModel, AutoTokenizer
+    print("✓ Successfully imported AutoModel and AutoTokenizer")
+except Exception as e:
+    print(f"✗ Error importing from transformers: {e}")
+    import traceback
+    traceback.print_exc()

test_model_load.py

@@ -0,0 +1,40 @@
+#!/usr/bin/env python
+"""Test script to check if model loads without state_dict errors."""
+
+from hatespeech_model import load_model_from_hf
+
+print("Testing model load...")
+print("=" * 60)
+
+try:
+    print("\n1. Loading Altered Shield model...")
+    altered_model, altered_tokenizer, _, _, _ = load_model_from_hf("altered")
+    print("✅ Altered Shield loaded successfully!")
+    print(f"   Model type: {type(altered_model).__name__}")
+    
+    # Test forward pass with dummy input
+    import torch
+    test_input = torch.randint(0, 1000, (1, 50))
+    test_attn_mask = torch.ones(1, 50)
+    
+    print("\n2. Testing forward pass...")
+    with torch.no_grad():
+        logits, rationale_probs, selector_logits, attns = altered_model(
+            input_ids=test_input,
+            attention_mask=test_attn_mask,
+            additional_input_ids=test_input,
+            additional_attention_mask=test_attn_mask
+        )
+    print(f"✅ Forward pass successful!")
+    print(f"   Logits shape: {logits.shape}")
+    print(f"   Output range: [{logits.min():.4f}, {logits.max():.4f}]")
+    
+    print("\n" + "=" * 60)
+    print("✅ All tests passed!")
+    
+except Exception as e:
+    print(f"\n❌ Error: {e}")
+    import traceback
+    traceback.print_exc()
+    print("\n" + "=" * 60)
+    print("❌ Tests failed")

uv.lock

@@ -18,6 +18,27 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/db/33/ef2f2409450ef6daa61459d5de5c08128e7d3edb773fefd0a324d1310238/altair-6.0.0-py3-none-any.whl", hash = "sha256:09ae95b53d5fe5b16987dccc785a7af8588f2dca50de1e7a156efa8a461515f8", size = 795410, upload-time = "2025-11-12T08:59:09.804Z" },
 ]
 
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081, upload-time = "2024-05-20T21:33:25.928Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" },
+]
+
+[[package]]
+name = "anyio"
+version = "4.12.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "idna" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/16/ce/8a777047513153587e5434fd752e89334ac33e379aa3497db860eeb60377/anyio-4.12.0.tar.gz", hash = "sha256:73c693b567b0c55130c104d0b43a9baf3aa6a31fc6110116509f27bf75e21ec0", size = 228266, upload-time = "2025-11-28T23:37:38.911Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7f/9c/36c5c37947ebfb8c7f22e0eb6e4d188ee2d53aa3880f3f2744fb894f0cb1/anyio-4.12.0-py3-none-any.whl", hash = "sha256:dad2376a628f98eeca4881fc56cd06affd18f659b17a747d3ff0307ced94b1bb", size = 113362, upload-time = "2025-11-28T23:36:57.897Z" },
+]
+
 [[package]]
 name = "attrs"
 version = "25.4.0"
@@ -27,6 +48,19 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/3a/2a/7cc015f5b9f5db42b7d48157e23356022889fc354a2813c15934b7cb5c0e/attrs-25.4.0-py3-none-any.whl", hash = "sha256:adcf7e2a1fb3b36ac48d97835bb6d8ade15b8dcce26aba8bf1d14847b57a3373", size = 67615, upload-time = "2025-10-06T13:54:43.17Z" },
 ]
 
+[[package]]
+name = "beautifulsoup4"
+version = "4.14.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "soupsieve" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/c3/b0/1c6a16426d389813b48d95e26898aff79abbde42ad353958ad95cc8c9b21/beautifulsoup4-4.14.3.tar.gz", hash = "sha256:6292b1c5186d356bba669ef9f7f051757099565ad9ada5dd630bd9de5fa7fb86", size = 627737, upload-time = "2025-11-30T15:08:26.084Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1a/39/47f9197bdd44df24d67ac8893641e16f386c984a0619ef2ee4c51fbbc019/beautifulsoup4-4.14.3-py3-none-any.whl", hash = "sha256:0918bfe44902e6ad8d57732ba310582e98da931428d231a5ecb9e7c703a735bb", size = 107721, upload-time = "2025-11-30T15:08:24.087Z" },
+]
+
 [[package]]
 name = "blinker"
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