Update app.py (#20)

- Update app.py (c65efe8e34efe9e865e0a322c6d61d397ad37000)


Co-authored-by: Muhammad Khaqan Nasir <[email protected]>

app.py

@@ -1,192 +1,464 @@
-import sys
-from pathlib import Path
-import os
-import gdown
 import streamlit as st
+import torch
+import pandas as pd
+import numpy as np
+from pathlib import Path
+import sys
+import plotly.express as px
+import plotly.graph_objects as go
+from transformers import BertTokenizer
+import nltk
+
+# Download required NLTK data
+try:
+    nltk.data.find('tokenizers/punkt')
+except LookupError:
+    nltk.download('punkt')
+try:
+    nltk.data.find('corpora/stopwords')
+except LookupError:
+    nltk.download('stopwords')
+try:
+    nltk.data.find('tokenizers/punkt_tab')
+except LookupError:
+    nltk.download('punkt_tab')
+try:
+    nltk.data.find('corpora/wordnet')
+except LookupError:
+    nltk.download('wordnet')
 
-# Set page config - must be first Streamlit command
-st.set_page_config(
-    page_title="TruthCheck - Advanced Fake News Detector",
-    page_icon="🛡️",
-    layout="wide",
-    initial_sidebar_state="expanded"
-)
+# Add project root to Python path
+project_root = Path(__file__).parent.parent
+sys.path.append(str(project_root))
 
-# Custom CSS for modern styling
+from src.models.hybrid_model import HybridFakeNewsDetector
+from src.config.config import *
+from src.data.preprocessor import TextPreprocessor
+
+# Custom CSS for streamlined styling
 st.markdown("""
 <style>
-    @import url('https://fonts.googleapis.com/css2?family=Poppins:wght@300;400;600&display=swap');
-    
-    body {
-        font-family: 'Poppins', sans-serif;
-        background-color: #FFFFFF;
-        color: #333333;
-    }
-    
-    .sidebar .sidebar-content {
-        background-color: #F4F7FA;
+    /* Import Google Fonts */
+    @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;800&display=swap');
+
+    /* Global Styles */
+    * {
+        margin: 0;
+        padding: 0;
+        box-sizing: border-box;
+    }
+
+    .stApp {
+        font-family: 'Inter', sans-serif;
+        background: #f8fafc;
+        min-height: 100vh;
+        color: #1a202c;
+    }
+
+    /* Hide Streamlit elements */
+    #MainMenu {visibility: hidden;}
+    footer {visibility: hidden;}
+    .stDeployButton {display: none;}
+    header {visibility: hidden;}
+    .stApp > header {visibility: hidden;}
+
+    /* Container */
+    .container {
+        max-width: 1200px;
+        margin: 0 auto;
         padding: 1rem;
     }
-    
-    .stButton>button {
-        background-color: #4B5EAA;
-        color: white;
-        border-radius: 8px;
-        padding: 0.5rem 1rem;
-        font-weight: 600;
-        transition: background-color 0.3s;
+
+    /* Header */
+    .header {
+        padding: 1rem 0;
+        text-align: center;
     }
-    
-    .stButton>button:hover {
-        background-color: #3A4A8C;
+
+    .header-title {
+        font-size: 2rem;
+        font-weight: 800;
+        color: #1a202c;
+        display: inline-flex;
+        align-items: center;
+        gap: 0.5rem;
     }
-    
-    .stTextArea textarea {
-        border: 1px solid #E0E0E0;
-        border-radius: 8px;
+
+    /* Hero Section */
+    .hero {
+        display: flex;
+        align-items: center;
+        gap: 2rem;
+        margin-bottom: 2rem;
+    }
+
+    .hero-left {
+        flex: 1;
         padding: 1rem;
     }
-    
-    .hero-section {
-        background-color: #F4F7FA;
-        padding: 2rem;
-        border-radius: 12px;
+
+    .hero-right {
+        flex: 1;
+        display: flex;
+        align-items: center;
+        justify-content: center;
+    }
+
+    .hero-right img {
+        max-width: 100%;
+        height: auto;
+        border-radius: 8px;
+    }
+
+    .hero-title {
+        font-size: 2.5rem;
+        font-weight: 700;
+        color: #1a202c;
+        margin-bottom: 0.5rem;
+    }
+
+    .hero-text {
+        font-size: 1rem;
+        color: #4a5568;
+        line-height: 1.5;
+        max-width: 450px;
+    }
+
+    /* About Section */
+    .about-section {
         margin-bottom: 2rem;
+        text-align: center;
+    }
+
+    .about-title {
+        font-size: 1.8rem;
+        font-weight: 600;
+        color: #1a202c;
+        margin-bottom: 0.5rem;
+    }
+
+    .about-text {
+        font-size: 1rem;
+        color: #4a5568;
+        line-height: 1.5;
+        max-width: 600px;
+        margin: 0 auto;
+    }
+
+    /* Input Section */
+    .input-container {
+        max-width: 800px;
+        margin: 0 auto;
+    }
+
+    .stTextArea > div > div > textarea {
+        border-radius: 8px !important;
+        border: 1px solid #d1d5db !important;
+        padding: 1rem !important;
+        font-size: 1rem !important;
+        font-family: 'Inter', sans-serif !important;
+        background: #ffffff !important;
+        min-height: 150px !important;
+        transition: all 0.2s ease !important;
+    }
+
+    .stTextArea > div > div > textarea:focus {
+        border-color: #6366f1 !important;
+        box-shadow: 0 0 0 2px rgba(99, 102, 241, 0.1) !important;
+        outline: none !important;
+    }
+
+    .stTextArea > div > div > textarea::placeholder {
+        color: #9ca3af !important;
+    }
+
+    /* Button Styling */
+    .stButton > button {
+        background: #6366f1 !important;
+        color: white !important;
+        border-radius: 8px !important;
+        padding: 0.75rem 2rem !important;
+        font-size: 1rem !important;
+        font-weight: 600 !important;
+        font-family: 'Inter', sans-serif !important;
+        transition: all 0.2s ease !important;
+        border: none !important;
+        width: 100% !important;
+    }
+
+    .stButton > button:hover {
+        background: #4f46e5 !important;
+        transform: translateY(-1px) !important;
+    }
+
+    /* Results Section */
+    .results-container {
+        margin-top: 1rem;
+        padding: 1rem;
+        border-radius: 8px;
     }
-    
-    .flash-message {
+
+    .result-card {
         padding: 1rem;
         border-radius: 8px;
+        border-left: 4px solid transparent;
         margin-bottom: 1rem;
-        font-weight: 600;
     }
-    
-    .success-message {
-        background-color: #E6F4EA;
-        color: #2E7D32;
+
+    .fake-news {
+        background: #fef2f2;
+        border-left-color: #ef4444;
     }
-    
-    .error-message {
-        background-color: #FEE2E2;
-        color: #B71C1C;
+
+    .real-news {
+        background: #ecfdf5;
+        border-left-color: #10b981;
     }
-    
-    /* Enhanced sidebar styling */
-    .stRadio > div {
+
+    .prediction-badge {
+        font-weight: 600;
+        font-size: 1rem;
+        margin-bottom: 0.5rem;
+        display: flex;
+        align-items: center;
         gap: 0.5rem;
     }
-    
-    .stRadio > div > label {
-        padding: 0.5rem 1rem;
-        border-radius: 8px;
-        transition: all 0.3s ease;
-        cursor: pointer;
-        font-weight: 500;
+
+    .confidence-score {
+        font-weight: 600;
+        margin-left: auto;
+        font-size: 1rem;
     }
-    
-    .stRadio > div > label:hover {
-        background-color: #E8F0FE;
-        transform: translateX(5px);
+
+    /* Chart Containers */
+    .chart-container {
+        padding: 1rem;
+        border-radius: 8px;
+        margin: 1rem 0;
     }
-    
-    .stRadio > div > label[data-checked="true"] {
-        background-color: #4B5EAA;
-        color: white;
+
+    /* Footer */
+    .footer {
+        margin-top: 2rem;
+        padding: 1rem 0;
+        text-align: center;
+        border-top: 1px solid #e5e7eb;
     }
 </style>
 """, unsafe_allow_html=True)
 
-MODEL_PATH = "models/saved/final_model.pt"
-GOOGLE_DRIVE_FILE_ID = "1xhYKuC5_Yri3mm3Ejt-SpB2WAVUTJvc_"
-GOOGLE_DRIVE_URL = f"https://drive.google.com/uc?id={GOOGLE_DRIVE_FILE_ID}"
+@st.cache_resource
+def load_model_and_tokenizer():
+    """Load the model and tokenizer (cached)."""
+    model = HybridFakeNewsDetector(
+        bert_model_name=BERT_MODEL_NAME,
+        lstm_hidden_size=LSTM_HIDDEN_SIZE,
+        lstm_num_layers=LSTM_NUM_LAYERS,
+        dropout_rate=DROPOUT_RATE
+    )
+    state_dict = torch.load(SAVED_MODELS_DIR / "final_model.pt", map_location=torch.device('cpu'))
+    model_state_dict = model.state_dict()
+    filtered_state_dict = {k: v for k, v in state_dict.items() if k in model_state_dict}
+    model.load_state_dict(filtered_state_dict, strict=False)
+    model.eval()
+    tokenizer = BertTokenizer.from_pretrained(BERT_MODEL_NAME)
+    return model, tokenizer
 
 @st.cache_resource
-def download_model():
-    """Download model from Google Drive if not exists."""
-    if not os.path.exists(MODEL_PATH):
-        os.makedirs(os.path.dirname(MODEL_PATH), exist_ok=True)
-        with st.spinner("Downloading model from Google Drive..."):
-            try:
-                gdown.download(GOOGLE_DRIVE_URL, MODEL_PATH, quiet=False)
-                st.markdown('<div class="flash-message success-message">Model downloaded successfully!</div>', unsafe_allow_html=True)
-            except Exception as e:
-                st.markdown(f'<div class="flash-message error-message">Failed to download model: {str(e)}</div>', unsafe_allow_html=True)
-                st.markdown('<div class="flash-message error-message">Please check your Google Drive link and make sure the file is publicly accessible.</div>', unsafe_allow_html=True)
-                return False
-    return True
-
-# Add src directory to Python path
-src_path = Path(__file__).parent / "src"
-sys.path.append(str(src_path))
-
-# Enhanced Sidebar navigation with icons
-st.sidebar.markdown("""
-<div style="text-align: center; margin-bottom: 2rem;">
-    <div style="font-size: 2.5rem; margin-bottom: 0.5rem;">🛡️</div>
-    <h1 style="color: #4B5EAA; font-size: 1.5rem; font-weight: 600; margin-bottom: 0.5rem; line-height: 1.2;">
-        TruthCheck
-    </h1>
-    <p style="color: #666; font-size: 0.9rem; margin: 0; font-weight: 300; line-height: 1.3;">
-        Advanced Fake News Detector
-    </p>
-</div>
-""", unsafe_allow_html=True)
+def get_preprocessor():
+    """Get the text preprocessor (cached)."""
+    return TextPreprocessor()
 
-st.sidebar.markdown("---")
-
-# Navigation with icons
-page = st.sidebar.radio(
-    "Navigate",
-    [
-        "🏠 Home",
-        "ℹ️ About", 
-        "📋 Terms of Use",
-        "🔒 Privacy Policy",
-        "👥 Team"
-    ],
-    label_visibility="collapsed",
-    key="navigation"
-)
-
-# Add some spacing and additional info
-st.sidebar.markdown("---")
-st.sidebar.markdown("""
-<div style="text-align: center; padding: 1.5rem; background: linear-gradient(135deg, #F8F9FA 0%, #E8F0FE 100%); border-radius: 12px; margin-top: 1rem; border: 1px solid #E0E7FF;">
-    <div style="font-size: 2rem; margin-bottom: 0.8rem;">🔍</div>
-    <p style="font-size: 0.85rem; color: #4B5EAA; margin: 0; font-weight: 500; line-height: 1.4;">
-        Protecting you from misinformation with AI-powered detection
-    </p>
-</div>
-""", unsafe_allow_html=True)
+def predict_news(text):
+    """Predict if the given news is fake or real."""
+    model, tokenizer = load_model_and_tokenizer()
+    preprocessor = get_preprocessor()
+    processed_text = preprocessor.preprocess_text(text)
+    encoding = tokenizer.encode_plus(
+        processed_text,
+        add_special_tokens=True,
+        max_length=MAX_SEQUENCE_LENGTH,
+        padding='max_length',
+        truncation=True,
+        return_attention_mask=True,
+        return_tensors='pt'
+    )
+    with torch.no_grad():
+        outputs = model(
+            encoding['input_ids'],
+            encoding['attention_mask']
+        )
+        probabilities = torch.softmax(outputs['logits'], dim=1)
+        prediction = torch.argmax(outputs['logits'], dim=1)
+        attention_weights = outputs['attention_weights']
+    attention_weights_np = attention_weights[0].cpu().numpy()
+    return {
+        'prediction': prediction.item(),
+        'label': 'FAKE' if prediction.item() == 1 else 'REAL',
+        'confidence': torch.max(probabilities, dim=1)[0].item(),
+        'probabilities': {
+            'REAL': probabilities[0][0].item(),
+            'FAKE': probabilities[0][1].item()
+        },
+        'attention_weights': attention_weights_np
+    }
 
-# Add footer info
-st.sidebar.markdown("""
-<div style="position: fixed; bottom: 1rem; left: 1rem; right: 1rem; text-align: center; padding: 0.5rem; background-color: rgba(255,255,255,0.9); border-radius: 8px; border-top: 1px solid #E0E0E0;">
-    <p style="font-size: 0.7rem; color: #888; margin: 0;">
-        © 2025 TruthCheck | AI-Powered
-    </p>
-</div>
-""", unsafe_allow_html=True)
+def plot_confidence(probabilities):
+    """Plot prediction confidence with simplified styling."""
+    fig = go.Figure(data=[
+        go.Bar(
+            x=list(probabilities.keys()),
+            y=list(probabilities.values()),
+            text=[f'{p:.1%}' for p in probabilities.values()],
+            textposition='auto',
+            marker=dict(
+                color=['#10b981', '#ef4444'],
+                line=dict(color='#ffffff', width=1),
+            ),
+        )
+    ])
+    fig.update_layout(
+        title={'text': 'Prediction Confidence', 'x': 0.5, 'xanchor': 'center', 'font': {'size': 18}},
+        xaxis=dict(title='Classification', titlefont={'size': 12}, tickfont={'size': 10}),
+        yaxis=dict(title='Probability', range=[0, 1], tickformat='.0%', titlefont={'size': 12}, tickfont={'size': 10}),
+        template='plotly_white',
+        height=300,
+        margin=dict(t=60, b=60)
+    )
+    return fig
 
-# Clean up the page variable to remove icons for the conditional logic
-page_clean = page.split(" ", 1)[1] if " " in page else page
+def plot_attention(text, attention_weights):
+    """Plot attention weights with simplified styling."""
+    tokens = text.split()[:20]
+    attention_weights = attention_weights[:len(tokens)]
+    if isinstance(attention_weights, (list, np.ndarray)):
+        attention_weights = np.array(attention_weights).flatten()
+    normalized_weights = attention_weights / max(attention_weights) if max(attention_weights) > 0 else attention_weights
+    colors = [f'rgba(99, 102, 241, {0.4 + 0.6 * float(w)})' for w in normalized_weights]
+    fig = go.Figure(data=[
+        go.Bar(
+            x=tokens,
+            y=attention_weights,
+            text=[f'{float(w):.3f}' for w in attention_weights],
+            textposition='auto',
+            marker=dict(color=colors),
+        )
+    ])
+    fig.update_layout(
+        title={'text': 'Attention Weights', 'x': 0.5, 'xanchor': 'center', 'font': {'size': 18}},
+        xaxis=dict(title='Words', tickangle=45, titlefont={'size': 12}, tickfont={'size': 10}),
+        yaxis=dict(title='Attention Score', titlefont={'size': 12}, tickfont={'size': 10}),
+        template='plotly_white',
+        height=350,
+        margin=dict(t=60, b=80)
+    )
+    return fig
 
-if __name__ == "__main__":
-    if page_clean == "Home":
-        if download_model():
-            from src.app import main
-            main()
+def main():
+    # Header
+    st.markdown("""
+    <div class="header">
+        <div class="container">
+            <h1 class="header-title">🛡️ TruthCheck</h1>
+        </div>
+    </div>
+    """, unsafe_allow_html=True)
+
+    # Hero Section
+    st.markdown("""
+    <div class="container">
+        <div class="hero">
+            <div class="hero-left">
+                <h2 class="hero-title">Instant Fake News Detection</h2>
+                <p class="hero-text">
+                    Verify news articles with our AI-powered tool, driven by BERT and BiLSTM for fast and accurate authenticity analysis.
+                </p>
+            </div>
+            <div class="hero-right">
+                <img src="/static/hero.png" alt="TruthCheck Illustration">
+            </div>
+        </div>
+    </div>
+    """, unsafe_allow_html=True)
+
+    # About Section
+    st.markdown("""
+    <div class="container">
+        <div class="about-section">
+            <h2 class="about-title">About TruthCheck</h2>
+            <p class="about-text">
+                TruthCheck uses a hybrid BERT-BiLSTM model to detect fake news with high accuracy. Paste an article below for instant analysis.
+            </p>
+        </div>
+    </div>
+    """, unsafe_allow_html=True)
+
+    # Input Section
+    st.markdown('<div class="container"><div class="input-container">', unsafe_allow_html=True)
+    news_text = st.text_area(
+        "Analyze a News Article",
+        height=150,
+        placeholder="Paste your news article here for instant AI analysis...",
+        key="news_input"
+    )
+    st.markdown('</div>', unsafe_allow_html=True)
+
+    # Analyze Button
+    st.markdown('<div class="container">', unsafe_allow_html=True)
+    col1, col2, col3 = st.columns([1, 2, 1])
+    with col2:
+        analyze_button = st.button("🔍 Analyze Now", key="analyze_button")
+    st.markdown('</div>', unsafe_allow_html=True)
+
+    if analyze_button:
+        if news_text and len(news_text.strip()) > 10:
+            with st.spinner("Analyzing article..."):
+                try:
+                    result = predict_news(news_text)
+                    st.markdown('<div class="container"><div class="results-container">', unsafe_allow_html=True)
+                    
+                    # Prediction Result
+                    col1, col2 = st.columns([1, 1], gap="medium")
+                    with col1:
+                        if result['label'] == 'FAKE':
+                            st.markdown(f'''
+                            <div class="result-card fake-news">
+                                <div class="prediction-badge">🚨 Fake News Detected <span class="confidence-score">{result["confidence"]:.1%}</span></div>
+                                <p>Our AI has identified this content as likely misinformation based on linguistic patterns and content analysis.</p>
+                            </div>
+                            ''', unsafe_allow_html=True)
+                        else:
+                            st.markdown(f'''
+                            <div class="result-card real-news">
+                                <div class="prediction-badge">✅ Authentic News <span class="confidence-score">{result["confidence"]:.1%}</span></div>
+                                <p>This content appears to be legitimate based on professional writing style and factual consistency.</p>
+                            </div>
+                            ''', unsafe_allow_html=True)
+                    
+                    with col2:
+                        st.markdown('<div class="chart-container">', unsafe_allow_html=True)
+                        st.plotly_chart(plot_confidence(result['probabilities']), use_container_width=True)
+                        st.markdown('</div>', unsafe_allow_html=True)
+                    
+                    # Attention Analysis
+                    st.markdown('<div class="chart-container">', unsafe_allow_html=True)
+                    st.plotly_chart(plot_attention(news_text, result['attention_weights']), use_container_width=True)
+                    st.markdown('</div></div></div>', unsafe_allow_html=True)
+                except Exception as e:
+                    st.markdown('<div class="container">', unsafe_allow_html=True)
+                    st.error(f"Error: {str(e)}. Please try again or contact support.")
+                    st.markdown('</div>', unsafe_allow_html=True)
         else:
-            st.markdown('<div class="flash-message error-message">Cannot start the application without the model file.</div>', unsafe_allow_html=True)
-    elif page_clean == "About":
-        from src.about import main
-        main()
-    elif page_clean == "Terms of Use":
-        from src.terms_of_use import main
-        main()
-    elif page_clean == "Privacy Policy":
-        from src.privacy_policy import main
-        main()
-    elif page_clean == "Team":
-        from src.team import main
-        main()
+            st.markdown('<div class="container">', unsafe_allow_html=True)
+            st.error("Please enter a news article (at least 10 words) for analysis.")
+            st.markdown('</div>', unsafe_allow_html=True)
+
+    # Footer
+    st.markdown("""
+    <div class="footer">
+        <p style="text-align: center; font-weight: 600; font-size: 16px;">💻 Developed with ❤️ using Streamlit | © 2025</p>
+    </div>
+    """, unsafe_allow_html=True)
+
+if __name__ == "__main__":
+    main()