Update app.py

app.py

@@ -1,464 +1,192 @@
-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')
-
-# Add project root to Python path
-project_root = Path(__file__).parent.parent
-sys.path.append(str(project_root))
+from pathlib import Path
+import os
+import gdown
+import streamlit as st
 
-from src.models.hybrid_model import HybridFakeNewsDetector
-from src.config.config import *
-from src.data.preprocessor import TextPreprocessor
+# 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"
+)
 
-# Custom CSS for streamlined styling
+# Custom CSS for modern styling
 st.markdown("""
 <style>
-    /* 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;
-    }
-
-    /* Header */
-    .header {
-        padding: 1rem 0;
-        text-align: center;
-    }
-
-    .header-title {
-        font-size: 2rem;
-        font-weight: 800;
-        color: #1a202c;
-        display: inline-flex;
-        align-items: center;
-        gap: 0.5rem;
-    }
-
-    /* Hero Section */
-    .hero {
-        display: flex;
-        align-items: center;
-        gap: 2rem;
-        margin-bottom: 2rem;
-    }
-
-    .hero-left {
-        flex: 1;
+    @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;
         padding: 1rem;
     }
-
-    .hero-right {
-        flex: 1;
-        display: flex;
-        align-items: center;
-        justify-content: center;
-    }
-
-    .hero-right img {
-        max-width: 100%;
-        height: auto;
+    
+    .stButton>button {
+        background-color: #4B5EAA;
+        color: white;
         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;
+        padding: 0.5rem 1rem;
         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;
+        transition: background-color 0.3s;
     }
-
-    /* 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;
+    
+    .stButton>button:hover {
+        background-color: #3A4A8C;
     }
-
-    /* Results Section */
-    .results-container {
-        margin-top: 1rem;
-        padding: 1rem;
+    
+    .stTextArea textarea {
+        border: 1px solid #E0E0E0;
         border-radius: 8px;
+        padding: 1rem;
     }
-
-    .result-card {
+    
+    .hero-section {
+        background-color: #F4F7FA;
+        padding: 2rem;
+        border-radius: 12px;
+        margin-bottom: 2rem;
+    }
+    
+    .flash-message {
         padding: 1rem;
         border-radius: 8px;
-        border-left: 4px solid transparent;
         margin-bottom: 1rem;
+        font-weight: 600;
     }
-
-    .fake-news {
-        background: #fef2f2;
-        border-left-color: #ef4444;
+    
+    .success-message {
+        background-color: #E6F4EA;
+        color: #2E7D32;
     }
-
-    .real-news {
-        background: #ecfdf5;
-        border-left-color: #10b981;
+    
+    .error-message {
+        background-color: #FEE2E2;
+        color: #B71C1C;
     }
-
-    .prediction-badge {
-        font-weight: 600;
-        font-size: 1rem;
-        margin-bottom: 0.5rem;
-        display: flex;
-        align-items: center;
+    
+    /* Enhanced sidebar styling */
+    .stRadio > div {
         gap: 0.5rem;
     }
-
-    .confidence-score {
-        font-weight: 600;
-        margin-left: auto;
-        font-size: 1rem;
-    }
-
-    /* Chart Containers */
-    .chart-container {
-        padding: 1rem;
+    
+    .stRadio > div > label {
+        padding: 0.5rem 1rem;
         border-radius: 8px;
-        margin: 1rem 0;
+        transition: all 0.3s ease;
+        cursor: pointer;
+        font-weight: 500;
     }
-
-    /* Footer */
-    .footer {
-        margin-top: 2rem;
-        padding: 1rem 0;
-        text-align: center;
-        border-top: 1px solid #e5e7eb;
+    
+    .stRadio > div > label:hover {
+        background-color: #E8F0FE;
+        transform: translateX(5px);
+    }
+    
+    .stRadio > div > label[data-checked="true"] {
+        background-color: #4B5EAA;
+        color: white;
     }
 </style>
 """, unsafe_allow_html=True)
 
-@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
+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 get_preprocessor():
-    """Get the text preprocessor (cached)."""
-    return TextPreprocessor()
-
-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
-    }
-
-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
-
-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
-
-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)
+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)
 
-    # 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)
+st.sidebar.markdown("---")
+
+# Navigation with icons
+page = st.sidebar.radio(
+    "Navigate",
+    [
+        "🏠 Home",
+        "ℹ️ About", 
+        "📋 Terms of Use",
+        "🔒 Privacy Policy",
+        "👥 Contact"
+    ],
+    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)
 
-    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="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)
+# 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)
 
-    # 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)
+# Clean up the page variable to remove icons for the conditional logic
+page_clean = page.split(" ", 1)[1] if " " in page else page
 
 if __name__ == "__main__":
-    main()
+    if page_clean == "Home":
+        if download_model():
+            from src.app import main
+            main()
+        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 == "Contact":
+        from src.contact import main
+        main()