# 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 src.models.hybrid_model import HybridFakeNewsDetector # from src.config.config import * # from src.data.preprocessor import TextPreprocessor # # Page config is set in main app.py # @st.cache_resource # def load_model_and_tokenizer(): # """Load the model and tokenizer (cached).""" # # Initialize model # model = HybridFakeNewsDetector( # bert_model_name=BERT_MODEL_NAME, # lstm_hidden_size=LSTM_HIDDEN_SIZE, # lstm_num_layers=LSTM_NUM_LAYERS, # dropout_rate=DROPOUT_RATE # ) # # Load trained weights # state_dict = torch.load(SAVED_MODELS_DIR / "final_model.pt", map_location=torch.device('cpu')) # # Filter out unexpected keys # model_state_dict = model.state_dict() # filtered_state_dict = {k: v for k, v in state_dict.items() if k in model_state_dict} # # Load the filtered state dict # model.load_state_dict(filtered_state_dict, strict=False) # model.eval() # # Initialize tokenizer # tokenizer = BertTokenizer.from_pretrained(BERT_MODEL_NAME) # return model, tokenizer # @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.""" # # Get model, tokenizer, and preprocessor from cache # model, tokenizer = load_model_and_tokenizer() # preprocessor = get_preprocessor() # # Preprocess text # processed_text = preprocessor.preprocess_text(text) # # Tokenize # 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' # ) # # Get prediction # 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'] # # Convert attention weights to numpy and get the first sequence # 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.""" # fig = go.Figure(data=[ # go.Bar( # x=list(probabilities.keys()), # y=list(probabilities.values()), # text=[f'{p:.2%}' for p in probabilities.values()], # textposition='auto', # ) # ]) # fig.update_layout( # title='Prediction Confidence', # xaxis_title='Class', # yaxis_title='Probability', # yaxis_range=[0, 1] # ) # return fig # def plot_attention(text, attention_weights): # """Plot attention weights.""" # tokens = text.split() # attention_weights = attention_weights[:len(tokens)] # Truncate to match tokens # # Ensure attention weights are in the correct format # if isinstance(attention_weights, (list, np.ndarray)): # attention_weights = np.array(attention_weights).flatten() # # Format weights for display # formatted_weights = [f'{float(w):.2f}' for w in attention_weights] # fig = go.Figure(data=[ # go.Bar( # x=tokens, # y=attention_weights, # text=formatted_weights, # textposition='auto', # ) # ]) # fig.update_layout( # title='Attention Weights', # xaxis_title='Tokens', # yaxis_title='Attention Weight', # xaxis_tickangle=45 # ) # return fig # def main(): # st.title("📰 Fake News Detection System") # st.write(""" # This application uses a hybrid deep learning model (BERT + BiLSTM + Attention) # to detect fake news articles. Enter a news article below to analyze it. # """) # # Sidebar # st.sidebar.title("About") # st.sidebar.info(""" # The model combines: # - BERT for contextual embeddings # - BiLSTM for sequence modeling # - Attention mechanism for interpretability # """) # # Main content # st.header("News Analysis") # # Text input # news_text = st.text_area( # "Enter the news article to analyze:", # height=200, # placeholder="Paste your news article here..." # ) # if st.button("Analyze"): # if news_text: # with st.spinner("Analyzing the news article..."): # # Get prediction # result = predict_news(news_text) # # Display result # col1, col2 = st.columns(2) # with col1: # st.subheader("Prediction") # if result['label'] == 'FAKE': # st.error(f"🔴 This news is likely FAKE (Confidence: {result['confidence']:.2%})") # else: # st.success(f"🟢 This news is likely REAL (Confidence: {result['confidence']:.2%})") # with col2: # st.subheader("Confidence Scores") # st.plotly_chart(plot_confidence(result['probabilities']), use_container_width=True) # # Show attention visualization # st.subheader("Attention Analysis") # st.write(""" # The attention weights show which parts of the text the model focused on # while making its prediction. Higher weights indicate more important tokens. # """) # st.plotly_chart(plot_attention(news_text, result['attention_weights']), use_container_width=True) # # Show model explanation # st.subheader("Model Explanation") # if result['label'] == 'FAKE': # st.write(""" # The model identified this as fake news based on: # - Linguistic patterns typical of fake news # - Inconsistencies in the content # - Attention weights on suspicious phrases # """) # else: # st.write(""" # The model identified this as real news based on: # - Credible language patterns # - Consistent information # - Attention weights on factual statements # """) # else: # st.warning("Please enter a news article to analyze.") # if __name__ == "__main__": # main() 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 src.models.hybrid_model import HybridFakeNewsDetector from src.config.config import * from src.data.preprocessor import TextPreprocessor @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 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.""" fig = go.Figure(data=[ go.Bar( x=list(probabilities.keys()), y=list(probabilities.values()), text=[f'{p:.2%}' for p in probabilities.values()], textposition='auto', marker_color=['#4B5EAA', '#FF6B6B'] ) ]) fig.update_layout( title='Prediction Confidence', xaxis_title='Class', yaxis_title='Probability', yaxis_range=[0, 1], template='plotly_white' ) return fig def plot_attention(text, attention_weights): """Plot attention weights.""" tokens = text.split() attention_weights = attention_weights[:len(tokens)] if isinstance(attention_weights, (list, np.ndarray)): attention_weights = np.array(attention_weights).flatten() formatted_weights = [f'{float(w):.2f}' for w in attention_weights] fig = go.Figure(data=[ go.Bar( x=tokens, y=attention_weights, text=formatted_weights, textposition='auto', marker_color='#4B5EAA' ) ]) fig.update_layout( title='Attention Weights', xaxis_title='Tokens', yaxis_title='Attention Weight', xaxis_tickangle=45, template='plotly_white' ) return fig def main(): # Hero section st.markdown("""

TrueCheck

Detect fake news with our advanced AI-powered system using BERT, BiLSTM, and Attention mechanisms.

Fake News Detection
""", unsafe_allow_html=True) # Sidebar info st.sidebar.markdown("---") st.sidebar.header("About TrueCheck") st.sidebar.markdown("""

TrueCheck uses a hybrid deep learning model combining:

""", unsafe_allow_html=True) # Main content st.header("Analyze News") news_text = st.text_area( "Enter the news article to analyze:", height=200, placeholder="Paste your news article here..." ) if st.button("Analyze", key="analyze_button"): if news_text: with st.spinner("Analyzing the news article..."): result = predict_news(news_text) col1, col2 = st.columns([1, 1], gap="large") with col1: st.markdown("### Prediction") if result['label'] == 'FAKE': st.markdown(f'
🔴 This news is likely FAKE (Confidence: {result["confidence"]:.2%})
', unsafe_allow_html=True) else: st.markdown(f'
🟢 This news is likely REAL (Confidence: {result["confidence"]:.2%})
', unsafe_allow_html=True) with col2: st.markdown("### Confidence Scores") st.plotly_chart(plot_confidence(result['probabilities']), use_container_width=True) st.markdown("### Attention Analysis") st.markdown("""

The attention weights show which parts of the text the model focused on while making its prediction. Higher weights indicate more important tokens.

""", unsafe_allow_html=True) st.plotly_chart(plot_attention(news_text, result['attention_weights']), use_container_width=True) st.markdown("### Model Explanation") if result['label'] == 'FAKE': st.markdown("""

The model identified this as fake news based on:

""", unsafe_allow_html=True) else: st.markdown("""

The model identified this as real news based on:

""", unsafe_allow_html=True) else: st.markdown('
Please enter a news article to analyze.
', unsafe_allow_html=True) if __name__ == "__main__": main()