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import streamlit as st
import torch
from transformers import BertTokenizer, BertForSequenceClassification
from tqdm import tqdm # Import tqdm for progress bar
import time # for time taken calc
# Load pre-trained model
label_dict = {"Urgency": 0, "Not Dark Pattern": 1, "Scarcity": 2, "Misdirection": 3, "Social Proof": 4, "Obstruction": 5, "Sneaking": 6, "Forced Action": 7}
model = BertForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=len(label_dict))
# Load fine-tuned weights
fine_tuned_model_path = "models/finetuned_BERT_5k_epoch_5.model"
model.load_state_dict(torch.load(fine_tuned_model_path, map_location=torch.device('cpu')))
# Preprocess the new text
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)
# Function to map numeric label to dark pattern name
def get_dark_pattern_name(label):
reverse_label_dict = {v: k for k, v in label_dict.items()}
return reverse_label_dict[label]
def find_dark_pattern(text_predict):
encoded_text = tokenizer.encode_plus(
text_predict,
add_special_tokens=True,
return_attention_mask=True,
pad_to_max_length=True,
max_length=256,
return_tensors='pt'
)
# Making the predictions
model.eval()
with torch.no_grad():
inputs = {
'input_ids': encoded_text['input_ids'],
'attention_mask': encoded_text['attention_mask']
}
outputs = model(**inputs)
predictions = outputs.logits
# Post-process the predictions
probabilities = torch.nn.functional.softmax(predictions, dim=1)
predicted_label = torch.argmax(probabilities, dim=1).item()
return get_dark_pattern_name(predicted_label)
# Streamlit app
def main():
# navigation
st.page_link("app.py", label="Home", icon="🏠")
st.page_link("pages/page_1.py", label="Training Metrics", icon="1️⃣")
# st.page_link("pages/page_2.py", label="Page 2", icon="2️⃣")
st.page_link("https://github.com/4darsh-Dev/CogniGaurd", label="GitHub", icon="🌎")
# Set page title
st.title("Dark Pattern Detector")
# Display welcome message
st.write("Welcome to Dark Pattern Detector powered by CogniGuard")
#
st.write("#### Built with Fine-Tuned BERT and Hugging Face Transformers")
# Get user input
text_to_predict = st.text_input("Enter the text to find Dark Pattern")
if st.button("Predict"):
# Record the start time
start_time = time.time()
# Add a simple progress message
st.write("Predicting Dark Pattern...")
progress_bar = st.progress(0)
for i in tqdm(range(10), desc="Predicting", unit="prediction"):
predicted_darkp = find_dark_pattern(text_to_predict)
progress_bar.progress((i + 1) * 10)
time.sleep(0.5) # Simulate some processing time
# Record the end time
end_time = time.time()
# Calculate the total time taken
total_time = end_time - start_time
# Display the predicted dark pattern and total time taken
st.write(f"Result: {predicted_darkp}")
st.write(f"Total Time Taken: {total_time:.2f} seconds")
# Add footer
st.markdown('<p style="text-align:center;">Made with ❤️ by <a href="https://www.adarshmaurya.onionreads.com">Adarsh Maurya</a></p>', unsafe_allow_html=True)
# Add page visit count
with open("assets/counter.txt", "r") as f:
pVisit = int(f.read())
pVisit += 1
with open("assets/counter.txt", "w") as f:
f.write(str(pVisit))
# Display page visit count
st.markdown(f'<p style="text-align:center;">Page Visits: {pVisit}</p>', unsafe_allow_html=True)
# Run the app
if __name__ == "__main__":
main()
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