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import streamlit as st
import pandas as pd
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from utils.text_processing import extract_text_from_pdf, split_into_clauses
from utils.model_utils import predict_unfairness
# Set page title and favicon
st.set_page_config(
page_title="Terms of Service Analyzer",
page_icon="📜",
layout="wide"
)
# Load model and tokenizer from Hugging Face
@st.cache_resource
def load_model():
model = AutoModelForSequenceClassification.from_pretrained("CodeHima/Tos-Roberta")
tokenizer = AutoTokenizer.from_pretrained("CodeHima/Tos-Roberta")
return model, tokenizer
model, tokenizer = load_model()
st.title("📜 Terms of Service Analyzer")
# File upload
uploaded_file = st.file_uploader("Choose a PDF or text file", type=["pdf", "txt"])
# Text input
text_input = st.text_area("Or paste your Terms of Service here")
if uploaded_file is not None or text_input:
# Create a progress bar
progress_bar = st.progress(0)
# Create a status text
status_text = st.empty()
if uploaded_file is not None:
status_text.text("Reading file...")
progress_bar.progress(10)
if uploaded_file.type == "application/pdf":
text = extract_text_from_pdf(uploaded_file)
else:
text = uploaded_file.getvalue().decode("utf-8")
else:
text = text_input
status_text.text("Splitting into clauses...")
progress_bar.progress(30)
clauses = split_into_clauses(text)
results = []
total_clauses = len(clauses)
for i, clause in enumerate(clauses):
status_text.text(f"Analyzing clause {i+1} of {total_clauses}...")
# Update progress calculation to ensure it's always between 0 and 100
progress = min(30 + int((i+1) / total_clauses * 60), 90)
progress_bar.progress(progress)
label, probabilities = predict_unfairness(clause, model, tokenizer)
results.append({
"clause": clause,
"label": label,
"probabilities": probabilities
})
status_text.text("Preparing results...")
progress_bar.progress(100)
df = pd.DataFrame(results)
# Calculate summary
total_clauses = len(df)
clearly_fair = sum(df['label'] == 'clearly_fair')
potentially_unfair = sum(df['label'] == 'potentially_unfair')
clearly_unfair = sum(df['label'] == 'clearly_unfair')
# Clear the progress bar and status text
progress_bar.empty()
status_text.empty()
# Display summary
st.header("Summary")
col1, col2, col3 = st.columns(3)
col1.metric("Clearly Fair", clearly_fair, f"{clearly_fair/total_clauses:.1%}")
col2.metric("Potentially Unfair", potentially_unfair, f"{potentially_unfair/total_clauses:.1%}")
col3.metric("Clearly Unfair", clearly_unfair, f"{clearly_unfair/total_clauses:.1%}")
# Recommendation
if clearly_unfair > 0 or potentially_unfair / total_clauses > 0.3:
st.warning("⚠️ Exercise caution! This ToS contains unfair or potentially unfair clauses.")
elif potentially_unfair > 0:
st.info("ℹ️ Proceed with awareness. This ToS contains some potentially unfair clauses.")
else:
st.success("✅ This ToS appears to be fair. Always read carefully nonetheless.")
# Display results
st.header("Detailed Analysis")
for _, row in df.iterrows():
if row['label'] == 'clearly_fair':
st.success(f"**{row['label'].replace('_', ' ').title()}:** {row['clause']}")
elif row['label'] == 'potentially_unfair':
st.warning(f"**{row['label'].replace('_', ' ').title()}:** {row['clause']}")
else:
st.error(f"**{row['label'].replace('_', ' ').title()}:** {row['clause']}")
st.write(f"Probabilities: Clearly Fair: {row['probabilities'][0]:.2f}, "
f"Potentially Unfair: {row['probabilities'][1]:.2f}, "
f"Clearly Unfair: {row['probabilities'][2]:.2f}")
st.divider()
else:
st.info("Please upload a file or paste your Terms of Service to begin analysis.") |