Update app.py

app.py

@@ -1,113 +1,118 @@
-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:
+import streamlit as st
+import pandas as pd
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import subprocess
+
+# Ensure the spaCy model is downloaded
+subprocess.run(["python", "-m", "spacy", "download", "en_core_web_sm"])
+
+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.")