updated files

app.py

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+# app.py
+import streamlit as st
+from transformers import pipeline
+import spacy
+from collections import Counter
+import matplotlib.pyplot as plt
+
+# Load AI models ONCE at startup to cache them and avoid reloading on every interaction
+@st.cache_resource
+def load_summarizer():
+    """Load the text summarization model"""
+    return pipeline("summarization", model="facebook/bart-large-cnn")
+
+@st.cache_resource
+def load_ner_model():
+    """Load the Named Entity Recognition model"""
+    return spacy.load("en_core_web_sm")
+
+# Initialize the models
+summarizer = load_summarizer()
+nlp = load_ner_model()
+
+def summarize_text(text):
+    """Function to summarize long text"""
+    # Limit input text to avoid model limits
+    input_text = text[:2000]
+    summary = summarizer(input_text, max_length=150, min_length=30, do_sample=False)
+    return summary[0]['summary_text']
+
+def extract_entities(text):
+    """Function to find people, orgs, money, and laws"""
+    doc = nlp(text)
+    entities = []
+    for ent in doc.ents:
+        # Filter for only the entity types we care about
+        if ent.label_ in ['PERSON', 'ORG', 'GPE', 'MONEY', 'LAW']:
+            entities.append((ent.text, ent.label_))
+    return entities
+
+    # app.py (continued)
+# Configure the page
+st.set_page_config(page_title="Policy Lens", page_icon="📜", layout="wide")
+st.title("📜 Policy Lens")
+st.markdown("**AI-Powered Legislative Analysis** - Paste a bill or policy below to get a plain language summary and key insights.")
+
+# Input section
+input_text = st.text_area("Paste Legislative Text Here:", height=250, placeholder="Paste the text of a bill, policy, or news article here...")
+
+if st.button("Analyze", type="primary") and input_text:
+    with st.spinner("Analyzing text with AI..."):
+        
+        # Create a layout with columns
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            st.header("📋 Summary")
+            summary = summarize_text(input_text)
+            st.success(summary)
+        
+        with col2:
+            st.header("🧠 Key Entities")
+            entities = extract_entities(input_text)
+            
+            # Categorize the entities
+            people = [text for text, label in entities if label == 'PERSON']
+            organizations = [text for text, label in entities if label == 'ORG']
+            money = [text for text, label in entities if label == 'MONEY']
+            locations = [text for text, label in entities if label == 'GPE']
+            
+            # Display the entities in an organized way
+            if people:
+                st.write("**People:**", ", ".join(set(people))) # Use set() to remove duplicates
+            if organizations:
+                st.write("**Organizations:**", ", ".join(set(organizations)))
+            if money:
+                st.write("**Financials:**", ", ".join(set(money)))
+            if locations:
+                st.write("**Locations:**", ", ".join(set(locations)))
+        
+        # Visualization section (optional but impressive)
+        st.header("📊 Entity Frequency")
+        if entities:
+            # Count the most common entities
+            entity_counts = Counter([label for text, label in entities])
+            
+            # Create a simple bar chart
+            fig, ax = plt.subplots()
+            ax.bar(entity_counts.keys(), entity_counts.values())
+            ax.set_ylabel('Frequency')
+            ax.set_title('Most Common Entity Types')
+            plt.xticks(rotation=45)
+            st.pyplot(fig)
+        else:
+            st.info("No significant entities found to display.")
+            
+else:
+    st.info("👆 Please paste some text to analyze. For demo purposes, you can find text on sites like congress.gov")
+
+# Add a footer
+st.markdown("---")
+st.caption("Policy Lens uses Facebook's BART model for summarization and spaCy for entity recognition.")