Update app.py

app.py

@@ -485,6 +485,196 @@
 
 ## NEW ONE
 
+# import streamlit as st
+# import pandas as pd
+# import re
+# from sentence_transformers import SentenceTransformer
+# from transformers import pipeline
+# from sklearn.metrics.pairwise import cosine_similarity
+# from sklearn.feature_extraction.text import TfidfVectorizer
+# from datetime import datetime
+
+# def clean_text(text):
+#     text = re.sub(r"(?i)(here is|here are) the requested output[s]*[:]*", "", text)
+#     text = re.sub(r"(?i)let me know if you'd like.*", "", text)
+#     text = re.sub(r"(?i)trend summary[:]*", "", text)
+#     text = re.sub(r"(?i)actionable insight[:]*", "", text)
+#     return text.strip()
+
+# @st.cache_data
+# def load_data():
+#     df = pd.read_csv("Illinois_Education_Bills_Summarized_With Features_2021_2025_07182025.csv")
+#     df['status_date'] = pd.to_datetime(df['status_date'], format='%d-%m-%Y', errors='coerce')
+#     df = df.dropna(subset=['status_date'])
+
+#     for col in ["Legislative Goal", "Policy Impact Areas", "Key Provisions",
+#                 "Intended Beneficiaries", "Potential Impact", "description"]:
+#         df[col] = df[col].fillna("")
+
+#     df["combined_text"] = (
+#         "Legislative Goal: " + df["Legislative Goal"] + "\n" +
+#         "Policy Impact Areas: " + df["Policy Impact Areas"] + "\n" +
+#         "Key Provisions: " + df["Key Provisions"] + "\n" +
+#         "Intended Beneficiaries: " + df["Intended Beneficiaries"] + "\n" +
+#         "Potential Impact: " + df["Potential Impact"] + "\n" +
+#         "Description: " + df["description"]
+#     )
+
+#     return df
+
+# @st.cache_resource
+# def load_models():
+#     embed_model = SentenceTransformer('all-MiniLM-L6-v2')
+#     summarizer = pipeline("summarization", model="t5-small", tokenizer="t5-small")
+#     return embed_model, summarizer
+
+# @st.cache_data
+# def compute_embeddings(texts, _model):
+#     return _model.encode(texts, show_progress_bar=True)
+
+# def semantic_search(query, embeddings, model, threshold=0.5):  
+#     query_embedding = model.encode([query])
+#     sims = cosine_similarity(query_embedding, embeddings)[0]
+#     return [(i, s) for i, s in enumerate(sims) if s > threshold]
+
+# def rag_summarize(texts, summarizer, top_k=5):
+#     if not texts:
+#         return "No relevant content to summarize."
+#     vect = TfidfVectorizer()
+#     m = vect.fit_transform(texts)
+#     mean_vec = m.mean(axis=0).A
+#     scores = cosine_similarity(mean_vec, m).flatten()
+#     top_indices = scores.argsort()[::-1][:top_k]
+#     ctx = "\n".join(texts[i] for i in top_indices)
+#     prompt = "summarize: " + ctx[:1024]
+#     out = summarizer(prompt, max_length=200, min_length=80, do_sample=False)  
+#     return out[0]['summary_text']
+
+# def extract_month_year(q):
+#     month_map = {m: i for i, m in enumerate(
+#         ["january", "february", "march", "april", "may", "june",
+#          "july", "august", "september", "october", "november", "december"], 1)}
+#     ql = q.lower()
+#     mon = next((v for k, v in month_map.items() if k in ql), None)
+#     ym = re.search(r"(19|20)\d{2}", q)
+#     yr = int(ym.group()) if ym else None
+#     return mon, yr
+
+# def extract_date_range(query):
+#     month_map = {
+#         "january": 1, "february": 2, "march": 3, "april": 4, "may": 5, "june": 6,
+#         "july": 7, "august": 8, "september": 9, "october": 10, "november": 11, "december": 12
+#     }
+
+#     patterns = [
+#         r"(?i)(?:from|between)?\s*([a-zA-Z]+)\s+(\d{4})\s*(?:to|through|and|-)\s*([a-zA-Z]+)\s+(\d{4})",
+#     ]
+
+#     for pattern in patterns:
+#         match = re.search(pattern, query)
+#         if match:
+#             start_month_str, start_year = match.group(1).lower(), int(match.group(2))
+#             end_month_str, end_year = match.group(3).lower(), int(match.group(4))
+
+#             start_month = month_map.get(start_month_str)
+#             end_month = month_map.get(end_month_str)
+
+#             if start_month and end_month:
+#                 start_date = datetime(start_year, start_month, 1)
+#                 end_date = datetime(end_year, end_month, 28)
+#                 return start_date, end_date
+
+#     return None, None
+
+
+# def extract_topic_match(query, df):
+#     query_lower = query.lower()
+#     return df[
+#         df['Category & Subcategory'].fillna('').str.lower().str.contains(query_lower) |
+#         df['Intent'].fillna('').str.lower().str.contains(query_lower) |
+#         df['Legislative Goal'].fillna('').str.lower().str.contains(query_lower) |
+#         df['Policy Impact Areas'].fillna('').str.lower().str.contains(query_lower) |
+#         df['Key Provisions'].fillna('').str.lower().str.contains(query_lower) |
+#         df['Potential Impact'].fillna('').str.lower().str.contains(query_lower)
+#     ]
+
+
+# st.set_page_config(page_title="IL Legislative Trends Q&A", layout="wide")
+# st.title("Illinois Legislative Trends Q&A")
+# st.markdown("Ask about trends in topics like higher education, funding, etc.")
+
+# df = load_data()
+# embed_model, summarizer = load_models()
+
+# query = st.text_input("Ask a question (e.g., ‘Trends from Jan 2024 to May 2025’):")
+
+# if query:
+#     start_date, end_date = extract_date_range(query)
+#     df2 = extract_topic_match(query, df)
+
+#     if df2.empty:
+#         df2 = df
+
+#     if start_date and end_date:
+#         df2 = df2[(df2['status_date'] >= start_date) & (df2['status_date'] <= end_date)]
+#         st.info(f"Filtering between: **{start_date:%B %Y}** and **{end_date:%B %Y}**")
+#     else:
+#         mon, yr = extract_month_year(query)
+#         if yr:
+#             df2 = df2[df2['status_date'].dt.year == yr]
+#             if mon:
+#                 df2 = df2[df2['status_date'].dt.month == mon]
+#                 st.info(f"Filtering by date: **{datetime(yr, mon, 1):%B %Y}**")
+#             else:
+#                 st.info(f"Filtering by year: **{yr}**")
+
+#     if df2.empty:
+#         st.warning("No matching records found.")
+#     else:
+#         texts = df2['combined_text'].tolist()
+#         embs = compute_embeddings(texts, _model=embed_model)
+#         res = semantic_search(query, embs, embed_model, threshold=0.5)
+
+#         if not res:
+#             st.warning("No relevant insights found.")
+#         else:
+#             st.subheader("Top Matching Insights")
+#             collected = []
+
+#             for idx, score in sorted(res, key=lambda x: x[1], reverse=True)[:10]: 
+#                 row = df2.iloc[idx]
+#                 date = row['status_date'].date()
+#                 bill_number = row['bill_number']
+#                 full_url = row['url']
+#                 cat = row.get('Category & Subcategory', '')
+#                 bene = row.get('Intended Beneficiaries', '')
+#                 goal = row.get('Legislative Goal', '')
+#                 impact = row.get('Policy Impact Areas', '')
+#                 provision = row.get('Key Provisions', '')
+#                 intent = row.get('Intent', '')
+#                 stance = row.get('Stance', '')
+#                 description = row.get('description', '')
+
+#                 st.markdown(f"**Date:** {date} | **Bill Number:** {bill_number} | **Score:** {score:.2f}")
+#                 st.markdown(f"**Category:** {cat}")
+#                 st.markdown(f"**Intended Beneficiaries:** {bene}")
+#                 st.markdown(f"**Goal:** {goal}")
+#                 st.markdown(f"**Intent:** {intent} | **Stance:** {stance}")
+#                 st.markdown(f"**Policy Impact Area:** {impact}")
+#                 st.markdown(f"**Key Provision:** {provision}")
+#                 st.markdown(f"**Description:** {description}")
+#                 st.markdown(f"[View Full Bill Text]({full_url})\n")
+#                 st.divider()
+
+#                 collected.append(row['combined_text'])
+
+#             st.subheader("RAG-Generated Overall Summary")
+#             summary = rag_summarize(collected, summarizer)
+#             st.success(summary)
+
+
+#BART
+
 import streamlit as st
 import pandas as pd
 import re
@@ -525,7 +715,8 @@ def load_data():
 @st.cache_resource
 def load_models():
     embed_model = SentenceTransformer('all-MiniLM-L6-v2')
-    summarizer = pipeline("summarization", model="t5-small", tokenizer="t5-small")
+    # Changed summarization model to facebook/bart-large-cnn for better summary quality
+    summarizer = pipeline("summarization", model="facebook/bart-large-cnn", tokenizer="facebook/bart-large-cnn")
     return embed_model, summarizer
 
 @st.cache_data
@@ -674,3 +865,4 @@ if query:
 
 
 
+