Update app.py

app.py

@@ -1,3 +1,148 @@
+# 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_Entire_Data_Insights_Final_v2.csv")
+#     df['status_date'] = pd.to_datetime(df['status_date'], format='%d-%m-%Y', errors='coerce')
+#     df = df.dropna(subset=['status_date'])
+#     df["llama_trend_summary"] = df["llama_trend_summary"].fillna("")
+#     df["llama_insight"] = df["llama_insight"].fillna("")
+#     df["summary_insight"] = df["llama_trend_summary"] + "\n" + df["llama_insight"]
+#     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=60, min_length=30, 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_topic_match(query, df):
+#     query_lower = query.lower()
+#     return df[
+#         df['category_&_subcategory_standardized'].fillna('').str.lower().str.contains(query_lower) |
+#         df['intent_standardized'].fillna('').str.lower().str.contains(query_lower) |
+#         df['legislative_goal_standardized'].fillna('').str.lower().str.contains(query_lower) |
+#         df['policy_impact_areas_standardized'].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 in higher education in 2024’):")
+
+# if query:
+#     mon, yr = extract_month_year(query)
+#     df2 = extract_topic_match(query, df)
+
+#     if df2.empty:
+#         df2 = df
+#     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['summary_insight'].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)[:5]:
+#                 row = df2.iloc[idx]
+#                 date = row['status_date'].date()
+#                 bill_number = row['bill_number']
+#                 full_url = row['url']
+#                 cat_std = row['Category & Subcategory']
+#                 goal = row['Legislative Goal']
+#                 impact = row['Policy Impact Areas']
+#                 provision = row['Key Provisions']
+#                 intent = row['Intent']
+#                 stance = row['Stance']
+#                 description = row['description']
+#                 summary = row['summary']
+
+#                 trend = clean_text(row['llama_trend_summary'])
+#                 insight = clean_text(row['llama_insight'])
+
+#                 st.markdown(f"**Date:** {date} | **Bill Number:** {bill_number} | **Score:** {score:.2f}")
+#                 st.markdown(f"**Category:** {cat_std}")
+#                 st.markdown(f"**Goal:** {goal}")
+#                 st.markdown(f"**Intent:** {intent} | **Stance:** {stance}")
+#                 st.markdown(f"**Policy Impacy Area:** {impact}")
+#                 st.markdown(f"**Key Provision:** {provision}")
+#                 st.markdown(f"**Description:** {description}")
+#                 st.markdown(f"**Summary:** {summary}")
+#                 st.markdown(f"Trend Summary:{trend}")
+#                 st.markdown(f"Actionable Insight:{insight}")
+#                 st.markdown(f"[View Full Bill Text]({full_url})\n")
+#                 st.divider()
+
+#                 collected.append(row['summary_insight'])
+
+#             st.subheader("RAG-Generated Overall Summary")
+#             summary = rag_summarize(collected, summarizer)
+#             st.success(summary)
+
 import streamlit as st
 import pandas as pd
 import re
@@ -21,7 +166,11 @@ def load_data():
     df = df.dropna(subset=['status_date'])
     df["llama_trend_summary"] = df["llama_trend_summary"].fillna("")
     df["llama_insight"] = df["llama_insight"].fillna("")
-    df["summary_insight"] = df["llama_trend_summary"] + "\n" + df["llama_insight"]
+    df["description"] = df["description"].fillna("")
+    df["summary"] = df["summary"].fillna("")
+    df["Policy Impact Areas"] = df["Policy Impact Areas"].fillna("")
+    df["Key Provisions"] = df["Key Provisions"].fillna("")
+    # Optional: Add more preprocessing if needed
     return df
 
 @st.cache_resource
@@ -66,6 +215,7 @@ def extract_topic_match(query, df):
     query_lower = query.lower()
     return df[
         df['category_&_subcategory_standardized'].fillna('').str.lower().str.contains(query_lower) |
+        df['category_granular_standardized'].fillna('').str.lower().str.contains(query_lower) |
         df['intent_standardized'].fillna('').str.lower().str.contains(query_lower) |
         df['legislative_goal_standardized'].fillna('').str.lower().str.contains(query_lower) |
         df['policy_impact_areas_standardized'].fillna('').str.lower().str.contains(query_lower)
@@ -90,21 +240,29 @@ if query:
         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}**")
+            st.info(f"Filtering by date: **{datetime(yr, mon, 1):%B %Y}**")
         else:
-            st.info(f" Filtering by year: **{yr}**")
+            st.info(f"Filtering by year: **{yr}**")
 
     if df2.empty:
         st.warning("No matching records found.")
     else:
-        texts = df2['summary_insight'].tolist()
+        texts = df2.apply(lambda row: "\n".join([
+            clean_text(str(row.get('llama_trend_summary', ''))),
+            clean_text(str(row.get('llama_insight', ''))),
+            str(row.get('summary', '')),
+            str(row.get('description', '')),
+            str(row.get('Policy Impact Areas', '')),
+            str(row.get('Key Provisions', ''))
+        ]), axis=1).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")
+            st.subheader("Top Matching Insights")
             collected = []
 
             for idx, score in sorted(res, key=lambda x: x[1], reverse=True)[:5]:
@@ -120,7 +278,6 @@ if query:
                 stance = row['Stance']
                 description = row['description']
                 summary = row['summary']
-
                 trend = clean_text(row['llama_trend_summary'])
                 insight = clean_text(row['llama_insight'])
 
@@ -128,19 +285,24 @@ if query:
                 st.markdown(f"**Category:** {cat_std}")
                 st.markdown(f"**Goal:** {goal}")
                 st.markdown(f"**Intent:** {intent} | **Stance:** {stance}")
-                st.markdown(f"**Policy Impacy Area:** {impact}")
+                st.markdown(f"**Policy Impact Area:** {impact}")
                 st.markdown(f"**Key Provision:** {provision}")
                 st.markdown(f"**Description:** {description}")
                 st.markdown(f"**Summary:** {summary}")
-                st.markdown(f"Trend Summary:{trend}")
-                st.markdown(f"Actionable Insight:{insight}")
-                st.markdown(f"[View Full Bill Text]({full_url})\n")
+                st.markdown(f"Trend Summary: {trend}")
+                st.markdown(f"Actionable Insight: {insight}")
+                st.markdown(f"[View Full Bill Text]({full_url})")
                 st.divider()
 
-                collected.append(row['summary_insight'])
+                collected.append("\n".join([
+                    trend, insight, summary, description, impact, provision
+                ]))
+
+            with st.expander("View summarized insights context"):
+                for i, t in enumerate(collected[:5]):
+                    st.markdown(f"**Insight #{i+1}:**\n{t}\n")
 
             st.subheader("RAG-Generated Overall Summary")
             summary = rag_summarize(collected, summarizer)
             st.success(summary)
 
-