Update app.py

app.py

@@ -3,23 +3,26 @@ import pandas as pd
 import io
 import matplotlib.pyplot as plt
 from script import getSearchResult, getClustersWithGraph, compare_clusters
+import os
 
 app = marimo.App()
 
+# 1) Upload + config
 @app.cell
 def _(mo):
     csv_upload = mo.ui.file(label="Upload Keyword CSV (1 column)")
-    import os
+    # pull secrets from env
     api_key = os.environ.get("GOOGLE_API_KEY", "")
-    cse_id = os.environ.get("GOOGLE_CSE_ID", "")
+    cse_id  = os.environ.get("GOOGLE_CSE_ID", "")
     country = mo.ui.text(label="Country Code (e.g. UK)", value="UK")
-    language = mo.ui.text(label="Language Code (e.g. EN)", value="EN")
-    database = mo.ui.text(label="SQLite DB Name", value="data.db")
-    serp_table = mo.ui.text(label="SERP Table", value="keywords_serps")
-    cluster_table = mo.ui.text(label="Cluster Table", value="keyword_clusters")
-    run_button = mo.ui.button(label="Run Clustering Comparison")
+    language= mo.ui.text(label="Language Code (e.g. EN)", value="EN")
+    database= mo.ui.text(label="SQLite DB Name", value="data.db")
+    serp_table   = mo.ui.text(label="SERP Table",    value="keywords_serps")
+    cluster_table= mo.ui.text(label="Cluster Table", value="keyword_clusters")
+    run_button   = mo.ui.button(label="Run Clustering Comparison")
     return csv_upload, api_key, cse_id, country, language, database, serp_table, cluster_table, run_button
 
+# 2) Timestamp selectors
 @app.cell
 def _(mo):
     timestamp_options = [
@@ -31,11 +34,11 @@ def _(mo):
         ("March 2025 core update (2025-04-03 00:00:00.000000)", "2025-04-03 00:00:00.000000"),
         ("June 2025 core update (2025-07-24 00:00:00.000000)", "2025-07-24 00:00:00.000000"),
     ]
-
     timestamp_1 = mo.ui.dropdown(timestamp_options, label="Choose Timestamp 1")
     timestamp_2 = mo.ui.dropdown(timestamp_options, label="Choose Timestamp 2")
     return timestamp_1, timestamp_2
 
+# 3) Read CSV
 @app.cell
 def _(csv_upload):
     if csv_upload.value:
@@ -45,25 +48,33 @@ def _(csv_upload):
         df_keywords, keywords = None, []
     return df_keywords, keywords
 
+# 4) Run search + clustering + comparison
 @app.cell
 def _(keywords, api_key, cse_id, country, language, database, serp_table, timestamp_1, timestamp_2, run_button):
     if run_button.clicked and keywords:
-        # Run search and store results
+        # 1st timestamp data fetch + cluster
         getSearchResult(keywords, language, country, api_key, cse_id, database, serp_table)
-
         fig1, clusters1 = getClustersWithGraph(database, serp_table, timestamp_1.value)
+
+        # 2nd timestamp data fetch + cluster
+        getSearchResult(keywords, language, country, api_key, cse_id, database, serp_table)
         fig2, clusters2 = getClustersWithGraph(database, serp_table, timestamp_2.value)
 
         movement = compare_clusters(clusters1, clusters2)
         return fig1, fig2, movement
+
+    # Explicit fallback
     return None, None, None
 
+# 5) Display the two network graphs
 @app.cell
 def _(fig1, fig2):
     if fig1 and fig2:
         display(fig1)
         display(fig2)
+    # no return needed here
 
+# 6) Show the movement table
 @app.cell
 def _(movement):
     if movement is not None: