Fix UI slowness with caching and improve harder questions with specific locations/time

PERFORMANCE IMPROVEMENTS:
- Add

@st
.cache_data to data loading function to prevent reloading Data.csv on every interaction
- Add

@st
.cache_data to questions loading to cache questions.txt file reads
- These should significantly speed up initial UI loading

IMPROVED HARDER QUESTIONS:
- 'Which station in Ahmedabad shows highest PM2.5 variability in winter 2023?'
- 'Calculate PM2.5 improvement rate from 2022 to 2023 for Mumbai vs Delhi'
- 'Identify Gujarat cities with PM2.5 >50 μg/m³ for 6+ consecutive months in 2023'
- 'Compare PM2.5 vs PM10 correlation: winter vs summer across top 5 polluted cities'

Now questions are more specific with actual locations (Ahmedabad, Mumbai, Delhi, Gujarat) and time periods (winter 2023, 2022-2023, etc.)

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <[email protected]>

app.py

@@ -559,9 +559,13 @@ with header_col2:
 st.markdown("<hr style='margin: 0.25rem 0; border: none; border-top: 1px solid #e2e8f0;'>", unsafe_allow_html=True)
 
 
-# Load data with error handling
+# Load data with caching for better performance
+@st.cache_data
+def load_data():
+    return preprocess_and_load_df(join(self_path, "Data.csv"))
+
 try:
-    df = preprocess_and_load_df(join(self_path, "Data.csv"))
+    df = load_data()
     # Data loaded silently - no success message needed
 except Exception as e:
     st.error(f"Error loading data: {e}")
@@ -575,16 +579,21 @@ with st.sidebar:
     # Quick Queries Section - moved to top
     st.markdown("### Quick Queries")
     
-    # Load quick prompts
-    questions = []
-    questions_file = join(self_path, "questions.txt")
-    if os.path.exists(questions_file):
-        try:
-            with open(questions_file, 'r', encoding='utf-8') as f:
-                content = f.read()
-                questions = [q.strip() for q in content.split("\n") if q.strip()]
-        except Exception as e:
-            questions = []
+    # Load quick prompts with caching
+    @st.cache_data
+    def load_questions():
+        questions = []
+        questions_file = join(self_path, "questions.txt")
+        if os.path.exists(questions_file):
+            try:
+                with open(questions_file, 'r', encoding='utf-8') as f:
+                    content = f.read()
+                    questions = [q.strip() for q in content.split("\n") if q.strip()]
+            except Exception as e:
+                questions = []
+        return questions
+    
+    questions = load_questions()
     
     # Add default prompts if file doesn't exist or is empty
     if not questions:

questions.txt

@@ -8,10 +8,10 @@ Show seasonal pollution patterns
 Which areas exceed WHO guidelines?
 What are peak pollution hours?
 Show PM10 vs PM2.5 comparison
-Which station records highest variability in PM2.5?
-Calculate pollution improvement rate year-over-year by city
-Identify cities with PM2.5 levels consistently above 50 μg/m³ for >6 months
-Find correlation between PM2.5 and PM10 across different seasons and cities
+Which station in Ahmedabad shows highest PM2.5 variability in winter 2023?
+Calculate PM2.5 improvement rate from 2022 to 2023 for Mumbai vs Delhi
+Identify Gujarat cities with PM2.5 >50 μg/m³ for 6+ consecutive months in 2023
+Compare PM2.5 vs PM10 correlation: winter vs summer across top 5 polluted cities
 Compare weekday vs weekend levels
 Plot yearly trend analysis
 Show pollution distribution by city