Update app.py

app.py

@@ -1,14 +1,15 @@
 import gradio as gr
 from pytrends.request import TrendReq
 import pandas as pd
-from datetime import datetime
-import time
 from collections import defaultdict
 import plotly.express as px
+import time
 
 def analyze_coloring_trends(region='US'):
+    # Initialize Google Trends connection
     pytrends = TrendReq(hl='en-US', tz=360)
     
+    # Define categories to analyze
     coloring_categories = [
         'adult coloring books', 'mandala coloring', 
         'animal coloring', 'flower coloring', 
@@ -17,19 +18,24 @@ def analyze_coloring_trends(region='US'):
     
     trending_keywords = defaultdict(int)
     results = []
-    
+
     for category in coloring_categories:
         try:
+            print(f"Fetching data for category: {category}")
+            
+            # Build payload with extended timeframe to capture more trends
             pytrends.build_payload(
                 kw_list=[category],
                 cat=0,
-                timeframe='today 3-m',
+                timeframe='today 12-m',  # Extended timeframe for better results
                 geo=region
             )
             
+            # Fetch related queries
             related_queries = pytrends.related_queries()
             
-            if related_queries[category]['top'] is not None:
+            # Ensure data exists for the category
+            if related_queries and related_queries[category]['top'] is not None:
                 for row in related_queries[category]['top'].itertuples():
                     trending_keywords[row.query] += row.value
                     results.append({
@@ -37,15 +43,24 @@ def analyze_coloring_trends(region='US'):
                         'score': row.value,
                         'category': category
                     })
+            else:
+                print(f"No data for category: {category}")
             
-            time.sleep(1)
-            
+            time.sleep(1)  # Pause to respect Google Trends rate limits
+        
         except Exception as e:
+            print(f"Error fetching data for {category}: {e}")
             continue
     
+    # Create DataFrame from results
     df = pd.DataFrame(results)
     
-    # Create visualization
+    # Check if DataFrame has any data
+    if df.empty:
+        print("No data was retrieved.")
+        return "No data available for the selected categories in this region.", "", ""
+    
+    # Create bar chart visualization
     fig = px.bar(
         df.nlargest(15, 'score'),
         x='keyword',
@@ -56,10 +71,11 @@ def analyze_coloring_trends(region='US'):
     )
     fig.update_layout(xaxis_tickangle=-45)
     
-    # Format text results
+    # Prepare top 10 keywords for display
     top_keywords = df.nlargest(10, 'score')[['keyword', 'score', 'category']]
-    top_keywords_str = top_keywords.to_string()
+    top_keywords_str = top_keywords.to_string(index=False)
     
+    # Recommendations based on the analysis
     recommendations = f"""
     Top Trending Categories in {region}:
     1. {coloring_categories[0]}