Update app.py

app.py

@@ -1,110 +1,52 @@
 import gradio as gr
 from pytrends.request import TrendReq
 import pandas as pd
-from collections import defaultdict
 import plotly.express as px
 import time
 
-def analyze_coloring_trends(search_query='adult coloring books', region='US'):
-    # Initialize Google Trends connection
+# Function to fetch trends and plot data
+def fetch_trends(search_query='adult coloring books', region='US'):
     pytrends = TrendReq(hl='en-US', tz=360)
     
-    # Predefined categories for the search query
-    predefined_categories = [
-        'adult coloring books', 'mandala coloring', 
-        'animal coloring', 'flower coloring', 
-        'kids coloring books'
-    ]
+    # Set up the payload with the given query and region
+    pytrends.build_payload([search_query], cat=0, timeframe='today 12-m', geo=region)
     
-    # If the search query matches one of the predefined categories, use it
-    if search_query.lower() in predefined_categories:
-        coloring_categories = [search_query]
-    else:
-        # Otherwise, use the user-provided search term as a custom query
-        coloring_categories = [search_query]
-
-    trending_keywords = defaultdict(int)
-    results = []
+    # Fetch related queries
+    related_queries = pytrends.related_queries()
+    
+    # Check if there are any top related queries
+    if related_queries and related_queries.get(search_query, {}).get('top') is not None:
+        # Process the top related queries into a dataframe
+        df = related_queries[search_query]['top']
+        df = df[['query', 'value']]  # Filter only relevant columns
+        df = df.nlargest(10, 'value')  # Get top 10 trending queries
 
-    for category in coloring_categories:
-        try:
-            print(f"Fetching data for category: {category} in region: {region}")
-            
-            # Build payload with extended timeframe to capture more trends
-            pytrends.build_payload(
-                kw_list=[category],
-                cat=0,
-                timeframe='today 12-m',  # Extended timeframe for better results
-                geo=region
-            )
-            
-            # Fetch related queries
-            related_queries = pytrends.related_queries()
-            print(f"Related queries for {category}: {related_queries}")
-            
-            # Ensure data exists for the category
-            if related_queries and related_queries.get(category, {}).get('top') is not None:
-                for row in related_queries[category]['top'].itertuples():
-                    trending_keywords[row.query] += row.value
-                    results.append({
-                        'keyword': row.query,
-                        'score': row.value,
-                        'category': category
-                    })
-            else:
-                print(f"No data for category: {category} in region: {region}")
-            
-            time.sleep(3)  # Increase delay to avoid rate limiting
+        # Create the Plotly bar chart for visualization
+        fig = px.bar(
+            df, x='query', y='value', 
+            title=f"Top 10 Trending Keywords for '{search_query}' in {region}",
+            labels={'query': 'Keyword', 'value': 'Popularity Score'},
+            color='value', color_continuous_scale='Viridis'
+        )
+        fig.update_layout(xaxis_tickangle=-45)
         
-        except Exception as e:
-            print(f"Error fetching data for {category}: {e}")
-            continue
-    
-    # Create DataFrame from results
-    df = pd.DataFrame(results)
-    
-    # If DataFrame is empty, handle the case gracefully
-    if df.empty:
-        print("No data available for the selected category in this region.")
-        return gr.Plot.update(), "No data available for the selected category in this region.", ""
-    
-    # Create bar chart visualization
-    fig = px.bar(
-        df.nlargest(15, 'score'),
-        x='keyword',
-        y='score',
-        color='category',
-        title='Top 15 Trending Coloring Book Keywords',
-        labels={'keyword': 'Keyword', 'score': 'Popularity Score'}
-    )
-    fig.update_layout(xaxis_tickangle=-45)
-    
-    # Prepare top 10 keywords for display
-    top_keywords = df.nlargest(10, 'score')[['keyword', 'score', 'category']]
-    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]}
-    
-    Recommended Actions:
-    - Focus on these trending keywords
-    - Create multiple variations
-    - Target different skill levels
-    - Consider seasonal themes
-    """
+        # Convert dataframe to a string for display
+        top_keywords_str = df.to_string(index=False)
+        
+        # Return the plot and the keyword list
+        return fig, top_keywords_str
     
-    return fig, top_keywords_str, recommendations
+    else:
+        return None, "No data found for the query in this region."
 
-# Create Gradio interface with search bar
+# Create Gradio Interface
 iface = gr.Interface(
-    fn=analyze_coloring_trends,
+    fn=fetch_trends,
     inputs=[
         gr.Textbox(
             label="Enter Search Term",
             value="adult coloring books",  # Default search term
-            placeholder="e.g. adult coloring books, animal coloring, etc."
+            placeholder="e.g. adult coloring books, mandala coloring, etc."
         ),
         gr.Dropdown(
             choices=['US', 'UK', 'CA', 'AU', 'DE', 'FR', 'ES', 'IT', 'JP', 'BR', 'IN'],
@@ -114,11 +56,10 @@ iface = gr.Interface(
     ],
     outputs=[
         gr.Plot(label="Trending Keywords Visualization"),
-        gr.Textbox(label="Top 10 Trending Keywords", lines=10),
-        gr.Textbox(label="Recommendations", lines=10)
+        gr.Textbox(label="Top 10 Trending Keywords", lines=10)
     ],
-    title="Coloring Book Trend Analyzer",
-    description="Analyze trending keywords in the coloring book market",
+    title="Google Trends Keyword Analyzer",
+    description="Analyze trending keywords for any search term in various regions using Google Trends",
     theme="default"
 )