Update app.py

app.py

@@ -5,128 +5,79 @@ from datetime import datetime
 import time
 from collections import defaultdict
 import plotly.express as px
-import plotly.graph_objects as go
 
-class ColoringBookAnalyzer:
-    def __init__(self):
-        self.pytrends = TrendReq(hl='en-US', tz=360)
-        self.coloring_categories = [
-            'adult coloring books', 'mandala coloring', 'animal coloring',
-            'flower coloring', 'kids coloring books', 'fantasy coloring',
-            'anime coloring', 'pattern coloring', 'quotes coloring'
-        ]
-        
-    def analyze_trends(self, timeframe='today 3-m', geo=''):
-        trending_keywords = defaultdict(int)
-        results = []
-        
-        for category in self.coloring_categories:
-            try:
-                self.pytrends.build_payload(
-                    kw_list=[category],
-                    cat=0,
-                    timeframe=timeframe,
-                    geo=geo
-                )
-                
-                related_queries = self.pytrends.related_queries()
-                
-                if related_queries[category]['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
-                        })
-                
-                time.sleep(1)
-                
-            except Exception as e:
-                continue
-        
-        return pd.DataFrame(results)
-
-    def create_trends_plot(self, df):
-        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)
-        return fig
-
-    def get_competition_analysis(self, df):
-        competition_data = []
-        for _, row in df.nlargest(5, 'score').iterrows():
-            try:
-                self.pytrends.build_payload(
-                    kw_list=[row['keyword']],
-                    cat=0,
-                    timeframe='today 3-m'
-                )
-                
-                interest_over_time = self.pytrends.interest_over_time()
-                
-                if not interest_over_time.empty:
-                    avg_interest = interest_over_time[row['keyword']].mean()
-                    competition_level = "High" if avg_interest > 75 else "Medium" if avg_interest > 40 else "Low"
-                    competition_data.append({
-                        'keyword': row['keyword'],
-                        'average_interest': avg_interest,
-                        'competition_level': competition_level
+def analyze_coloring_trends(region='US'):
+    pytrends = TrendReq(hl='en-US', tz=360)
+    
+    coloring_categories = [
+        'adult coloring books', 'mandala coloring', 
+        'animal coloring', 'flower coloring', 
+        'kids coloring books'
+    ]
+    
+    trending_keywords = defaultdict(int)
+    results = []
+    
+    for category in coloring_categories:
+        try:
+            pytrends.build_payload(
+                kw_list=[category],
+                cat=0,
+                timeframe='today 3-m',
+                geo=region
+            )
+            
+            related_queries = pytrends.related_queries()
+            
+            if related_queries[category]['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
                     })
-                
-                time.sleep(1)
-                
-            except Exception as e:
-                continue
-                
-        return pd.DataFrame(competition_data)
-
-def analyze_coloring_books(region):
-    analyzer = ColoringBookAnalyzer()
+            
+            time.sleep(1)
+            
+        except Exception as e:
+            continue
     
-    # Get trends
-    trends_df = analyzer.analyze_trends(geo=region)
+    df = pd.DataFrame(results)
     
     # Create visualization
-    trends_plot = analyzer.create_trends_plot(trends_df)
+    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)
     
-    # Get competition analysis
-    competition_df = analyzer.get_competition_analysis(trends_df)
-    
-    # Format results
-    top_keywords = trends_df.nlargest(10, 'score')[['keyword', 'score', 'category']]
+    # Format text results
+    top_keywords = df.nlargest(10, 'score')[['keyword', 'score', 'category']]
     top_keywords_str = top_keywords.to_string()
     
-    competition_str = competition_df.to_string()
-    
-    # Generate recommendations
-    recommendations = """
-    Recommended Niches Based on Analysis:
-    1. Complex Adult Coloring Books
-    2. Simple Kids Coloring Books
-    3. Mandala Collections
-    4. Animal Themed Books
-    5. Seasonal Coloring Books
+    recommendations = f"""
+    Top Trending Categories in {region}:
+    1. {coloring_categories[0]}
+    2. {coloring_categories[1]}
+    3. {coloring_categories[2]}
     
-    Tips for Success:
-    - Focus on clear, high-quality designs
-    - Create both simple and complex versions
-    - Include 30-50 designs per book
-    - Price between $6.99 - $12.99
-    - Use relevant keywords in title and description
+    Recommended Actions:
+    - Focus on these trending keywords
+    - Create multiple variations
+    - Target different skill levels
+    - Consider seasonal themes
     """
     
-    return trends_plot, top_keywords_str, competition_str, recommendations
+    return fig, top_keywords_str, recommendations
 
 # Create Gradio interface
 iface = gr.Interface(
-    fn=analyze_coloring_books,
+    fn=analyze_coloring_trends,
     inputs=[
         gr.Dropdown(
             choices=['US', 'UK', 'CA', 'AU', 'DE', 'FR', 'ES', 'IT'],
@@ -137,15 +88,12 @@ iface = gr.Interface(
     outputs=[
         gr.Plot(label="Trending Keywords Visualization"),
         gr.Textbox(label="Top 10 Trending Keywords", lines=10),
-        gr.Textbox(label="Competition Analysis", lines=10),
         gr.Textbox(label="Recommendations", lines=10)
     ],
     title="Coloring Book Trend Analyzer",
-    description="Analyze trending keywords and competition in the coloring book market",
+    description="Analyze trending keywords in the coloring book market",
     theme="default"
 )
 
-# For local testing
-if __name__ == "__main__":
-    iface.launch()
-    
+# Launch the interface
+iface.launch()