Create App.py

App.py

@@ -0,0 +1,151 @@
+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 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
+                    })
+                
+                time.sleep(1)
+                
+            except Exception as e:
+                continue
+                
+        return pd.DataFrame(competition_data)
+
+def analyze_coloring_books(region):
+    analyzer = ColoringBookAnalyzer()
+    
+    # Get trends
+    trends_df = analyzer.analyze_trends(geo=region)
+    
+    # Create visualization
+    trends_plot = analyzer.create_trends_plot(trends_df)
+    
+    # Get competition analysis
+    competition_df = analyzer.get_competition_analysis(trends_df)
+    
+    # Format results
+    top_keywords = trends_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
+    
+    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
+    """
+    
+    return trends_plot, top_keywords_str, competition_str, recommendations
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=analyze_coloring_books,
+    inputs=[
+        gr.Dropdown(
+            choices=['US', 'UK', 'CA', 'AU', 'DE', 'FR', 'ES', 'IT'],
+            label="Select Region",
+            default="US"
+        )
+    ],
+    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",
+    theme="default"
+)
+
+# For local testing
+if __name__ == "__main__":
+    iface.launch()
+