Update app.py

app.py

@@ -2,6 +2,9 @@ import streamlit as st
 from pytrends.request import TrendReq
 import pandas as pd
 import matplotlib.pyplot as plt
+import seaborn as sns
+import plotly.express as px
+from wordcloud import WordCloud
 
 class pytrendsExpander:
     def __init__(self, keyword):
@@ -11,82 +14,108 @@ class pytrendsExpander:
 
     def expand_search(self, m):
         pytrends = self.pytrends
-
-        # Get seed keyword
         kw_list = [self.keyword]
         pytrends.build_payload(kw_list, cat=0, timeframe='today 1-m', geo='US', gprop='')
-
-        # Get results from the seed keyword
         queries = pytrends.related_queries()
-
-        # Store the results in the data frame
         df_results = pd.DataFrame(queries.get(self.keyword).get('top').rename_axis('ranking').reset_index())
-
         return df_results.head(m)
 
-# Set up the pytrends API
 pytrends = TrendReq(hl='en-US', tz=360)
 
-st.title('Google Trends Analyzer')
+st.set_page_config(layout="wide")
+st.title('Advanced Google Trends Analyzer')
 
-# Sidebar for options
 with st.sidebar:
     st.header('Analysis Options')
-
-    # Input for multiple keywords as separate text boxes
-    keyword1 = st.text_input('Enter first keyword to analyze:', 'Streamlit')
-    keyword2 = st.text_input('Enter second keyword to analyze (optional):', '')
-    keyword3 = st.text_input('Enter third keyword to analyze (optional):', '')
-
-    # Geographical focus dropdown
+    keywords = st.text_area('Enter keywords to analyze (one per line):', 'Streamlit')
+    kw_list = [kw.strip() for kw in keywords.split('\n') if kw.strip()]
+    
     countries = ['US', 'FR', 'TH', 'DE', 'IN', 'JP', 'BR', 'GB', 'CA', 'AU']
     geo = st.selectbox('Select geographical region:', countries)
-
-    # Time range selection
-    timeframes = ['now 1-H', 'now 5-H', 'now 1-d', 'now 7-d', 'today 1-m', 'today 3-m']
+    
+    timeframes = ['now 1-H', 'now 4-H', 'now 1-d', 'now 7-d', 'today 1-m', 'today 3-m', 'today 12-m', 'today 5-y']
     timeframe = st.selectbox('Select Time Range:', timeframes)
-
-    # Data Source selection limited to specific options
-    data_source = st.selectbox('Select Data Source:', 
-                               ['Web Search', 'Image Search', 'YouTube Search'])
-
-if keyword1 or keyword2 or keyword3:
-    # Prepare the keywords list
-    kw_list = [kw.strip() for kw in [keyword1, keyword2, keyword3] if kw]
-
-    # Convert data source to pytrends parameter
+    
+    data_sources = ['Web Search', 'Image Search', 'YouTube Search', 'News Search', 'Google Shopping']
+    data_source = st.selectbox('Select Data Source:', data_sources)
+    
+    chart_type = st.selectbox('Select Chart Type:', ['Line', 'Area', 'Bar'])
+    
+    advanced_options = st.expander('Advanced Options')
+    with advanced_options:
+        normalize = st.checkbox('Normalize Data', value=True)
+        moving_average = st.checkbox('Apply Moving Average')
+        if moving_average:
+            ma_window = st.slider('Moving Average Window', 1, 30, 7)
+
+if kw_list:
     gprop_map = {
-        'Web Search': '',
-        'Image Search': 'images',
-        'YouTube Search': 'youtube'
+        'Web Search': '', 'Image Search': 'images', 'YouTube Search': 'youtube',
+        'News Search': 'news', 'Google Shopping': 'froogle'
     }
     gprop = gprop_map[data_source]
     
-    # Fetch data from Google Trends
     pytrends.build_payload(kw_list, cat=0, timeframe=timeframe, geo=geo, gprop=gprop)
     interest_over_time_df = pytrends.interest_over_time()
-
+    
     if not interest_over_time_df.empty:
-        # Display data as a line chart
-        st.line_chart(interest_over_time_df[kw_list])
-
-        # Display the dataframe
+        st.subheader('Interest Over Time')
+        
+        if moving_average:
+            for col in kw_list:
+                interest_over_time_df[f'{col}_MA'] = interest_over_time_df[col].rolling(window=ma_window).mean()
+            plot_columns = [f'{col}_MA' for col in kw_list]
+        else:
+            plot_columns = kw_list
+        
+        if chart_type == 'Line':
+            fig = px.line(interest_over_time_df, x=interest_over_time_df.index, y=plot_columns)
+        elif chart_type == 'Area':
+            fig = px.area(interest_over_time_df, x=interest_over_time_df.index, y=plot_columns)
+        else:  # Bar
+            fig = px.bar(interest_over_time_df, x=interest_over_time_df.index, y=plot_columns)
+        
+        st.plotly_chart(fig, use_container_width=True)
+        
+        st.subheader('Data Table')
         st.write(interest_over_time_df)
-
-        # Download option for the dataframe
+        
         csv = interest_over_time_df.to_csv().encode('utf-8')
         st.download_button(label="Download data as CSV", data=csv, file_name='google_trends_data.csv', mime='text/csv')
         
-        # Embedding the chart
-        st.write('Share or embed the chart by copying the URL.')
-        st.write(st.text_area("URL for embedding", value=st.get_url(), height=100))
+        st.subheader('Correlation Heatmap')
+        corr = interest_over_time_df[kw_list].corr()
+        fig, ax = plt.subplots(figsize=(10, 8))
+        sns.heatmap(corr, annot=True, cmap='coolwarm', ax=ax)
+        st.pyplot(fig)
         
-        # Use pytrendsExpander for network analysis
         st.subheader('Related Queries and Topics')
-        expander = pytrendsExpander(kw_list[0])
-        related_df = expander.expand_search(5)
-        st.write(related_df)
+        for keyword in kw_list:
+            st.write(f"Related to '{keyword}':")
+            expander = pytrendsExpander(keyword)
+            related_df = expander.expand_search(10)
+            st.write(related_df)
+            
+            # Generate and display word cloud
+            wordcloud = WordCloud(width=800, height=400, background_color='white').generate_from_frequencies(
+                dict(zip(related_df['query'], related_df['value']))
+            )
+            fig, ax = plt.subplots(figsize=(10, 5))
+            ax.imshow(wordcloud, interpolation='bilinear')
+            ax.axis('off')
+            st.pyplot(fig)
+        
+        st.subheader('Geographical Interest')
+        interest_by_region = pytrends.interest_by_region(resolution='COUNTRY', inc_low_vol=True, inc_geo_code=True)
+        fig = px.choropleth(interest_by_region, locations=interest_by_region.index,
+                            color=kw_list[0], # You can allow users to select which keyword to display
+                            hover_name=interest_by_region.index,
+                            color_continuous_scale=px.colors.sequential.Plasma)
+        st.plotly_chart(fig, use_container_width=True)
+        
     else:
         st.write('No data found for these keywords.')
+else:
+    st.write('Please enter at least one keyword to analyze.')
 
-st.write('This is an enhanced Google Trends analysis app.')
+st.write('This is an advanced Google Trends analysis app.')