Update app.py

app.py

@@ -2,27 +2,69 @@ import streamlit as st
 from pytrends.request import TrendReq
 import pandas as pd
 import matplotlib.pyplot as plt
+from pytrendsexpander import pytrendsExpander
 
 # Set up the pytrends API
 pytrends = TrendReq(hl='en-US', tz=360)
 
 st.title('Google Trends Analyzer')
 
-# Input for keyword
-keyword = st.text_input('Enter a keyword to analyze:', 'Streamlit')
+# Input for multiple keywords
+keywords = st.text_input('Enter keywords to analyze (comma-separated):', 'Streamlit, Data Science')
 
-if keyword:
+# Geographical focus
+geo = st.text_input('Enter geographical region (e.g., US, FR, TH):', '')
+
+# Time range selection
+timeframe = st.selectbox('Select Time Range:', 
+                         ['today 12-m', 'today 3-m', 'today 1-m', 'today 5-y', 'all'])
+
+# Category selection
+category = st.text_input('Enter category number (default is 0):', '0')
+
+# Data Source selection
+data_source = st.selectbox('Select Data Source:', 
+                           ['Web Search', 'Image Search', 'News Search', 'Google Shopping', 'YouTube Search'])
+
+if keywords:
+    # Prepare the keywords list
+    kw_list = [kw.strip() for kw in keywords.split(',')]
+    
+    # Convert data source to pytrends parameter
+    gprop_map = {
+        'Web Search': '',
+        'Image Search': 'images',
+        'News Search': 'news',
+        'Google Shopping': 'froogle',
+        'YouTube Search': 'youtube'
+    }
+    gprop = gprop_map[data_source]
+    
     # Fetch data from Google Trends
-    pytrends.build_payload([keyword], cat=0, timeframe='today 12-m', geo='', gprop='')
+    pytrends.build_payload(kw_list, cat=int(category), 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[keyword])
+        st.line_chart(interest_over_time_df[kw_list])
 
         # Display the dataframe
         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))
+        
+        # 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)
     else:
-        st.write('No data found for this keyword.')
+        st.write('No data found for these keywords.')
 
-st.write('This is a simple Google Trends analysis app.')
+st.write('This is an enhanced Google Trends analysis app.')