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Google trends data and keyword research

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ajaysi
2025-04-05 22:50:43 +05:30
parent a2fb77f700
commit 579bf7d0a6
5 changed files with 1122 additions and 379 deletions
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425
lib/alwrity_ui/google_trends_ui.py Normal file
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"""
Module for displaying Google Trends data in the Streamlit UI.
This module provides functions for visualizing Google Trends data, including:
- Interest over time
- Regional interest
- Related queries
- Related topics
"""
import streamlit as st
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import logging
# Set up logging
logger = logging.getLogger(__name__)
def display_google_trends_data(trends_data, search_keyword):
"""
Display Google Trends data in a structured format with tabs for different sections.
Args:
trends_data (dict): Dictionary containing Google Trends data
search_keyword (str): The search keyword used for the analysis
"""
if not trends_data:
st.warning("No Google Trends data available for this search.")
return
st.subheader(f"Google Trends Analysis for '{search_keyword}'")
# Add an informative message about Google Trends
with st.expander("ℹ️ About Google Trends Data", expanded=False):
st.markdown("""
**What is Google Trends?**
Google Trends is a public web facility that shows how often a particular search-term is entered relative to the total search-volume across various regions of the world, and in various languages.
**What data is shown here?**
- **Related Keywords**: Terms that are frequently searched together with your keyword
- **Interest Over Time**: How interest in your keyword has changed over the past 12 months
- **Regional Interest**: Where in the world your keyword is most popular
- **Related Queries**: What people search for before and after searching for your keyword
- **Related Topics**: Topics that are closely related to your keyword
**How to interpret the data:**
- Interest values range from 0 to 100, where 100 is the peak popularity for the term
- A value of 50 means the term is half as popular as the peak
- A value of 0 means there was not enough data for this term
""")
# Create tabs for different sections
tab1, tab2, tab3, tab4, tab5 = st.tabs([
"Related Keywords",
"Interest Over Time",
"Regional Interest",
"Related Queries",
"Related Topics"
])
with tab1:
display_keywords_section(trends_data.get('related_keywords', []))
with tab2:
display_interest_over_time(trends_data.get('interest_over_time', pd.DataFrame()))
with tab3:
display_regional_interest(trends_data.get('regional_interest', pd.DataFrame()))
with tab4:
display_related_queries(trends_data.get('related_queries', pd.DataFrame()))
with tab5:
display_related_topics(trends_data.get('related_topics', pd.DataFrame()))
# Add a footer with data source information
st.markdown("---")
st.caption("Data source: Google Trends | Last updated: " + pd.Timestamp.now().strftime("%Y-%m-%d %H:%M:%S"))
def display_keywords_section(keywords):
"""Display related keywords from Google Trends in a table format."""
if not keywords:
st.info("No related keywords data available.")
return
st.subheader("Related Keywords")
st.write("Keywords related to your search:")
# Add explanation about related keywords
with st.expander("ℹ️ About Related Keywords", expanded=False):
st.markdown("""
**What are Related Keywords?**
Related keywords are terms that are frequently searched together with your main keyword.
These keywords can help you understand what topics are associated with your search term
and can be valuable for content planning and SEO strategies.
**How to use this data:**
- Use these keywords to expand your content strategy
- Identify gaps in your content that you could fill
- Understand what your audience is interested in
- Improve your SEO by incorporating these terms naturally in your content
""")
# Create a DataFrame for better display
df = pd.DataFrame(keywords, columns=['Keyword'])
st.dataframe(df, use_container_width=True)
# Add a note about the number of keywords
st.caption(f"Found {len(keywords)} related keywords")
def display_interest_over_time(interest_df):
"""Display a chart showing interest over time for a given search keyword."""
if interest_df.empty:
st.info("No interest over time data available.")
return
st.subheader("Interest Over Time")
# Add explanation about interest over time
with st.expander("ℹ️ About Interest Over Time", expanded=False):
st.markdown("""
**What is Interest Over Time?**
Interest Over Time shows how interest in your search term has changed over the past 12 months.
The data is normalized and presented on a scale from 0 to 100, where 100 is the peak popularity
for the term, 50 means the term is half as popular, and 0 means there was not enough data.
**How to interpret this chart:**
- Look for peaks and valleys to identify trends
- Compare with seasonal patterns or events
- Identify if interest is growing, declining, or stable
- Use this data to time your content releases for maximum impact
""")
try:
# Ensure we have the required columns
if 'date' not in interest_df.columns:
st.error("Interest over time data is missing the 'date' column.")
return
if 'interest' not in interest_df.columns:
st.error("Interest over time data is missing the 'interest' column.")
return
# Create the chart
fig = px.line(
interest_df,
x='date',
y='interest',
title='Interest Over Time',
labels={'date': 'Date', 'interest': 'Interest'},
line_shape='spline'
)
fig.update_layout(
xaxis_title="Date",
yaxis_title="Interest",
hovermode='x unified'
)
st.plotly_chart(fig, use_container_width=True)
# Add summary statistics
if not interest_df.empty:
col1, col2, col3 = st.columns(3)
with col1:
st.metric("Average Interest", f"{interest_df['interest'].mean():.1f}")
with col2:
st.metric("Peak Interest", f"{interest_df['interest'].max():.1f}")
with col3:
st.metric("Lowest Interest", f"{interest_df['interest'].min():.1f}")
except Exception as e:
st.error(f"Error displaying interest over time chart: {str(e)}")
logger.error(f"Error in display_interest_over_time: {e}")
def display_regional_interest(regional_df):
"""Display a chart showing interest by region for the search keyword."""
if regional_df.empty:
st.info("No regional interest data available.")
return
st.subheader("Regional Interest")
# Add explanation about regional interest
with st.expander("ℹ️ About Regional Interest", expanded=False):
st.markdown("""
**What is Regional Interest?**
Regional Interest shows how interest in your search term varies across different countries.
The data is normalized and presented on a scale from 0 to 100, where 100 is the peak popularity
for the term in that region, 50 means the term is half as popular, and 0 means there was not enough data.
**How to interpret this map:**
- Darker colors indicate higher interest in that region
- Lighter colors indicate lower interest
- Hover over a country to see the exact interest value
- Use this data to target your content to specific regions
""")
try:
# Ensure we have the required columns
if 'country_code' not in regional_df.columns:
st.error("Regional interest data is missing the 'country_code' column.")
return
if 'interest' not in regional_df.columns:
st.error("Regional interest data is missing the 'interest' column.")
return
# Create the choropleth map
fig = go.Figure(data=go.Choropleth(
locations=regional_df['country_code'],
z=regional_df['interest'],
text=regional_df['country_code'], # This will show in the hover text
colorscale='Viridis',
colorbar_title="Interest Level",
zmin=0,
zmax=100,
marker_line_color='darkgray',
marker_line_width=0.5,
showscale=True,
colorbar=dict(
title="Interest Level",
tickformat=".0f",
tickmode="linear",
tick0=0,
dtick=20
)
))
# Update the layout for better visualization
fig.update_layout(
title=dict(
text='Regional Interest Distribution',
x=0.5,
xanchor='center'
),
geo=dict(
showframe=False,
showcoastlines=True,
projection_type='equirectangular',
showland=True,
landcolor='lightgray',
showocean=True,
oceancolor='aliceblue',
showcountries=True,
countrycolor='darkgray'
),
width=800,
height=500,
margin=dict(l=0, r=0, t=30, b=0)
)
# Display the map
st.plotly_chart(fig, use_container_width=True)
# Display top 5 countries with highest interest
if not regional_df.empty:
st.subheader("Top Regions by Interest")
top_regions = regional_df.sort_values('interest', ascending=False).head(5)
# Create a more visually appealing bar chart for top regions
fig_bar = go.Figure(data=[
go.Bar(
x=top_regions['country_code'],
y=top_regions['interest'],
text=top_regions['interest'].round(1),
textposition='auto',
marker_color='rgb(55, 83, 109)'
)
])
fig_bar.update_layout(
title='Top 5 Regions by Interest Level',
xaxis_title='Region',
yaxis_title='Interest Level',
yaxis_range=[0, 100],
showlegend=False
)
st.plotly_chart(fig_bar, use_container_width=True)
except Exception as e:
st.error(f"Error displaying regional interest chart: {str(e)}")
logger.error(f"Error in display_regional_interest: {e}")
def display_related_queries(queries_df):
"""Display related queries in a structured format."""
if queries_df.empty:
st.info("No related queries data available.")
return
st.subheader("Related Queries")
# Add explanation about related queries
with st.expander("ℹ️ About Related Queries", expanded=False):
st.markdown("""
**What are Related Queries?**
Related Queries show what people search for before and after searching for your keyword.
These queries can help you understand the search intent and context around your keyword.
**How to interpret this data:**
- The 'value' column shows the relative interest compared to your main keyword
- Higher values indicate stronger association with your keyword
- Use these queries to expand your content strategy
- Identify what questions your audience is trying to answer
""")
try:
# Ensure we have the required columns
if 'query' not in queries_df.columns:
st.error("Related queries data is missing the 'query' column.")
return
if 'value' not in queries_df.columns:
st.error("Related queries data is missing the 'value' column.")
return
# Sort by value in descending order
queries_df = queries_df.sort_values('value', ascending=False)
# Display as a table
st.dataframe(queries_df, use_container_width=True)
# Add a note about the number of queries
st.caption(f"Found {len(queries_df)} related queries")
except Exception as e:
st.error(f"Error displaying related queries: {str(e)}")
logger.error(f"Error in display_related_queries: {e}")
def display_related_topics(topics_df):
"""Display related topics in a structured format."""
if topics_df.empty:
st.info("No related topics data available.")
return
st.subheader("Related Topics")
# Add explanation about related topics
with st.expander("ℹ️ About Related Topics", expanded=False):
st.markdown("""
**What are Related Topics?**
Related Topics show broader topics that are associated with your search term.
These topics can help you understand the broader context and themes related to your keyword.
**How to interpret this data:**
- The 'value' column shows the relative interest compared to your main keyword
- Higher values indicate stronger association with your keyword
- Use these topics to understand the broader context of your keyword
- Identify themes that might be relevant to your content strategy
""")
try:
# Ensure we have the required columns
if 'topic' not in topics_df.columns:
st.error("Related topics data is missing the 'topic' column.")
return
if 'value' not in topics_df.columns:
st.error("Related topics data is missing the 'value' column.")
return
# Sort by value in descending order
topics_df = topics_df.sort_values('value', ascending=False)
# Display as a table
st.dataframe(topics_df, use_container_width=True)
# Add a note about the number of topics
st.caption(f"Found {len(topics_df)} related topics")
except Exception as e:
st.error(f"Error displaying related topics: {str(e)}")
logger.error(f"Error in display_related_topics: {e}")
def process_trends_data(trends_data):
"""
Process and format Google Trends data for display.
Args:
trends_data (dict): Raw Google Trends data
Returns:
dict: Formatted data ready for display
"""
if not trends_data:
return {}
processed_data = {}
# Process related keywords
if 'related_keywords' in trends_data:
processed_data['related_keywords'] = trends_data['related_keywords']
# Process interest over time
if 'interest_over_time' in trends_data and not trends_data['interest_over_time'].empty:
processed_data['interest_over_time'] = trends_data['interest_over_time']
# Process regional interest
if 'regional_interest' in trends_data and not trends_data['regional_interest'].empty:
processed_data['regional_interest'] = trends_data['regional_interest']
# Process related queries
if 'related_queries' in trends_data and not trends_data['related_queries'].empty:
processed_data['related_queries'] = trends_data['related_queries']
# Process related topics
if 'related_topics' in trends_data and not trends_data['related_topics'].empty:
processed_data['related_topics'] = trends_data['related_topics']
return processed_data

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lib/alwrity_ui/keyword_web_researcher.py
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@@ -102,7 +102,12 @@ def validate_api_keys():
return api_keys
def do_web_research():
"""Input keywords and do web research with advanced options."""
"""Main function to perform web research based on user input."""
# Reset session state variables for this research operation
if 'metaphor_results_displayed' in st.session_state:
del st.session_state.metaphor_results_displayed
logger.info("Starting do_web_research function")
try:
@@ -509,7 +514,7 @@ def do_web_research():
status_display.success("✨ Research completed!")
# Display results in an organized way
with st.expander("📊 Research Results", expanded=True):
with st.expander("📊 Research Results", expanded=False):
st.write(web_research_result)
else:
st.warning("No results found for your search")