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import streamlit as st |
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from pytrends.request import TrendReq |
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import pandas as pd |
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import matplotlib.pyplot as plt |
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import seaborn as sns |
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import plotly.express as px |
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from wordcloud import WordCloud |
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pytrends = TrendReq(hl='en-US', tz=360) |
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st.set_page_config(layout="wide") |
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st.title('Advanced Google Trends Analyzer') |
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with st.sidebar: |
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st.header('Analysis Options') |
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keywords = st.text_area('Enter keywords to analyze (one per line):', 'Streamlit') |
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kw_list = [kw.strip() for kw in keywords.split('\n') if kw.strip()] |
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countries = ['US', 'FR', 'TH', 'DE', 'IN', 'JP', 'BR', 'GB', 'CA', 'AU'] |
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geo = st.selectbox('Select geographical region:', countries) |
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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'] |
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timeframe = st.selectbox('Select Time Range:', timeframes) |
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data_sources = ['Web Search', 'Image Search', 'YouTube Search', 'News Search', 'Google Shopping'] |
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data_source = st.selectbox('Select Data Source:', data_sources) |
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chart_type = st.selectbox('Select Chart Type:', ['Line', 'Area', 'Bar']) |
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advanced_options = st.expander('Advanced Options') |
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with advanced_options: |
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normalize = st.checkbox('Normalize Data', value=True) |
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moving_average = st.checkbox('Apply Moving Average') |
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if moving_average: |
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ma_window = st.slider('Moving Average Window', 1, 30, 7) |
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if kw_list: |
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gprop_map = { |
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'Web Search': '', 'Image Search': 'images', 'YouTube Search': 'youtube', |
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'News Search': 'news', 'Google Shopping': 'froogle' |
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} |
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gprop = gprop_map[data_source] |
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pytrends.build_payload(kw_list, cat=0, timeframe=timeframe, geo=geo, gprop=gprop) |
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interest_over_time_df = pytrends.interest_over_time() |
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if not interest_over_time_df.empty: |
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st.subheader('Interest Over Time') |
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if moving_average: |
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for col in kw_list: |
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interest_over_time_df[f'{col}_MA'] = interest_over_time_df[col].rolling(window=ma_window).mean() |
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plot_columns = [f'{col}_MA' for col in kw_list] |
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else: |
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plot_columns = kw_list |
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if chart_type == 'Line': |
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fig = px.line(interest_over_time_df, x=interest_over_time_df.index, y=plot_columns) |
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elif chart_type == 'Area': |
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fig = px.area(interest_over_time_df, x=interest_over_time_df.index, y=plot_columns) |
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else: |
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fig = px.bar(interest_over_time_df, x=interest_over_time_df.index, y=plot_columns) |
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st.plotly_chart(fig, use_container_width=True) |
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csv = interest_over_time_df.to_csv().encode('utf-8') |
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st.download_button(label="Download data as CSV", data=csv, file_name='google_trends_data.csv', mime='text/csv') |
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st.subheader('Correlation Heatmap') |
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corr = interest_over_time_df[kw_list].corr() |
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fig, ax = plt.subplots(figsize=(10, 8)) |
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sns.heatmap(corr, annot=True, cmap='coolwarm', ax=ax) |
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st.pyplot(fig) |
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st.subheader('Geographical Interest') |
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interest_by_region = pytrends.interest_by_region(resolution='COUNTRY', inc_low_vol=True, inc_geo_code=True) |
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fig = px.choropleth(interest_by_region, locations=interest_by_region.index, |
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color=kw_list[0], |
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hover_name=interest_by_region.index, |
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color_continuous_scale=px.colors.sequential.Plasma) |
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st.plotly_chart(fig, use_container_width=True) |
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else: |
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st.write('No data found for these keywords.') |
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else: |
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st.write('Please enter at least one keyword to analyze.') |
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st.write('This is an advanced Google Trends analysis app.') |
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