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

# Initialize pytrends
pytrends = TrendReq(hl='en-US', tz=360)

st.set_page_config(layout="wide")
st.title('Advanced Google Trends Analyzer')

with st.sidebar:
    st.header('Analysis Options')
    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)
    
    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_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',
        'News Search': 'news', 'Google Shopping': 'froogle'
    }
    gprop = gprop_map[data_source]
    
    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:
        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)
        
        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')
        
        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)
        
        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 advanced Google Trends analysis app.')