Update app.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import plotly.express as px
+from wordcloud import WordCloud, STOPWORDS
+import matplotlib.pyplot as plt
+import folium
+import plotly.express as px
+import seaborn as sns
+import json
+import os
+from streamlit_folium import folium_static 
+
+st.set_option('deprecation.showPyplotGlobalUse', False)
+
+DATA_ = pd.read_csv("states.csv")
+st.title("Sentiment Analysis of Tweets")
+st.sidebar.title("Sentiment Analysis of Tweets")
+st.markdown("This application is a streamlit dashboard to analyze the sentiment of Tweets")
+st.sidebar.markdown("This application is a streamlit dashboard to analyze the sentiment of Tweets")
+
+
+def run():
+    
+    @st.cache(persist=True)
+    def load_data():
+        DATA_['tweet_created'] = pd.to_datetime(DATA_['Datetime'])
+        return DATA_
+    data = load_data()
+    
+    st.sidebar.subheader("Show random tweet")
+    random_tweet = st.sidebar.radio('Sentiment', ('Positive', 'Neutral', 'Negative'))
+    st.sidebar.markdown(data.query('sentiment_flair == @random_tweet')[["Text"]].sample(n=1).iat[0,0])
+    
+    st.sidebar.markdown("### Number of tweets by sentiment")
+    select = st.sidebar.selectbox('Visualization type', ['Histogram', 'Pie chart'])
+    sentiment_count = data['sentiment_flair'].value_counts()
+    sentiment_count = pd.DataFrame({'Sentiment':sentiment_count.index, 'Tweets':sentiment_count.values})
+    
+    if not st.sidebar.checkbox("Hide", True):
+        st.markdown("### Number of tweets by sentiment")
+        if select == "Histogram":
+            fig = px.bar(sentiment_count, x='Sentiment', y='Tweets', color='Tweets', height=500)
+            st.plotly_chart(fig)
+        else:
+            fig = px.pie(sentiment_count, values='Tweets', names='Sentiment')
+            st.plotly_chart(fig)
+            
+    
+    st.sidebar.subheader("When and Where are users tweeting from?")
+    hour = st.sidebar.slider("Hour of day", 0,23)
+    modified_data = data[data['tweet_created'].dt.hour == hour]
+    if not st.sidebar.checkbox("Close", True, key='1'):
+        st.markdown("### Tweets locations based on the time of date")
+        st.markdown("%i tweets between %i:00 and %i:00" % (len(modified_data), hour, (hour+1)%24))
+        st.map(modified_data)
+        if st.sidebar.checkbox("Show Raw Data", False):
+            st.write(modified_data)
+    st.sidebar.subheader("Breakdown language tweets by sentiment")
+    choice = st.sidebar.multiselect('Pick language', ('en', 'hi'), key='0')
+
+    if len(choice) > 0:
+        choice_data = data[data.language.isin(choice)]
+        fig_choice = px.histogram(choice_data, x='language',
+        y='sentiment_flair',
+        histfunc = 'count', color = 'sentiment_flair',
+        facet_col='sentiment_flair',
+        labels={'sentiment_flair':'tweets'}, height=600, width=800)
+        st.plotly_chart(fig_choice)
+
+    st.sidebar.header("Word Cloud")
+    word_sentiment = st.sidebar.radio('Display word cloud for what sentiment?',('Positive', 'Neutral','Negative'))
+
+    if not st.sidebar.checkbox("Close", True, key='3'):
+        st.header('Word cloud for %s sentiment' % (word_sentiment))
+        df = data[data['sentiment_flair']==word_sentiment]
+        words = ' '.join(df['Text'])
+        processed_words = ' '.join([word for word in words.split() if 'http' not in word and not word.startswith('@') and word !='RT'])
+        wordcloud = WordCloud(stopwords=STOPWORDS,
+        background_color='white', height=640, width=800).generate(processed_words)
+        plt.imshow(wordcloud)
+        plt.xticks([])
+        plt.yticks([])
+        st.pyplot() 
+            
+  ####################################        choropleth map       #############################################################     
+    with open('india_state.json') as file:
+      geojsonData = json.load(file)
+        
+    for i in geojsonData['features']:
+       i['id'] = i['properties']['NAME_1']
+    
+    map_choropleth_high_public = folium.Map(location = [20.5937,78.9629], zoom_start = 4)
+    df1 = data
+    df1 = df1[df1['location'].notna()]
+    
+    def get_state(x):
+    
+        states = ["Andaman and Nicobar Islands","Andhra Pradesh","Arunachal Pradesh","Assam","Bihar","Chandigarh","Chhattisgarh",
+              "Dadra and Nagar Haveli","Daman and Diu","Delhi","Goa","Gujarat","Haryana","Himachal Pradesh","Jammu and Kashmir",
+              "Jharkhand","Karnataka","Kerala","Ladakh","Lakshadweep","Madhya Pradesh","Maharashtra","Manipur","Meghalaya",
+              "Mizoram","Nagaland","Odisha","Puducherry","Punjab","Rajasthan","Sikkim","Tamil Nadu","Telangana","Tripura","Uttar Pradesh","Uttarakhand","West Bengal"]
+
+        states_dict = {"Delhi":"New Delhi","Gujarat":"Surat","Haryana":"Gurgaon", "Karnataka":"Bangalore", "Karnataka":"Bengaluru",  "Maharashtra":"Pune","Maharashtra":"Mumbai","Maharashtra":"Navi Mumbai","Telangana":"Hyderabad","West Bengal":"Kolkata",
+                   "Gujarat":"Surat","Rajasthan":"Kota","Rajasthan":"Jodhpur","Karnataka":"Bengaluru South","Uttar Pradesh":"Lukhnow","Uttar Pradesh":"Noida","Bihar":"Patna","Uttarakhand":"Dehradun","Madhya Pradesh":"Indore" , "Madhya Pradesh":"Bhopal",
+                    "Andaman and Nicobar Islands":"Andaman and Nicobar Islands", "Andhra Pradesh":"Andhra Pradesh","Arunachal Pradesh":"Arunachal Pradesh","Assam":"Assam","Bihar":"Bihar",
+                     "Chandigarh":"Chandigarh","Chhattisgarh":"Chhattisgarh", "Dadra and Nagar Haveli": "Dadra and Nagar Haveli","Daman and Diu":"Daman and Diu","Delhi":"Delhi",
+                      "Goa":"Goa","Gujarat":"Gujarat","Haryana":"Haryana","Himachal Pradesh":"Himachal Pradesh","Jammu and Kashmir":"Jammu and Kashmir", "Jharkhand": "Jharkhand",
+                      "Karnataka":"Karnataka","Kerala":"Kerala","Ladakh":"Ladakh","Lakshadweep":"Lakshadweep","Madhya Pradesh":"Madhya Pradesh","Maharashtra":"Maharashtra",
+                      "Odisha":"Odisha","Puducherry":"Puducherry","Punjab":"Punjab","Rajasthan":"Rajasthan","Tamil Nadu":"Tamil Nadu","Telangana":"Telangana","Uttar Pradesh":"Uttar Pradesh",
+                       "Uttarakhand":"Uttarakhand","West Bengal":"West Bengal","West Bengal":"Calcutta","Uttar Pradesh":"Lucknow"
+                   }
+
+        abv = x.split(',')[-1].lstrip()
+        state_name = x.split(',')[0].lstrip()
+   
+        if abv in states:
+          state = abv
+        else:
+         if state_name in states_dict.values():
+            state = list(states_dict.keys())[list(states_dict.values()).index(state_name)]
+        
+         else:
+            state = 'Non_India'    
+    
+        return state
+
+    # create abreviated states column
+    df2 = df1.copy()
+
+    df2['states'] = df1['location'].apply(get_state)
+   
+   # extract total sentiment per state
+    df_state_sentiment = df2.groupby(['states'])['Label'].value_counts().unstack().fillna(0.0).reset_index()
+    df_state_sentiment['total_sentiment'] = -(df_state_sentiment[0])+df_state_sentiment[1] 
+    dff = df_state_sentiment[df_state_sentiment['states'] != 'Non_India']
+    
+    folium.Choropleth(geo_data=geojsonData,
+                 data=dff,
+                 name='CHOROPLETH',
+                 key_on='feature.id',
+                 columns = ['states','total_sentiment'],
+                 fill_color='YlOrRd',
+                 fill_opacity=0.7,
+                 line_opacity=0.4,
+                 legend_name='Sentiments',
+                 highlight=True).add_to(map_choropleth_high_public)
+
+    folium.LayerControl().add_to(map_choropleth_high_public)
+       
+  #display(map_choropleth_high_public)
+    
+    st.sidebar.header("Map Visualisation")   
+    if not st.sidebar.checkbox("Close", True, key='4'):   
+       folium_static(map_choropleth_high_public)
+        
+            
+if __name__ == '__main__':
+    run()