PolarWeb/temp.py at main · Uplyuz/PolarWeb · GitHub

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# Tab 4: Heads Up - Keyword Comparison
with tab4:
    st.markdown("<h3 style='text-align: center;'>Compare Sentiment for Two Keywords</h3>", unsafe_allow_html=True)

    col1, col2 = st.columns(2)

    with col1:
        keyword1 = st.text_input("Enter first keyword to search tweets:", "Keyword 1")
    with col2:
        keyword2 = st.text_input("Enter second keyword to search tweets:", "Keyword 2")

    tweet_limit = 20

    if st.button("Compare"):
        # Check if the keywords are valid
        if not keyword1.strip() or not keyword2.strip():
            st.warning("Please enter both keywords before comparing.")
        else:
            # Prepare query strings for each keyword
            querystring1 = {"query": keyword1, "section": "latest", "limit": (tweet_limit)}
            querystring2 = {"query": keyword2, "section": "latest", "limit": (tweet_limit)}

            try:
                # Fetch tweets for each keyword using pagination function
                tweets1 = fetch_tweets_with_pagination(url_tweets_search_api_01, querystring1, headers)
                tweets2 = fetch_tweets_with_pagination(url_tweets_search_api_01, querystring2, headers)

                # Convert fetched tweets to DataFrames
                search1 = pd.DataFrame(tweets1, columns=['Date', 'Tweet', 'Tweet_Likes'])
                search2 = pd.DataFrame(tweets2, columns=['Date', 'Tweet', 'Tweet_Likes'])

                # Preprocess and translate tweets for each search
                search1 = preprocess_tweet(search1)
                search1['Tweet'] = search1['Tweet'].apply(traducir)
                search1 = search1.dropna(subset=['Tweet'])
                search1 = search1[search1['Tweet'].str.strip().astype(bool)]

                search2 = preprocess_tweet(search2)
                search2['Tweet'] = search2['Tweet'].apply(traducir)
                search2 = search2.dropna(subset=['Tweet'])
                search2 = search2[search2['Tweet'].str.strip().astype(bool)]

                # Apply sentiment analysis using the model on both search results
                search1 = analyze_sentiments(model_custom, tokenizer_custom, search1)
                search2 = analyze_sentiments(model_custom, tokenizer_custom, search2)

                st.success("Sentiment analysis completed for both keywords.")

                # Display sentiment distribution for the first keyword
                st.markdown(f"### Sentiment Distribution for **{keyword1}**")
                sentiment_dist_plotly(search1)  # Plot distribution for the first keyword

                st.markdown(f"### Sentiment Distribution for **{keyword2}**")
                sentiment_dist_plotly(search2)  # Plot distribution for the second keyword

            except Exception as e:
                st.error(f"An error occurred during the comparison: {e}")


#graph to add: bar charts with comparison bars