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| import streamlit as st | |
| import streamlit.components.v1 as components | |
| import os # Import the os module | |
| import pandas as pd | |
| from sidebar_logo import add_sidebar_logo, load_css | |
| # Set page configuration | |
| st.set_page_config( | |
| layout="wide" | |
| ) | |
| add_sidebar_logo() | |
| load_css() | |
| st.markdown(""" | |
| <div style=" | |
| font-size: 1.9rem; | |
| font-weight: 800; | |
| background: linear-gradient(135deg, #a78bfa, #818cf8, #f472b6); | |
| -webkit-background-clip: text; | |
| -webkit-text-fill-color: transparent; | |
| font-family: 'Poppins', sans-serif; | |
| "> | |
| 📓 5. Sentiment Analysis | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # Define the file path with regular spaces | |
| path_to_html = "Sentiment_analysis.html" | |
| # Check if the HTML file exists | |
| if not os.path.exists(path_to_html): | |
| st.error("HTML file not found!") | |
| else: | |
| # Read HTML content | |
| with open(path_to_html, 'r', encoding='utf-8') as f: | |
| html_data = f.read() | |
| # Show HTML content | |
| st.header(":violet[NLP - Sentiment Analysis]") | |
| st.markdown("""Hi guys. Now that you know how TFIDF (Term Frequency Inverse Document Frequency) algorithm works, it is about time for you to | |
| understand how to perform 'sentiment analysis'. For those who are guessing, yes it is exactly as it sounds. In Sentiment analysis, | |
| we are trying to interpret the mood or the sentiment of a sentence. The most common usecase of sentiment anaylsis would be product | |
| review portals on the websites where consumers post their feedbacks. These feedbacks are generally high in volume and hence to process | |
| and interpret each one of them backend developers generally use 'sentiment analysis'. The core working principle relies heavily on Tfidf | |
| and Logistic Regression in pipleine. Let us start working straight away to understand the concept much better. Don't forget to | |
| download 'train.csv' dataset below.""") | |
| df = pd.read_csv("train.csv") | |
| def download_csv(): | |
| df.to_csv("train.csv", index=False) | |
| with open("train.csv", "rb") as f: | |
| data = f.read() | |
| return data | |
| # Create a download button | |
| button_label = ":violet[Download CSV]" | |
| button_download = st.download_button(label=button_label, data=download_csv(), file_name='train.csv', mime='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet') | |
| st.write("---") | |
| st.components.v1.html(html_data, width=1000, height=8000) | |
| def download_notebook(): | |
| with open("Sentiment_analysis.ipynb", "rb") as f: | |
| data = f.read() | |
| return data | |
| # Create a download button for the notebook | |
| st.write("----") | |
| st.write("To download 'Sentiment Analysis' Jupyter notebook click on the button below.") | |
| button_label = ":violet[Download Jupyter Notebook]" | |
| button_download = st.download_button(label=button_label, data=download_notebook(), file_name="Sentiment_analysis.ipynb", mime='application/x-ipynb+json') | |