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| import streamlit as st | |
| import pandas as pd | |
| from io import StringIO | |
| import csv | |
| from transformers import pipeline | |
| # Load the text classification model pipeline | |
| classifier = pipeline("text-classification", model="cychristophercyc/Group12_CustomModel_victory", return_all_scores=True) | |
| summarizer = pipeline("summarization", model="facebook/bart-large-cnn") | |
| # Streamlit application title | |
| st.title("News Categorization") | |
| st.write("Upload a CVS file containing content in 'content' column") | |
| genre = st.radio( | |
| "What's your Chosen Category", | |
| [":rainbow[Business and Finance]", | |
| ":rainbow[Health and Wellness]", | |
| ":rainbow[Sports]", | |
| ":rainbow[Arts, Culture, and Entertainment]", | |
| ":rainbow[Politics]", | |
| ":rainbow[Science and Technology]", | |
| ], | |
| ) | |
| if genre == ':rainbow[Business and Finance]': | |
| st.write('You selected Business and Finance.') | |
| select = "Business and Finance" | |
| if genre == ':rainbow[Health and Wellness]': | |
| st.write('You selected Health and Wellness.') | |
| select = "Health and Wellness" | |
| if genre == ':rainbow[Sports]': | |
| st.write('You selected Sports.') | |
| select = "Sports" | |
| if genre == ':rainbow[Arts, Culture, and Entertainment]': | |
| st.write('You selected Arts, Culture, and Entertainment.') | |
| select = "Arts, Culture, and Entertainment" | |
| if genre == ':rainbow[Politics]': | |
| st.write('You selected Politics.') | |
| select = "Politics" | |
| if genre == ':rainbow[Science and Technology]': | |
| st.write('You selected Science and Technology.') | |
| select = "Science and Technology" | |
| # upload file | |
| uploaded_file = st.file_uploader("Choose a file") | |
| if uploaded_file is not None: | |
| # To read file as bytes: | |
| bytes_data = uploaded_file.getvalue() | |
| st.write(bytes_data) | |
| # To convert to a string based IO: | |
| stringio = StringIO(uploaded_file.getvalue().decode("utf-8")) | |
| st.write(stringio) | |
| # To read file as string: | |
| string_data = stringio.read() | |
| st.write(string_data) | |
| # Can be used wherever a "file-like" object is accepted: | |
| dataframe = pd.read_csv(uploaded_file) | |
| st.write(dataframe) | |
| a = 0 | |
| content_list = [] | |
| list_of_dictionaries =[] | |
| for row in dataframe: | |
| # Extract the 'content' element and add it to the content_list | |
| content_list.append(row['content']) | |
| a+=1 | |
| if st.button("Classify"): | |
| # Perform text classification on the input text | |
| for n in range(a): | |
| results = classifier(content_list)[n] | |
| # Display the classification result | |
| max_score = float('-inf') | |
| max_label = '' | |
| for result in results: | |
| if result['score'] > max_score: | |
| max_score = result['score'] | |
| max_label = result['label'] | |
| st.write("Text:", content_list[n]) | |
| st.write("Label:", max_label) | |
| st.write("Score:", max_score) | |
| new_dictionary = { | |
| "Text":content_list[n], | |
| "Label" : max_label | |
| } | |
| list_of_dictionaries.append(new_dictionary) | |
| filtered_list = [d for d in list_of_dictionaries if d['Label'].strip() == select] | |
| for m in len(filtered_list): | |
| Sum = summarizer(content_list)[m] | |
| st.write("Summary:", content_list)[m]) | |