| import streamlit as st | |
| from setfit import SetFitModel | |
| from file_processing import get_paragraphs | |
| ####################################### Dashboard ###################################################### | |
| # App | |
| st.title("Identify references to vulnerable groups.") | |
| st.write("""Vulnerable groups encompass various communities and individuals who are disproportionately affected by the impacts of climate change | |
| due to their socioeconomic status, geographical location, or inherent characteristics. By incorporating the needs and perspectives of these groups | |
| into national climate policies, governments can ensure equitable outcomes, promote social justice, and strive to build resilience within the most marginalized populations, | |
| fostering a more sustainable and inclusive society as we navigate the challenges posed by climate change.This app allows you to identify whether a text contains any | |
| references to vulnerable groups, for example when talking about policy documents.""") | |
| # Document upload | |
| uploaded_file = st.file_uploader("Upload your file here") | |
| # Create text input box | |
| #input_text = st.text_area(label='Please enter your text here', value="This policy has been implemented to support women.") | |
| #st.write('Prediction:', model(input_text)) | |
| ######################################### Model ######################################################### | |
| # Load the model | |
| model = SetFitModel.from_pretrained("leavoigt/vulnerable_groups") | |
| # Define the classes | |
| id2label = { | |
| 0: 'Agricultural communities', | |
| 1: 'Children and Youth', | |
| 2: 'Coastal communities', | |
| 3: 'Drought-prone regions', | |
| 4: 'Economically disadvantaged communities', | |
| 5: 'Elderly population', | |
| 6: 'Ethnic minorities and indigenous people', | |
| 7: 'Informal sector workers', | |
| 8: 'Migrants and Refugees', | |
| 9: 'Other', | |
| 10: 'People with Disabilities', | |
| 11: 'Rural populations', | |
| 12: 'Sexual minorities (LGBTQI+)', | |
| 13: 'Urban populations', | |
| 14: 'Women'} | |
| ### Process document to paragraphs | |
| # Source: https://blog.jcharistech.com/2021/01/21/how-to-save-uploaded-files-to-directory-in-streamlit-apps/ | |
| # Store uploaded file temporarily in directory to get file path (necessary for processing) | |
| # def save_uploadedfile(upl_file): | |
| # with open(os.path.join("tempDir",upl_file.name),"wb") as f: | |
| # f.write(upl_file.getbuffer()) | |
| # return st.success("Saved File:{} to tempDir".format(upl_file.name)) | |
| # if uploaded_file is not None: | |
| # # Save the file | |
| # file_details = {"FileName": uploaded_file.name, "FileType": uploaded_file.type} | |
| # save_uploadedfile(uploaded_file) | |
| # #Get the file path | |
| file = st.file_uploader("File upload", type=["pdf"]) | |
| if uploaded_file is not None: | |
| # Retrieve the file name | |
| with tempfile.NamedTemporaryFile(mode="wb") as temp: | |
| bytes_data = files.getvalue() | |
| temp.write(bytes_data) | |
| print(temp.name) | |
| # # Process file | |
| # par_list = get_paragraphs(uploaded_file) | |
| # ### Make predictions | |
| # preds = vg_model(par_list) | |
| # # Get label names | |
| # preds_list = preds.tolist() | |
| # predictions_names=[] | |
| # # loop through each prediction | |
| # for ele in preds_list: | |
| # try: | |
| # index_of_one = ele.index(1) | |
| # except ValueError: | |
| # index_of_one = "NA" | |
| # if index_of_one != "NA": | |
| # name = id2label[index_of_one] | |
| # else: | |
| # name = "NA" | |
| # predictions_names.append(name) | |
| # # Combine the paragraphs and labels to a dataframe | |
| # df_predictions = pd.DataFrame({'Paragraph': par_list, 'Prediction': predictions_names}) | |
| # # Drop all "Other" and "NA" predictions | |
| # filtered_df = df[df['Prediction'].isin(['Other', 'NA'])] | |
| # ##################################### | |
| # st.write(df) | |