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| import streamlit as st | |
| from src.evaluate import evaluate_prompt, model_list | |
| st.title("Toxic Tweets") | |
| # description of the project | |
| st.info("This app utilizes a multi-head classifier that is fine-tuned to evaluate the toxicity level of a given prompt, providing 6 labels and their corresponding toxicity scores in descending order. By utilizing pre-trained language models, large labeled datasets, and fine-tuning techniques, the app helps determine if the prompt is toxic or not and contributes to enhancing online safety.") | |
| # variables defined | |
| sentiment_model_names = model_list() | |
| section1, section2 = st.columns(2) | |
| # function to predict the output | |
| def predict(model_name, prompt): | |
| output = evaluate_prompt(model_name, prompt) | |
| with section2: | |
| st.table(output) | |
| st.success("Completed!") | |
| # main code | |
| with section1: | |
| st.header("Input") | |
| prompt = st.text_area("Prompt", "You fucking idiot. I will kill you!") | |
| model = st.selectbox("Select Model", sentiment_model_names) | |
| st.warning("albert & bert are self-supervised models, so possible relations are\ | |
| LABLE_0|NEGATIVE, LABEL_1|POSITIVE.") | |
| st.button("Submit", on_click=lambda: predict(model, prompt)) | |
| with section2: | |
| st.header("Output") | |
| st.write("") | |