| | import pandas as pd |
| | import streamlit as st |
| | from inference import inference |
| | from inference import DebertaEvaluator |
| |
|
| | st.title("Essay Scoring") |
| |
|
| | categories=['cohesion', 'syntax', 'vocabulary', 'phraseology', 'grammar', 'conventions'] |
| |
|
| | initial_scores = {category: '-' for category in categories} |
| | scores_df = pd.DataFrame(initial_scores, index=['Score']) |
| |
|
| | pd.set_option('display.float_format', lambda x: '%0.1f' % x) |
| |
|
| | text = "Here is a sample essay." |
| |
|
| | user_input = st.text_area("Enter your essay here:", value=text) |
| |
|
| | if st.button("Calculate Scores"): |
| | scores = inference(user_input) |
| | scores = [round(score * 2) / 2 for score in scores[0]] |
| | new_table = {categories[i]: scores[i] for i in range(len(categories))} |
| | scores_df = pd.DataFrame(new_table, index=['Score']) |
| |
|
| | |
| | st.table(scores_df) |
| |
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