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| # + | |
| from features import FeatureGenerator | |
| from joblib import load | |
| import gradio as gr | |
| import pandas as pd | |
| import numpy as np | |
| import plotly.graph_objects as go | |
| model = load('xgb_linreg_rf.joblib') | |
| feature_generator = FeatureGenerator() | |
| def round_h(x): | |
| return np.round(2 * x) / 2 | |
| def score_essay(text): | |
| if not text: | |
| gr.Error("Empty essay text") | |
| return None, None | |
| df = pd.DataFrame({"full_text": [text]}) | |
| df = feature_generator.generate_features(df) | |
| res = model.predict(df.iloc[:, 1:])[0].clip(1, 5) | |
| total_score = round_h(res.mean()) | |
| rounded_res = round_h(res) | |
| predicted_values = ["cohesion", "conventions", "grammar", "phraseology", "syntax", "vocabulary"] | |
| res_df = pd.DataFrame(rounded_res, index=predicted_values) | |
| total_score_msg = str(total_score) | |
| if total_score > 4: | |
| total_score_msg += " \N{party popper}" | |
| return total_score_msg, go.Figure(go.Bar(x=rounded_res, y=predicted_values, orientation='h', text=rounded_res), | |
| layout_xaxis_range=[1, 5]) | |
| examples = ['Hello, my name is Katherine, and I enjoy reading books. My favorite book is War and Peace by Leo Tolstoy.'] | |
| title = "English proficiency checker" | |
| description = "Your essay will be scored according to six analytic measures: cohesion, \ | |
| syntax, vocabulary, phraseology, grammar, and conventions. \ | |
| Each measure represents a component of proficiency in essay \ | |
| writing, with greater scores corresponding to greater proficiency \ | |
| in that measure. The scores range from 1.0 to 5.0 in increments of 0.5." | |
| demo = gr.Interface( | |
| fn=score_essay, | |
| inputs=gr.Textbox(lines=2, show_label=False, placeholder="Put an essay here..."), | |
| outputs=[gr.Textbox(label='Your score', interactive=False), gr.Plot(show_label=False)], | |
| allow_flagging='never', | |
| examples=examples, | |
| title=title, | |
| description=description, | |
| ) | |
| demo.launch() | |