| import streamlit as st | |
| import pandas as pd | |
| import plotly.express as px | |
| from src import * | |
| model = ModelLoader() | |
| prediction = PredictionServices(model.Model, model.Tokenizer) | |
| def single_predict(text): | |
| preds = prediction.single_predict(text) | |
| if preds < 0.5: | |
| st.success(f'Non Toxic Comment!!! :thumbsup:') | |
| else: | |
| st.error(f'Toxic Comment!!! :thumbsdown:') | |
| prediction.plot(preds) | |
| def batch_predict(data): | |
| if prediction.data_validation(data): | |
| st.success(f'Data Validation Successfull :thumbsup:') | |
| preds = prediction.batch_predict(data) | |
| return preds.to_csv(index=False).encode('utf-8') | |
| else: | |
| st.error(f'Data Validation Failed :thumbsdown:') | |
| st.title('Toxic Comment Classifier') | |
| menu = ["Single Value Prediciton","Batch Prediction"] | |
| choice = st.sidebar.radio("Menu",menu) | |
| if choice=="Single Value Prediciton": | |
| st.subheader("Prediction") | |
| form = st.form("comment_form") | |
| comment = form.text_input("Enter comment") | |
| form.form_submit_button("Predict",on_click=single_predict(comment)) | |
| else: | |
| st.subheader("Batch Prediction") | |
| csv_file = st.file_uploader("Upload Image",type=['csv','parquet']) | |
| if csv_file is not None: | |
| csv = batch_predict(csv_file) | |
| st.download_button( | |
| label="Predict and Download", | |
| data=csv, | |
| file_name='prediction.csv', | |
| mime='text/csv', | |
| ) | |