| import os |
| import re |
| import pickle |
| import streamlit as st |
| import tensorflow as tf |
| from tensorflow.keras.layers import TextVectorization |
|
|
|
|
| def clean_text(text): |
| text = re.sub(r'<[^>]+>', '', text) |
| text = re.sub(r'http\S+|www\S+|https\S+', '', text) |
| text = re.sub(r'[^a-zA-Z\'\s]', ' ', text) |
| text = re.sub(r'(\s)([iI][eE]|[eE][gG])(\s)', r' \2 ', text) |
| text = " ".join(text.split()) |
| return text.lower() |
|
|
|
|
| @st.cache_resource |
| def load_model(): |
| model = tf.keras.models.load_model(os.path.join("model", "toxmodel.keras")) |
| return model |
|
|
|
|
| @st.cache_resource |
| def load_vectorizer(): |
| from_disk = pickle.load(open(os.path.join("model", "vectorizer.pkl"), "rb")) |
| new_v = TextVectorization.from_config(from_disk['config']) |
| new_v.adapt(tf.data.Dataset.from_tensor_slices(["xyz"])) |
| new_v.set_weights(from_disk['weights']) |
| return new_v |
|
|
|
|
| st.title("Toxic Comment Test") |
| st.divider() |
| model = load_model() |
| vectorizer = load_vectorizer() |
| default_prompt = "i love you man, but fuck you!" |
| input_text = st.text_area("Comment:", default_prompt, height=150).lower() |
| if st.button("Test"): |
| if not input_text: |
| st.write("⚠ Warning: Empty prompt.") |
| elif len(input_text) < 15: |
| st.write("⚠ Warning: Model is far less accurate with a small prompt.") |
| if input_text == default_prompt: |
| st.write("Expected results from default prompt are positive for 0 and 2") |
| with st.spinner("Testing..."): |
| clean_input_text = clean_text(input_text) |
| inputv = vectorizer([clean_input_text]) |
| output = model.predict(inputv) |
| res = (output > 0.5) |
| st.write(["toxic","severe toxic","obscene","threat","insult","identity hate"], res) |
| st.write(output) |
|
|
