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