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import os |
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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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@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.cache_resource |
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def load_vocab(): |
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vocab = {} |
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with open('vocab.txt', 'r') as f: |
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for line in f: |
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token, index = line.strip().split('\t') |
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vocab[token] = int(index) |
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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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input_text = st.text_area("Comment:", "I love you man, but fuck you!", height=150) |
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if st.button("Test"): |
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with st.spinner("Testing..."): |
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inputv = vectorizer([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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print(output) |
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