---
license: cc0-1.0
---

**Note:** Due to nature of toxic comments, data and code contain explicit language.

Data is from kaggle, the *Toxic Comment Classification Challenge*
<br>
https://www.kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge/data?select=train.csv.zip

Dataset used for training: https://huggingface.co/datasets/vluz/Tox

Trained over 30 epoch in a runpod

### 🤗 Running demo here:    
https://huggingface.co/spaces/vluz/Tox

<hr>

Code requires pandas, tensorflow, and streamlit. All can be installed via `pip`.

```python
import os
import pickle
import streamlit as st
import tensorflow as tf
from tensorflow.keras.layers import TextVectorization


@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"])) # Keras bug
    new_v.set_weights(from_disk['weights'])
    return new_v


st.title("Toxic Comment Test")
st.divider()
model = load_model()
vectorizer = load_vectorizer()
input_text = st.text_area("Comment:", "I love you man, but fuck you!", height=150)
if st.button("Test"):
    with st.spinner("Testing..."):
        inputv = vectorizer([input_text])
        output = model.predict(inputv)
        res = (output > 0.5)
    st.write(["toxic","severe toxic","obscene","threat","insult","identity hate"], res)
    st.write(output)
```


Put `toxmodel.keras` and `vectorizer.pkl` into the `model` dir.     

Then do:
```
stramlit run toxtest.py
```

Expected results from default prompt are positive for 0 and 2

<hr>

Full code can be found here:
<br>
https://github.com/vluz/ToxTest/
<br>