Delete app.py

app.py

@@ -1,42 +0,0 @@
-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"])) # fix for 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()
-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..."):
-        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)
-