Upload app.py

app.py

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+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)