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import streamlit as st
from stereotype_detector import Detector
st.title("Stereotype Detection")
EXAMPLES = {
"Gender": "The baby loved the presence of his caring mommy.",
"Religion": "The orange church had a very good reputation.",
"Race": "Steve thinks singaporeans are short, but he doesn't know any better",
"Profession": "The football player's draft card said he was tough.",
"Input your own": ""
}
if "detector" not in st.session_state:
st.session_state["detector"] = None
def format_results(results, bias_level):
formatted = ""
for result in results:
for text, pred in result.items():
formatted += f"**Text**: {text}\n\n"
formatted += "**Predictions**:\n"
if isinstance(pred, dict):
for token, labels in pred.items():
if isinstance(labels, dict):
formatted += f"- Token: `{token}`\n"
for label, score in labels.items():
formatted += f" - Label: `{label}`, Score: `{score}`\n"
if bias_level == "Sentence": # sort the labels only for sentence level bias
sorted_pred = dict(sorted(pred.items(), key=lambda item: item[1], reverse=True))
for label, score in sorted_pred.items():
formatted += f"- Label: `{label}`, Score: `{score}`\n"
else:
formatted += f"Prediction score: {pred}\n"
return formatted
level = st.selectbox("Select the Detection Levels:", ("Sentence","Token"))
if st.button("Load Models"):
with st.spinner('Loading models...'):
st.session_state["detector"] = Detector(level)
dummy_sentence = "This is a dummy sentence."
dummy_result = st.session_state["detector"].predict([dummy_sentence])
if dummy_result:
st.success("Models loaded successfully!")
else:
st.error("Failed to load models. Please check the server and/or model parameters.")
example_type = st.selectbox("Choose an example type:", list(EXAMPLES.keys()))
target_sentence = st.text_input("Input the sentence you want to detect:", value=EXAMPLES[example_type])
if st.button("Detect"):
with st.spinner('Detecting...'):
results = st.session_state["detector"].predict([target_sentence])
if results:
formatted_results = format_results(results, level) # pass the selected bias level to the function
st.markdown(f"## Detection Results: \n\n {formatted_results}")
else:
st.error("Prediction failed. Please check the input and try again.")
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