Spaces:
Runtime error
Runtime error
| import streamlit as st | |
| from bias_detector import Detector | |
| st.title("Multidimensional Bias Multilevel 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": "" | |
| # Add more examples as needed | |
| } | |
| def format_results(results): | |
| formatted = "" | |
| for result in results: | |
| for text, pred in result.items(): | |
| formatted += f"**Text**: {text}\n\n" | |
| formatted += "**Predictions**:\n" | |
| # Check if pred is a dictionary | |
| if isinstance(pred, dict): | |
| for token, labels in pred.items(): | |
| if isinstance(labels, dict): # handling token-level output | |
| formatted += f"- Token: `{token}`\n" | |
| for label, score in labels.items(): | |
| formatted += f" - Label: `{label}`, Score: `{score}`\n" | |
| else: # handling sentence-level output when labels is not a dictionary | |
| formatted += f"- Label: `{token}`, Score: `{labels}`\n" | |
| else: # handling edge cases where pred is not a dictionary | |
| formatted += f"Prediction score: {pred}\n" | |
| return formatted | |
| level = st.selectbox("Select the Bias Levels:", ("Sentence","Token")) | |
| dimension = st.selectbox("Select the Bias Dimensions:", ("All","Gender","Religion","Race","Profession")) | |
| detector = Detector(level,dimension) | |
| if st.button("Load Models"): | |
| st.text("Loading models...") | |
| dummy_sentence = "This is a dummy sentence." | |
| dummy_result = detector.predict([dummy_sentence]) # perform a dummy prediction | |
| if dummy_result: | |
| st.text("Models loaded successfully!") | |
| else: | |
| st.text("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"): | |
| results = detector.predict([target_sentence]) | |
| if results: | |
| formatted_results = format_results(results) | |
| st.markdown(f"## Detection Results: \n\n {formatted_results}") | |
| else: | |
| st.text("Prediction failed. Please check the input and try again.") | |