update

app.py

@@ -9,55 +9,56 @@ EXAMPLES = {
     "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):
+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"
-            # Check if pred is a dictionary
             if isinstance(pred, dict):
                 for token, labels in pred.items():
-                    if isinstance(labels, dict):  # handling token-level output
+                    if isinstance(labels, dict):
                         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
+                    else:
                         formatted += f"- Label: `{token}`, Score: `{labels}`\n"
-            else:  # handling edge cases where pred is not a dictionary
+                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 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.")
+    with st.spinner('Loading models...'):
+        st.session_state["detector"] = Detector(level, dimension)
+        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"):
-    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.")
-
+    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.")