update

app.py

@@ -3,10 +3,6 @@ from bias_detector import Detector
 
 st.title("Multidimensional Multilevel Bias Detection")
 
-level = st.selectbox("Select the Bias Levels:", ("Token","Sentence"))
-dimension = st.selectbox("Select the Bias Dimensions:", ("All","Gender","Religion","Race","Profession"))
-detector = Detector(level,dimension)
-target_sentence = st.text_input("Input the sentence you want to detect:")
 
 def format_results(results):
     formatted = ""
@@ -14,13 +10,47 @@ def format_results(results):
         for text, pred in result.items():
             formatted += f"**Text**: {text}\n\n"
             formatted += "**Predictions**:\n"
-            for token, labels in pred.items():
-                formatted += f"- Token: `{token}`\n"
-                for label, score in labels.items():
-                    formatted += f"    - Label: `{label}`, Score: `{score}`\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:", ("Token","Sentence"))
+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.")
+
+target_sentence = st.text_input("Input the sentence you want to detect:")
+
 if st.button("Detect"):
     results = detector.predict([target_sentence])
-    formatted_results = format_results(results)
-    st.markdown(f"## Detection Results: \n\n {formatted_results}")
+    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.")
+
+
+
+
+

bias_detector/bias_detector.py

@@ -26,7 +26,7 @@ class Detector:
                     "Religion": "wu981526092/Token-Level-Religion-Bias-Detector",
                 },
                 "Sentence": {
-                    "All":None,
+                    "All": "wu981526092/Sentence-Level-Multidimensional-Bias-Detector",
                     "Religion": "wu981526092/Sentence-Level-Religion-Bias-Detector",
                     "Profession": "wu981526092/Sentence-Level-Profession-Bias-Detector",
                     "Race": "wu981526092/Sentence-Level-Race-Bias-Detector",
@@ -67,7 +67,7 @@ class Detector:
         # Create the API endpoint from the model path
         self.API_URL = f"https://api-inference.huggingface.co/models/{self.model_path}"
         API_token = os.getenv("BIAS_DETECTOR_API_KEY")
-        #API_token = "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"
+        API_token = "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"
         # Add authorization token (if required)
         self.headers = {"Authorization": f"Bearer {API_token}"} # Replace `your_api_token` with your token