Update app.py

app.py

@@ -22,6 +22,14 @@ emo_dict = {
     'neu': 'Neutral'
 }
 
+# static classes for now, but it would be best ot have the user select from multiple, and to enter their own
+class_options = {
+    "racism": ["racism", "hate speech", "bigotry", "racially targeted", "racially diminutive", "racial slur", "ethnic slur", "ethnic hate", "pro-white nationalism"],
+    "LGBTQ+ hate": ["homophobia", "gay slur", "trans slur", "homophobic slur", "transphobia", "anti-LBGTQ+", "hate speech"],
+    "sexually explicit": ["sexually explicit", "sexually coercive", "sexual exploitation", "vulgar", "raunchy", "sexually demeaning", "sexual violence", "victim blaming"],
+    "misophonia": ["chewing", "breathing", "mouthsounds", "popping", "sneezing", "yawning", "smacking", "sniffling", "panting"]
+}
+
 pipe = pipeline("automatic-speech-recognition")
 
 # Create a Gradio interface with audio file and text inputs
@@ -54,7 +62,7 @@ def classify_toxicity(audio_file, text_input, classify_anxiety):
     text_classifier = pipeline("zero-shot-classification", model="MoritzLaurer/mDeBERTa-v3-base-mnli-xnli")
     
     sequence_to_classify = transcribed_text
-    candidate_labels = classify_anxiety
+    candidate_labels = class_options.get(classify_anxiety, [])
     # classification_output = classifier(sequence_to_classify, candidate_labels, multi_label=False)
     classification_output = text_classifier(sequence_to_classify, candidate_labels, multi_label=False)
     print(classification_output)
@@ -69,7 +77,7 @@ def classify_toxicity(audio_file, text_input, classify_anxiety):
  
 with gr.Blocks() as iface:
     with gr.Column():
-        classify = gr.Radio(["racial identity hate", "LGBTQ+ hate", "sexually explicit", "misophonia"])
+        classify = gr.Radio(["racism", "LGBTQ+ hate", "sexually explicit", "misophonia"])
     with gr.Column():
         aud_input = gr.Audio(source="upload", type="filepath", label="Upload Audio File")
         text = gr.Textbox(label="Enter Text", placeholder="Enter text here...")