Update app.py

app.py

@@ -2,9 +2,16 @@ import evaluate
 from evaluate.utils import launch_gradio_widget
 import gradio as gr
 from transformers import AutoModelForSequenceClassification, pipeline, RobertaForSequenceClassification, RobertaTokenizer, AutoTokenizer
+# pull in emotion detection
+# --- Add element for specification
+# pull in text classification
+# --- Add custom labels
+# --- Associate labels with radio elements
+# add logic to initiate mock notificaiton when detected
+# pull in misophonia-specific model
 
 # Create a Gradio interface with audio file and text inputs
-def classify_toxicity(audio_file, text_input):
+def classify_toxicity(audio_file, text_input, classify_anxiety):
     # Transcribe the audio file using Whisper ASR
     if audio_file != None:
         whisper_module = evaluate.load("whisper")
@@ -23,16 +30,31 @@ def classify_toxicity(audio_file, text_input):
  
     toxicity_score = toxicity_results["toxicity"][0]
     print(toxicity_score)
+
+    # Text classification 
+
+    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+
+    classifiation_model = pipeline("zero-shot-classification", model="MoritzLaurer/mDeBERTa-v3-base-mnli-xnli")
+    
+    sequence_to_classify = transcribed_text
+    candidate_labels = classify_anxiety
+    classification_output = classifier(sequence_to_classify, candidate_labels, multi_label=False)
+    print(classification_output)
+
+
     return toxicity_score, transcribed_text
     # return f"Toxicity Score ({available_models[selected_model]}): {toxicity_score:.4f}"
  
 with gr.Blocks() as iface:
+    with gr.Column():
+        classify = gr.Radio(["racial identity hate", "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...")
         submit_btn = gr.Button(label="Run")
     with gr.Column():
         out_text = gr.Textbox()
-    submit_btn.click(fn=classify_toxicity, inputs=[aud_input, text], outputs=out_text)
+    submit_btn.click(fn=classify_toxicity, inputs=[aud_input, text, classify], outputs=out_text)
 
 iface.launch()