Update app.py

app.py

@@ -47,6 +47,21 @@ def classify_emotion(audio):
     emotion_classifier = foreign_class(source="speechbrain/emotion-recognition-wav2vec2-IEMOCAP", pymodule_file="custom_interface.py", classname="CustomEncoderWav2vec2Classifier")
     out_prob, score, index, text_lab = emotion_classifier.classify_file(audio)
     return  emo_dict[text_lab[0]]
+
+def slider_logic(slider):
+        if slider == 1:
+            theshold = .98
+        elif slider == 2:
+            threshold = .88
+        elif slider == 3:
+            threshold = .77
+        elif slider == 4:
+            threshold = .66
+        elif slider == 5:
+            threshold = .55
+        else:
+            threshold = []
+        return threshold
     
 # Create a Gradio interface with audio file and text inputs
 def classify_toxicity(audio_file, text_input, classify_anxiety, emo_class, explitive_selection, slider):
@@ -59,6 +74,7 @@ def classify_toxicity(audio_file, text_input, classify_anxiety, emo_class, expli
         print("emo_class ", emo_class, "explitive select", explitive_selection)
 
         ## SLIDER ##
+        threshold = slider_logic(slider)
         
         #------- explitive call ---------------
         
@@ -94,10 +110,12 @@ def classify_toxicity(audio_file, text_input, classify_anxiety, emo_class, expli
         print("keys ", classification_output.keys())
 
         # plot.update(x=classification_df["labels"], y=classification_df["scores"])
-        
+        if toxicity_score > threshold:
+            print("threshold exceeded!!")
         return toxicity_score, classification_output, transcribed_text
         # return f"Toxicity Score ({available_models[selected_model]}): {toxicity_score:.4f}"
     else: 
+        threshold = slider_logic(slider)
         model = whisper.load_model("large")
         # model = model_cache[model_name]
         # class_names = classify_anxiety.split(",")