Update app.py

app.py

@@ -1,9 +1,8 @@
 from typing import Dict
-
+import torch
 import gradio as gr
 import whisper
 from whisper.tokenizer import get_tokenizer
-
 import classify
 
 model_cache = {}
@@ -11,22 +10,15 @@ model_cache = {}
 
 def zero_shot_classify(audio_path: str, class_names: str, model_name: str) -> Dict[str, float]:
     class_names = class_names.split(",")
-    print("model name: ", model_name)
+    # Specify the path to your fine-tuned model and configuration
+    model_path = "mskov/whisper-small-esc50"
     
-    # Specify the path to the "whisper-small-esc50" model
-    model_path = "mskov/whisper-small-esc50"  
+    # Load the model
+    model = whisper.Whisper.load(model_path)
     
-    if model_name not in model_cache:
-        model = whisper.load_model(model_path)
-        print("whisper model not in model_cache ", model)
-        model_cache[model_name] = model
-    else:
-        model = model_cache[model_name]
-        print("model is in cache ", model)
+    # Load the tokenizer
+    tokenizer = whisper.tokenizer.get_tokenizer(multilingual=".en" not in model.config.name)
 
-    # Rest of your code remains the same
-    tokenizer = get_tokenizer(multilingual=".en" not in model_name)
-    
     internal_lm_average_logprobs = classify.calculate_internal_lm_average_logprobs(
         model=model,
         class_names=class_names,