Update app.py

app.py

@@ -35,16 +35,15 @@ tokenizer = AutoTokenizer.from_pretrained("medalpaca/medalpaca-7b")
 #load the first interface
 
 def fn(*args):
-  global symptoms
-  all_symptoms = [symptom for symptom_list in args for symptom in symptom_list]
-
-  if len(all_symptoms) > 17:
-    raise gr.Error("Please select a maximum of 17 symptoms.")
-  elif len(all_symptoms) < 3:
-    raise gr.Error("Please select at least 3 symptoms.")
-
-  symptoms = all_symptoms  # Update global symptoms list
-  return predd(loaded_rf,symptoms)
+    global symptoms
+    all_symptoms = [symptom for symptom_list in args for symptom in symptom_list]
+    if len(all_symptoms) > 17:
+        raise gr.Error("Please select a maximum of 17 symptoms.")
+    elif len(all_symptoms) < 3:
+        raise gr.Error("Please select at least 3 symptoms.")
+        symptoms = all_symptoms  # Update global symptoms list
+    loaded_rf = joblib.load("model_joblib")
+    return predd(loaded_rf,symptoms)
 
 
 
@@ -77,17 +76,16 @@ demo = gr.Interface(
 
 
 def predict(message, history):
-  prompt = f"""
-  Answer the following question:
-  {message}/n
-  Answer:
-  """
-  batch = tokenizer(prompt, return_tensors='pt')
-  with torch.cuda.amp.autocast():
+    prompt = f"""
+    Answer the following question:
+    {message}/n
+    Answer:
+    """
+    batch = tokenizer(prompt, return_tensors='pt')
+    with torch.cuda.amp.autocast():
 
     output_tokens = model.generate(**batch, max_new_tokens=100)
-
-  return tokenizer.decode(output_tokens[0], skip_special_tokens=True).replace(prompt,"")
+    return tokenizer.decode(output_tokens[0], skip_special_tokens=True).replace(prompt,"")
 
 
 loaded_rf = joblib.load("model_joblib")