Update app.py

app.py

@@ -18,22 +18,21 @@ def ask_question_HFModel1(question, modelHF, tokenizerHF):
     return question, answer
 
 def FetchQwenFromSpace(sQ):
-            '''
-            HFModelName ="Qwen/Qwen1.5-0.5B"
-            tokenizer = Qwen2Tokenizer.from_pretrained(HFModelName) #, trust_remote_code=bTrust_remote_code, model_max_length=8192) #model_max_length = 8192   #, use_fast=False    #Qwen
-            model = Qwen2ForCausalLM.from_pretrained(HFModelName)
-            modelHF = model
-            tokenizerHF = tokenizer
-            '''
-            #Using pre-trained model from HF
-            #from transformers import AutoModelCasualLM
-            sHuggingFacePath = "RohiniPS/Qwen1B-QnA-3-5" #"RohiniPS/Qwen1B-QnA-3-5" 
-            modelHF = Qwen2ForCausalLM.from_pretrained(sHuggingFacePath)    #("your_username/my-awesome-model")
-            tokenizerHF = Qwen2Tokenizer.from_pretrained(sHuggingFacePath)
-            ###
+    HFModelName ="Qwen/Qwen1.5-0.5B"
+    tokenizer = Qwen2Tokenizer.from_pretrained(HFModelName) #, trust_remote_code=bTrust_remote_code, model_max_length=8192) #model_max_length = 8192   #, use_fast=False    #Qwen
+    model = Qwen2ForCausalLM.from_pretrained(HFModelName)
+    modelHF = model
+    tokenizerHF = tokenizer
+    '''
+    #Using pre-trained model from HF
+    #from transformers import AutoModelCasualLM
+    sHuggingFacePath = "RohiniPS/Qwen1B-QnA-3-5" #"RohiniPS/Qwen1B-QnA-3-5" 
+    modelHF = Qwen2ForCausalLM.from_pretrained(sHuggingFacePath)    #("your_username/my-awesome-model")
+    tokenizerHF = Qwen2Tokenizer.from_pretrained(sHuggingFacePath)
+    ###'''
     
-            sQuestion, sAnswer = ask_question_HFModel1(sQ, modelHF, tokenizerHF)
-            return sQuestion, sAnswer
+    sQuestion, sAnswer = ask_question_HFModel1(sQ, modelHF, tokenizerHF)
+    return sQuestion, sAnswer
     
 
 def GetAnswer(Question):