Update app.py

app.py

@@ -203,18 +203,16 @@ def predict(inputs, top_p, temperature, chat_counter, chatbot=[], history=[]):
     # TODO - make this parse out markdown so we can have similar interface
     counter=0
     for chunk in response.iter_lines():
-        #Skipping first chunk
+
         if counter == 0:
           counter+=1
           continue
-        #counter+=1
-        # check whether each line is non-empty
+
         if chunk.decode() :
           chunk = chunk.decode()
-          # decode each line as response data is in bytes
-          if len(chunk) > 12 and "content" in json.loads(chunk[6:])['choices'][0]['delta']:
-              #if len(json.loads(chunk.decode()[6:])['choices'][0]["delta"]) == 0:
-              #  break
+
+            if len(chunk) > 12 and "content" in json.loads(chunk[6:])['choices'][0]['delta']:
+
               partial_words = partial_words + json.loads(chunk[6:])['choices'][0]["delta"]["content"]
               if token_counter == 0:
                 history.append(" " + partial_words)
@@ -225,11 +223,11 @@ def predict(inputs, top_p, temperature, chat_counter, chatbot=[], history=[]):
 
               
               
-              df=pd.DataFrame()
-              if UseMemory: 
-                 outputfileName = 'ChatGPT-RLHF-Memory.csv' # Test first time file create
-                 df = store_message(chat, history, outputfileName) # Save to dataset
-                 basedir = get_base(outputfileName)
+              #df=pd.DataFrame()
+              #if UseMemory: 
+              #   outputfileName = 'ChatGPT-RLHF-Memory.csv' # Test first time file create
+              #   df = store_message(chat, history, outputfileName) # Save to dataset
+              #   basedir = get_base(outputfileName)
               #return history, df, basedir