Fix sharing state of model between users

app.py

@@ -14,8 +14,6 @@ gpt3_turbo = "gpt-3.5-turbo"
 gpt4 = "gpt-4"
 gpt4_turbo = "gpt-4-turbo-preview"
 
-model = gpt3_turbo
-
 def repo_get_all_employees_from_database():
     url = "https://api.airtable.com/v0/appopGmlHujYnd6Vw/Interviewers?maxRecords=100&view=Grid%20view"
     headers = {
@@ -77,27 +75,29 @@ def predict(message, history):
         5. At the end print ids and names of finally selected employees in json format. Please remember that in your output should be maximum {num_employees} employee.
         '''.format(data=data, date_time=date_time, num_employees=num_employees)
     
+    model = gpt3_turbo
+    
     for human, assistant in history:
+        if (switch_to.format(model=gpt3_turbo).lower() in human.lower()):
+            model = gpt3_turbo
+        if (switch_to.format(model=gpt4).lower() in human.lower()):
+            model = gpt4    
+        if (switch_to.format(model=gpt4_turbo).lower() in human.lower()):
+            model = gpt4_turbo  
+        
         history_openai_format.append({"role": "user", "content": human })
         history_openai_format.append({"role": "assistant", "content": assistant})
-    history_openai_format.append({"role": "user", "content": prompt})
 
-    global model
-    switched = False
-    
     if (switch_to.format(model=gpt3_turbo).lower() in message.lower()):
         model = gpt3_turbo
-        switched = True
-
     if (switch_to.format(model=gpt4).lower() in message.lower()):
         model = gpt4
-        switched = True
-
     if (switch_to.format(model=gpt4_turbo).lower() in message.lower()):
-        model = gpt4_turbo
-        switched = True
+        model = gpt4_turbo 
+    
+    history_openai_format.append({"role": "user", "content": prompt})
 
-    if (switched):
+    if (model != gpt3_turbo):
         print(switched_to.format(model=model))
   
     response = client.chat.completions.create(
@@ -107,10 +107,13 @@ def predict(message, history):
         temperature=0,
         stream=True)
 
-    partial_message = "🤖 {model}:\n\n".format(model=model)
+    msg_header = "🤖 {model}:\n\n".format(model=model)
+    partial_message = msg_header
     for chunk in response:
         if chunk.choices[0].delta.content is not None:
               partial_message = partial_message + chunk.choices[0].delta.content
+              pattern = r'({msg_header})+'.format(msg_header=msg_header)
+              partial_message = re.sub(pattern, msg_header, partial_message)
               yield partial_message
 
 pre_configured_promt = "For conducting an interview I need 1 employee in given time slot: start time is March 11 2024 2 pm, duration 1 hour"