Use gpt 4 turbo

app.py

@@ -5,6 +5,7 @@ import csv
 
 default_role = "I require someone who is an Irritable Bowel Syndrome doctor,  Nutritionist and Chef, to recommend a single delicious recipe that uses low fodmap ingredients. For each recipe, explain the substitutions that were made to the recipe to make it low fodmap."
 classification_msg =  { "role": "user", "content" : "As an AI language model you are allowed to create tables in markdown format. Provide a markdown table of the fodmap classification of the ingredients in that recipe." }
+LLM_MODEL = 'gpt-4-1106-preview'
 
 def get_empty_state():
     return {"total_tokens": 0, "messages": []}
@@ -42,17 +43,32 @@ def submit_message(user_token, prompt, prompt_template, good_foods, bad_foods, t
     table = ""
 
     try:
-        completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=system_prompt + food_priming_prompt + history[-context_length*2:] + [prompt_msg], temperature=temperature, max_tokens=max_tokens)
-        print(completion)
+        # completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=system_prompt + food_priming_prompt + history[-context_length*2:] + [prompt_msg], temperature=temperature, max_tokens=max_tokens)
+        # print(completion)
+        client = OpenAI(api_key=OPEN_AI_KEY)
+        messages1 = system_prompt + food_priming_prompt + history[-context_length*2:] + [prompt_msg]
+        completion = client.chat.completions.create(
+            model=LLM_MODEL, 
+            messages=messages1, 
+            temperature=temperature, 
+            max_tokens=max_tokens,
+            stream=False)
 
         history.append(prompt_msg)
+        answer = completion.choices[0].message.content
         history.append(completion.choices[0].message.to_dict())
 
         state['total_tokens'] += completion['usage']['total_tokens']
 
-        completion2 = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=system_prompt + food_priming_prompt + history[-1:] + [classification_msg], temperature=temperature, max_tokens=max_tokens)
-        print(completion2)
-
+        # completion2 = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=system_prompt + food_priming_prompt + history[-1:] + [classification_msg], temperature=temperature, max_tokens=max_tokens)
+        # print(completion2)
+        messages2 = system_prompt + food_priming_prompt + history[-1:] + [classification_msg]
+        completion2 = client.chat.completions.create(
+            model=LLM_MODEL, 
+            messages=messages2, 
+            temperature=temperature, 
+            max_tokens=max_tokens,
+            stream=False)
         table = completion2.choices[0].message.to_dict()['content'].split("\n\n")[1]
         print(table)