New API

app.py

@@ -4,7 +4,7 @@ from scipy import spatial
 from collections import defaultdict
 
 import tiktoken
-import openai
+from openai import OpenAI
 import gradio as gr
 from tenacity import retry, stop_after_attempt, wait_random_exponential
 
@@ -20,12 +20,14 @@ def ask_naive(query):
         {"role": "system", "content": "You are a college sociology professor. Provide a very brief answer to this student question."},
         {"role": "user", "content": query},
     ]
-    response = openai.ChatCompletion.create(
+
+    client = OpenAI()
+    response = client.chat.completions.create(
         model='gpt-3.5-turbo',
         messages=messages,
     )
     
-    response_message = response["choices"][0]["message"]["content"]
+    response_message = response.choices[0].message.content
     return response_message
 
 # search function
@@ -38,11 +40,15 @@ def strings_ranked_by_relatedness(
     top_n: int = 100
 ) -> tuple[list[str], list[float]]:
     """Returns a list of strings and relatednesses, sorted from most related to least."""
-    query_embedding_response = openai.Embedding.create(
+    
+    client = OpenAI()
+
+    
+    query_embedding_response = client.embeddings.create(
         model=EMBEDDING_MODEL,
         input=query,
     )
-    query_embedding = query_embedding_response["data"][0]["embedding"]
+    query_embedding = query_embedding_response.data[0].embedding
     strings_and_relatednesses = [
         (row["text"], relatedness_fn(query_embedding, row["embedding"]))
         for i, row in df.iterrows()
@@ -81,11 +87,13 @@ def respond(question, textbook_samples):
         """ }
         ]
     
-    response = openai.ChatCompletion.create(
+    client = OpenAI()
+    
+    response = client.chat.completions.create(
         model='gpt-3.5-turbo',
         n=1,
         messages=messages)
-    return response["choices"][0]["message"]["content"]
+    return response.choices[0].message.content
         
 def ask(query):
     psuedo_answer = ask_naive(query)