Update app.py

app.py

@@ -1,6 +1,6 @@
 import gradio as gr
 from transformers import AutoTokenizer, AutoModel
-import openai
+from openai import OpenAI
 import os
 
 # Load the NASA-specific bi-encoder model and tokenizer
@@ -9,7 +9,10 @@ bi_tokenizer = AutoTokenizer.from_pretrained(bi_encoder_model_name)
 bi_model = AutoModel.from_pretrained(bi_encoder_model_name)
 
 # Set up OpenAI API key
-openai.api_key = os.getenv('OPENAI_API_KEY')
+
+openaiapi = os.getenv('OPENAI_API_KEY')
+client = OpenAI(api_key=openaiapi)
+
 
 def encode_text(text):
     inputs = bi_tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=128)
@@ -23,19 +26,19 @@ def generate_response(user_input, context_embedding):
     combined_input = f"Question: {user_input}\nContext: {context_str}\nAnswer:"
     
     # Generate a response using GPT-4
-    response = openai.ChatCompletion.create(
+    response = client.chat.completions.create(
     model="gpt-4",
     messages=[
         {"role": "user", "content": combined_input}
     ],
-    max_tokens=150,
-    temperature=0.7,
+    max_tokens=400,
+    temperature=0.5,
     top_p=0.9,
     frequency_penalty=0.5,
     presence_penalty=0.0
     )
     
-    return response.choices[0].message['content'].strip()
+    return response.choices[0].message.content.strip()
 
 def chatbot(user_input, context=""):
     context_embedding = encode_text(context) if context else ""