Update app.py

app.py

@@ -5,7 +5,7 @@ import validators
 import requests
 import tempfile
 import gradio as gr
-import openai
+from openai import AzureOpenAI
 import re
 import json
 from transformers import pipeline
@@ -16,14 +16,19 @@ import pandas as pd
 
 class SentimentAnalyzer:
     def __init__(self):
-        # self.model="facebook/bart-large-mnli"
-        openai.api_key=os.getenv("OPENAI_API_KEY")
+        
+        self.client = AzureOpenAI(
+          api_key = os.getenv("AZURE_OPENAI_API_KEY"),  
+          api_version = "2024-02-01",
+          azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
+        )
+        
     def emotion_analysis(self,text):
         prompt = f""" Your task is find the top 3 emotion for this converstion {text}: <Sadness, Happiness, Fear, Disgust, Anger> and it's emotion score for the Mental Healthcare Doctor Chatbot and patient conversation text.\
         you are analyze the text and provide the output in the following list format heigher to lower order: ["emotion1","emotion2","emotion3"][score1,score2,score3]''' [with top 3 result having the highest score]
         The scores should be in the range of 0.0 to 1.0, where 1.0 represents the highest intensity of the emotion.
         """
-        response = openai.Completion.create(
+        response = self.client.Completion.create(
             model="text-davinci-003",
             prompt=prompt,
             temperature=0,
@@ -77,7 +82,7 @@ class Summarizer:
     def __init__(self):
         openai.api_key=os.getenv("OPENAI_API_KEY")
     def generate_summary(self, text):
-        model_engine = "text-davinci-003"
+        model_engine = "GPT3"
         prompt = f"""summarize the following conversation delimited by triple backticks. write within 30 words.```{text}``` """
         completions = openai.Completion.create(
             engine=model_engine,
@@ -97,7 +102,11 @@ sentiment = SentimentAnalyzer()
 class LangChain_Document_QA:
 
     def __init__(self):
-        openai.api_key=os.getenv("OPENAI_API_KEY")
+        self.client = AzureOpenAI(
+          api_key = os.getenv("AZURE_OPENAI_API_KEY"),  
+          api_version = "2024-02-01",
+          azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
+        )
 
     def _add_text(self,history, text):
         history = history + [(text, None)]
@@ -171,8 +180,8 @@ class LangChain_Document_QA:
             Patient: ['''{text}''']
             Perform as Mental Healthcare Doctor Chatbot
                  """
-        response = openai.Completion.create(
-            model="text-davinci-003",
+        response = self.client.Completion.create(
+            model="GPT3",
             prompt=prompt,
             temperature=0,
             max_tokens=500,