Update app.py

app.py

@@ -1,11 +1,12 @@
+# from pydantic import NoneStr
 import os
 import mimetypes
 import requests
 import tempfile
 import gradio as gr
+import openai
 import re
 import json
-from openai import OpenAI
 from transformers import pipeline
 import matplotlib.pyplot as plt
 import plotly.express as px
@@ -13,8 +14,6 @@ import plotly.express as px
 class SentimentAnalyzer:
     def __init__(self):
         self.model="facebook/bart-large-mnli"
-        self.client = OpenAI()
-        
     def analyze_sentiment(self, text):
         pipe = pipeline("zero-shot-classification", model=self.model)
         label=["positive","negative","neutral"]
@@ -25,11 +24,14 @@ class SentimentAnalyzer:
     def emotion_analysis(self,text):
         prompt = f""" Your task is to analyze {text} and predict the emotion using scores. Emotions are categorized into the following list: Sadness, Happiness, Joy, Fear, Disgust, and Anger. You need to provide the emotion with 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.
 Please analyze the text and provide the output in the following format: emotion: score [with one result having the highest score]."""
-        response = self.client.completions.create(
-            model="gpt-3.5-turbo",
+        response = openai.Completion.create(
+            model="text-davinci-003",
             prompt=prompt,
             temperature=1,
-            max_tokens=60
+            max_tokens=60,
+            top_p=1,
+            frequency_penalty=0,
+            presence_penalty=0
         )
 
         message = response.choices[0].text.strip().replace("\n","")
@@ -61,14 +63,14 @@ Please analyze the text and provide the output in the following format: emotion:
 
 class Summarizer:
     def __init__(self):
-        self.client = OpenAI()
+        pass
 
     def generate_summary(self, text):
-        model_engine = "gpt-3.5-turbo"
+        model_engine = "text-davinci-003"
         prompt = f"""summarize the following conversation delimited by triple backticks.
                      write within 30 words.
                      ```{text}``` """
-        completions =  self.client.completions.create(
+        completions = openai.Completion.create(
             engine=model_engine,
             prompt=prompt,
             max_tokens=60,
@@ -86,7 +88,7 @@ sentiment = SentimentAnalyzer()
 class LangChain_Document_QA:
 
     def __init__(self):
-        self.client = OpenAI()
+        pass
 
     def _add_text(self,history, text):
         history = history + [(text, None)]
@@ -138,7 +140,7 @@ class LangChain_Document_QA:
 
 
     def _suggested_answer(self,text):
-
+      try:
         history = self._chat_history()
         start_sequence = "\nCustomer:"
         restart_sequence = "\nVodafone Customer Relationship Manager:"
@@ -149,18 +151,22 @@ class LangChain_Document_QA:
         prompt = f"""{history}{start_sequence}{text}{restart_sequence} if customer ask any information take it from {customer_details}.
                   if customer say thanks or thankyou tone related messages You should not ask anything to end the conversation with greetings tone.
         """
-        response = self.client.completions.create(
-            model="gpt-3.5-turbo",
+        response = openai.Completion.create(
+            model="text-davinci-003",
             prompt=prompt,
             temperature=0,
-            max_tokens=500
+            max_tokens=500,
+            top_p=1,
+            frequency_penalty=0,
+            presence_penalty=0.6,
         )
 
         message = response.choices[0].text.strip()
         if  ":" in message:
           message = re.sub(r'^.*:', '', message)
         return message.strip()
-
+      except:
+        return "I can't get the response"
 
 
 
@@ -201,14 +207,13 @@ class LangChain_Document_QA:
     def clear_func(self):
         history_state.clear()
     def gradio_interface(self):
-      with gr.Blocks(css="style.css") as demo:
+      with gr.Blocks(css="style.css",theme=gr.themes.Soft()) as demo:
+          with gr.Row():
+            gr.HTML("""<img class="leftimage" align="left" src="https://templates.images.credential.net/1612472097627370951721412474196.png" alt="Image" width="210" height="210">
+            <img align="right" class="rightimage" src="https://download.logo.wine/logo/Vodafone/Vodafone-Logo.wine.png" alt="Image" width="230" height="230" >""")
+
           with gr.Row():
-            gr.HTML("""<center class="darkblue" text-align:center;padding:30px;'>
-            <h1 style="color:#fff">
-               Virtual Assistant Chatbot
-            </h1>
-            </center>""")  
-          
+            gr.HTML("""<center><h1>Vodafone Generative AI CRM ChatBot</h1></center>""")
           chatbot = gr.Chatbot([], elem_id="chatbot").style(height=300)
           with gr.Row():
               with gr.Column(scale=0.50):
@@ -285,7 +290,7 @@ class LangChain_Document_QA:
           clear_btn.click(lambda: None, None, chatbot, queue=False)
           Sentiment_btn.click(self._on_sentiment_btn_click,[],[txt5,plot,plot_2,plot_3,plot_4])
 
+      demo.title = "Vodafone Generative AI CRM ChatBot"
       demo.launch()
-        
 document_qa =LangChain_Document_QA()
 document_qa.gradio_interface()