Update app.py

app.py

@@ -85,7 +85,7 @@ history_state = gr.State()
 summarizer = Summarizer()
 sentiment = SentimentAnalyzer()
 
-class Chat_Bot:
+class LangChain_Document_QA:
 
  def __init__(self):
  pass
@@ -121,7 +121,7 @@ class Chat_Bot:
  scores = sentiment_scores.values()
  fig = px.bar(x=scores, y=labels, orientation='h', color=labels, color_discrete_map={"Negative": "red", "Positive": "green", "Neutral": "gray"})
  fig.update_traces(texttemplate='%{x:.2f}%', textposition='outside')
- fig.update_layout(height=300, width=160)
+ fig.update_layout(height=500, width=200)
  return fig
 
  def _history_of_chat(self):
@@ -145,10 +145,10 @@ class Chat_Bot:
  start_sequence = "\nCustomer:"
  restart_sequence = "\nVodafone Customer Relationship Manager:"
  prompt = 'your task is make a conversation between a customer and vodafone telecom customer relationship manager.'
- file_path = "vodafone_customer_details.json"
+ file_path = "/content/vodafone_customer_details.json"
  with open(file_path) as file:
  customer_details = json.load(file)
- prompt += f"{history}{start_sequence}{text}{restart_sequence} if customer ask any information take it from {customer_details} and if customer say thankyou You should end the conversation with greetings."
+ prompt = f"{history}{start_sequence}{text}{restart_sequence} if customer ask any information take it from {customer_details} and if customer say thankyou You should end the conversation with greetings."
  response = openai.Completion.create(
  model="text-davinci-003",
  prompt=prompt,
@@ -185,20 +185,20 @@ class Chat_Bot:
  scores=self._text_box(customer_emotion,agent_emotion,agent_sentiment_score,customer_sentiment_score)
 
  customer_fig=self._display_graph(customer_sentiment_score)
- customer_fig.update_layout(title="Sentiment Analysis",width=400)
+ customer_fig.update_layout(title="Sentiment Analysis",width=800)
 
  agent_fig=self._display_graph(agent_sentiment_score)
- agent_fig.update_layout(title="Sentiment Analysis",width=400)
+ agent_fig.update_layout(title="Sentiment Analysis",width=800)
 
  agent_emotion_score = sentiment.emotion_analysis_for_graph(agent_emotion)
 
  agent_emotion_fig=self._display_graph(agent_emotion_score)
- agent_emotion_fig.update_layout(title="Emotion Analysis",width=400)
+ agent_emotion_fig.update_layout(title="Emotion Analysis",width=800)
 
  customer_emotion_score = sentiment.emotion_analysis_for_graph(customer_emotion)
 
  customer_emotion_fig=self._display_graph(customer_emotion_score)
- customer_emotion_fig.update_layout(title="Emotion Analysis",width=400)
+ customer_emotion_fig.update_layout(title="Emotion Analysis",width=800)
 
  return scores,customer_fig,agent_fig,customer_emotion_fig,agent_emotion_fig
 
@@ -259,17 +259,17 @@ class Chat_Bot:
  with gr.Row():
  gr.HTML("""<center><h1>Sentiment and Emotion Score Graph</h1></center>""")
  with gr.Row():
- with gr.Column(scale=0.50, min_width=0):
- plot =gr.Plot(label="Customer", size=(300, 400))
- # with gr.Row():
- with gr.Column(scale=0.50, min_width=0):
- plot_2 =gr.Plot(label="Agent", size=(300, 400))
+ with gr.Column(scale=0.70, min_width=0):
+ plot =gr.Plot(label="Customer", size=(500, 600))
  with gr.Row():
- with gr.Column(scale=0.50, min_width=0):
- plot_3 =gr.Plot(label="Customer_Emotion", size=(300, 400))
- # with gr.Row():
- with gr.Column(scale=0.50, min_width=0):
- plot_4 =gr.Plot(label="Agent_Emotion", size=(300, 400))
+ with gr.Column(scale=0.70, min_width=0):
+ plot_2 =gr.Plot(label="Agent", size=(500, 600))
+ with gr.Row():
+ with gr.Column(scale=0.70, min_width=0):
+ plot_3 =gr.Plot(label="Customer_Emotion", size=(500, 600))
+ with gr.Row():
+ with gr.Column(scale=0.70, min_width=0):
+ plot_4 =gr.Plot(label="Agent_Emotion", size=(500, 600))
 
 
  txt_msg = txt.submit(self._add_text, [chatbot, txt], [chatbot, txt])
@@ -285,5 +285,5 @@ class Chat_Bot:
 
  demo.title = "Vodafone Generative AI CRM ChatBot"
  demo.launch()
-bot = Chat_Bot()
-bot.gradio_interface()
+document_qa =LangChain_Document_QA()
+document_qa.gradio_interface()