Update app.py

app.py

@@ -14,7 +14,6 @@ import plotly.express as px
 class SentimentAnalyzer:
  def __init__(self):
  self.model="facebook/bart-large-mnli"
-
  def analyze_sentiment(self, text):
  pipe = pipeline("zero-shot-classification", model=self.model)
  label=["positive","negative","neutral"]
@@ -61,9 +60,11 @@ Please analyze the text and provide the output in the following format: emotion:
  print(score_dict)
  return score_dict
 
+
 class Summarizer:
  def __init__(self):
  pass
+
  def generate_summary(self, text):
  model_engine = "text-davinci-003"
  prompt = f"""summarize the following conversation delimited by triple backticks.
@@ -80,15 +81,15 @@ class Summarizer:
  message = completions.choices[0].text.strip()
  return message
 
-
-history_state = gr.State([])
+history_state = gr.State()
 summarizer = Summarizer()
 sentiment = SentimentAnalyzer()
 
 class LangChain_Document_QA:
 
  def __init__(self):
- pass
+ pass
+
  def _add_text(self,history, text):
  history = history + [(text, None)]
  history_state.value = history
@@ -143,11 +144,11 @@ class LangChain_Document_QA:
  history = self._chat_history()
  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.at end the conversation thanks for coming'
+ prompt = 'your task is make a conversation between a customer and vodafone telecom customer relationship manager.'
  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}."
+ 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,
@@ -202,97 +203,87 @@ class LangChain_Document_QA:
  return scores,customer_fig,agent_fig,customer_emotion_fig,agent_emotion_fig
 
 
- def clear_func(self,history_state):
- history_state.clear()
-
 
  def gradio_interface(self):
- 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">
+ 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><h1>Vodafone Generative AI CRM ChatBot</h1></center>""")
- gr.HTML(
- """<br style="color:white;">"""
- )
- chatbot = gr.Chatbot([], elem_id="chatbot").style(height=300)
- with gr.Row():
- with gr.Column(scale=0.50):
+
+ with gr.Row():
+ 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):
  txt = gr.Textbox(
  show_label=False,
  placeholder="Customer",
  ).style(container=False)
- with gr.Column(scale=0.50):
+ with gr.Column(scale=0.50):
  txt2 = gr.Textbox(
  show_label=False,
  placeholder="Agent",
  ).style(container=False)
- 
- with gr.Column(scale=0.40):
+
+ with gr.Column(scale=0.40):
  txt3 =gr.Textbox(
  show_label=False,
  placeholder="GPT_Suggestion",
  ).style(container=False)
- with gr.Column(scale=0.10, min_width=0):
+ with gr.Column(scale=0.10, min_width=0):
  button=gr.Button(
  value="🚀"
  )
- with gr.Row(scale=0.50):
- emptyBtn = gr.Button(
- "🧹 New Conversation",
- )
- with gr.Row():
- with gr.Column(scale=0.40):
+ with gr.Row():
+ with gr.Column(scale=0.40):
  txt4 =gr.Textbox(
  show_label=False,
  lines=4,
  placeholder="Summary",
  ).style(container=False)
- with gr.Column(scale=0.10, min_width=0):
+ with gr.Column(scale=0.10, min_width=0):
  end_btn=gr.Button(
  value="End"
  )
- with gr.Column(scale=0.40):
+ with gr.Column(scale=0.40):
  txt5 =gr.Textbox(
  show_label=False,
  lines=4,
  placeholder="Sentiment",
  ).style(container=False)
- 
- with gr.Column(scale=0.10, min_width=0):
+
+ with gr.Column(scale=0.10, min_width=0):
  Sentiment_btn=gr.Button(
  value="📊",callback=self._on_sentiment_btn_click
  )
- with gr.Row():
- gr.HTML("""<center><h1>Sentiment and Emotion Score Graph</h1></center>""")
- with gr.Row():
- with gr.Column(scale=0.70, min_width=0):
- plot =gr.Plot(label="Customer", size=(500, 600))
- with gr.Row():
- with gr.Column(scale=0.70, 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])
- txt_msg.then(lambda: gr.update(interactive=True), None, [txt])
- txt.submit(self._suggested_answer,txt,txt3)
- button.click(self._agent_text, [chatbot,txt3], chatbot)
- txt2.submit(self._agent_text, [chatbot, txt2], chatbot).then(
+ with gr.Row():
+ gr.HTML("""<center><h1>Sentiment and Emotion Score Graph</h1></center>""")
+ with gr.Row():
+ with gr.Column(scale=0.70, min_width=0):
+ plot =gr.Plot(label="Customer", size=(500, 600))
+ with gr.Row():
+ with gr.Column(scale=0.70, 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])
+ txt_msg.then(lambda: gr.update(interactive=True), None, [txt])
+ txt.submit(self._suggested_answer,txt,txt3)
+ button.click(self._agent_text, [chatbot,txt3], chatbot)
+ txt2.submit(self._agent_text, [chatbot, txt2], chatbot).then(
  self._agent_text, [chatbot, txt2], chatbot
- )
- end_btn.click(self._display_history, [], txt4)
- emptyBtn.click(self.clear_func,history_state,[])
- emptyBtn.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()
+ )
+ end_btn.click(self._display_history, [], txt4)
+
+ 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()