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()