Update app.py

app.py

@@ -1,63 +1,167 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+import random
+import time
+import os
+from sales_helper import SalesGPT
+from langchain_openai import AzureChatOpenAI
+from openai import AzureOpenAI
+llm = AzureChatOpenAI(temperature=0,deployment_name="GPT-3")
+from time import sleep
+sales_agent = SalesGPT.from_llm(llm, verbose=False)
+# init sales agent
+sales_agent.seed_agent()
+stage = "\n"
+bot_conversation = ""
+customer_conversation = ""
+convo_history = sales_agent.conversation_history
+client = AzureOpenAI()
+def user(user_message, history):
+    if user_message:
+        sales_agent.human_step(user_message)
+    return "", history + [[user_message, None]]
+
+def stages():
+  global stage
+  stage += "\n\n"+sales_agent.determine_conversation_stage()
+  return stage
+
+def download_report():
+  global convo_history
+  sales_evaluation_criteria = {
+      "understanding": "Does the salesperson understand the customer pain points and challenges?",
+      "opening_effectiveness": "Was the opening of the pitch effective?",
+      "focus_on_benefits": "Was there sufficient focus and emphasis on customer benefits of the products/features pitched?",
+      "trust_building": "Did the salesperson establish trust and credibility by sharing testimonials, case studies, references, success stories of other satisfied customers?",
+      "urgency_creation": "Did the salesperson create urgency, such as through time-sensitive offers or consequences of not taking the decision?",
+      "objection_handling": "Did the salesperson handle objections well and proactively address/prepared for the objections?",
+      "engagement": "Was the conversation engaging?",
+      "balance_of_talk_and_listen": "Was there a balance between the salesperson talking and listening to the customer?",
+      "closing_strategy": "Was there a clear call to action, summarization, and reiteration of the value proposition in the close strategy?",
+      "purposefulness": "Throughout the pitch/conversation, was the conversation purposeful, and did it end with clear next steps?"
+  }
+  client = AzureOpenAI()
+
+  conversation = [
+    {"role": "system", "content": f"You Are Context verification Reporter.using these condition {sales_evaluation_criteria} to verify following context to Give me a Report Form of the context Scoring and Reason for Scoring."},
+    {"role": "user", "content": f""" this is the Context:{convo_history}.
+      """}
+      ]
+  response = client.chat.completions.create(
+      model="GPT-3",
+      messages=conversation,
+      temperature=0,
+      max_tokens=1000
+  )
+
+  message = response.choices[0].message.content
+  report_file_path = f"report.txt"
+  with open(report_file_path,"w") as file:
+      file.write(message)  
+
+  return message
+
+def bot(history):
+    bot_message = sales_agent._call({})
+    history[-1][1] = ""
+    for character in bot_message:
+        history[-1][1] += character
+        time.sleep(0.05)
+        yield history
+summarizer = Summarizer()
+sentiment = SentimentAnalyzer()
+
+def history_of_both(convo_history):
+    # Initialize lists to store messages from customer and bot
+    customer_messages = []
+    bot_messages = []
+
+    # Iterate through the input list
+    for message in input_list:
+        if message.endswith('<END_OF_TURN>'):
+            # Customer message
+            if len(customer_messages) == len(bot_messages):
+                customer_messages.append(message[:-13])
+            else:
+                bot_messages.append(message[:-13])
+        else:
+            # Bot message
+            if len(customer_messages) == len(bot_messages):
+                bot_messages.append(message)
+            else:
+                customer_messages.append(message)
+
+    bot_conversation = " ".join(bot_messages)
+    customer_conversation = " ".join(customer_messages)
+
+    return bot_conversation, customer_conversation
+
+def generate_convo_summary():
+    global convo_history
+    summary=summarizer.generate_summary(convo_history)
+    return summary
+
+def sentiment_analysis():
+    global convo_history
+    bot_conversation, customer_conversation = history_of_both(convo_history)
+    customer_conversation_sentiment_scores = sentiment.analyze_sentiment(customer_conversation)
+    bot_conversation_sentiment_scores = sentiment.analyze_sentiment(bot_conversation)
+    return "Sentiment Scores for customer_conversation:\n"+customer_conversation_sentiment_scores+"\nSentiment Scores for sales_agent_conversation:\n"+bot_conversation_sentiment_scores
+
+def emotion_analysis():
+    global convo_history,bot_conversation,customer_conversation
+    bot_conversation, customer_conversation = history_of_both(convo_history)
+    customer_emotion=sentiment.emotion_analysis(customer_conversation)
+    bot_emotion=sentiment.emotion_analysis(bot_conversation)
+    return "Emotions for customer_conversation:\n"+customer_emotion+"\nEmotions for sales_agent_conversation:\n"+bot_emotion
+
+with gr.Blocks(theme="Taithrah/Minimal") as demo:
+    gr.HTML("""<center><h1>Sales Persona Chatbot</h1></center>""")
+    
+    with gr.Row():
+        with gr.Column():
+            chatbot = gr.Chatbot()
+        with gr.Column():
+            show_stages = gr.Textbox(label="Stages",lines=18,container=False)
+    with gr.Row():
+      with gr.Column(scale=0.70):
+        msg = gr.Textbox(show_label=False,container=False)
+      with gr.Column(scale=0.30):
+        clear = gr.Button("Clear")
+    with gr.Row():
+      with gr.Column(scale=0.50):
+        with gr.Row():
+          gen_report_view = gr.Textbox(label="Generated Report",container=False)
+        with gr.Row():
+          gen_report_btn = gr.Button("Generate Report")
+          report_down_btn = gr.DownloadButton(label="Download Report",value="report.txt")
+      with gr.Column(scale=0.50):
+        with gr.Row():
+          summary_view = gr.Textbox(label="Summary",container=False)
+        with gr.Row():
+          summary_btn = gr.Button("Generate Summary")
+    with gr.Row():
+      with gr.Column(scale=0.50):
+        with gr.Row():
+          sentiment_view = gr.Textbox(label="Sentiment",container=False)
+        with gr.Row():
+          sentiment_btn = gr.Button("Sentiment")
+      with gr.Column(scale=0.50):
+        with gr.Row():
+          emotion_view = gr.Textbox(label="Emotion",container=False)
+        with gr.Row():
+          emotion_btn = gr.Button("Emotion")
+
+
+    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
+        bot, chatbot, chatbot
+    )
+    msg.submit(stages,[],show_stages)
+    gen_report_btn.click(download_report,[],gen_report_view,queue=False)
+    summary_btn.click(generate_convo_summary,[],summary_view)
+    sentiment_btn.click(sentiment_analysis,[],sentiment_view)
+    emotion_btn.click(emotion_analysis,[],emotion_view)
+    clear.click(lambda: None, None, chatbot, queue=False)
+    clear.click(lambda: None, None, show_stages, queue=False)
+    
+demo.queue()
+demo.launch()