app.py

from pydantic import NoneStr
import os
import mimetypes
import validators
import requests
import tempfile
import gradio as gr
import openai
import re
import json
import matplotlib.pyplot as plt
import plotly.express as px
import pandas as pd


class SentimentAnalyzer:
    def __init__(self):
        # self.model="facebook/bart-large-mnli"
        openai.api_key=os.getenv("OPENAI_API_KEY")
    def emotion_analysis(self,text):
        prompt = f""" Your task is find the top 3 emotion for this converstion {text}: <Sadness, Happiness, Fear, Disgust, Anger> and it's emotion score for the Human Resource Assistant Chatbot and Job Seeker conversation text.\
        you are analyze the text and provide the output in the following list format heigher to lower order: '''["emotion1","emotion2","emotion3"][score1,score2,score3]''' [with top 3 result having 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.
        """
        response = openai.Completion.create(
            model="text-davinci-003",
            prompt=prompt,
            temperature=0,
            max_tokens=60,
            top_p=1,
            frequency_penalty=0,
            presence_penalty=0
        )
        message = response.choices[0].text.strip().replace("\n","")
        return message

    def analyze_sentiment_for_graph(self, text):
        prompt = f""" Your task is find the setiments for this converstion {text} : <labels = positive, negative, neutral> and it's sentiment score for the Human Resource Assistant Chatbot and Job Seeker conversation text.\
        you are analyze the text and provide the output in the following json format heigher to lower order: '''["label1","label2","label3"][score1,score2,score3]'''
        """
        response = openai.Completion.create(
        model="text-davinci-003",
        prompt=prompt,
        temperature=0,
        max_tokens=60,
        top_p=1,
        frequency_penalty=0,
        presence_penalty=0
        )

        # Extract the generated text
        sentiment_scores = response.choices[0].text.strip()
        start_index = sentiment_scores.find("[")
        end_index = sentiment_scores.find("]")
        list1_text = sentiment_scores[start_index + 1: end_index]
        list2_text = sentiment_scores[end_index + 2:-1]
        sentiment = list(map(str.strip, list1_text.split(",")))
        scores = list(map(float, list2_text.split(",")))
        score_dict={"Sentiment": sentiment, "Score": scores}
        print(score_dict)
        return score_dict

    def emotion_analysis_for_graph(self,text):
        start_index = text.find("[")
        end_index = text.find("]")
        list1_text = text[start_index + 1: end_index]
        list2_text = text[end_index + 2:-1]
        emotions = list(map(str.strip, list1_text.split(",")))
        scores = list(map(float, list2_text.split(",")))
        score_dict={"Emotion": emotions, "Score": scores}
        print(score_dict)
        return score_dict


class Summarizer:
    def __init__(self):
        openai.api_key=os.getenv("OPENAI_API_KEY")
    def generate_summary(self, text):
        model_engine = "text-davinci-003"
        prompt = f"""summarize the following conversation delimited by triple backticks. write within 30 words.```{text}``` """
        completions = openai.Completion.create(
            engine=model_engine,
            prompt=prompt,
            max_tokens=60,
            n=1,
            stop=None,
            temperature=0.5,
        )
        message = completions.choices[0].text.strip()
        return message

history_state = gr.State()
summarizer = Summarizer()
sentiment = SentimentAnalyzer()

class LangChain_Document_QA:

    def __init__(self):
        openai.api_key=os.getenv("OPENAI_API_KEY")

    def _add_text(self,history, text):
        history = history + [(text, None)]
        history_state.value = history
        return history,gr.update(value="", interactive=False)

    def _agent_text(self,history, text):
        response = text
        history[-1][1] = response
        history_state.value = history
        return history

    def _chat_history(self):
        history = history_state.value
        formatted_history = " "
        for entry in history:
            customer_text, agent_text = entry
            formatted_history += f"Job Seeker: {customer_text}\n"
            if agent_text:
                formatted_history += f"Human Resource Assistant Chatbot: {agent_text}\n"
        return formatted_history

    def _display_history(self):
        formatted_history=self._chat_history()
        summary=summarizer.generate_summary(formatted_history)
        return summary

    def _display_graph(self,sentiment_scores):
        df = pd.DataFrame(sentiment_scores)
        fig = px.bar(df, x='Score', y='Sentiment', orientation='h', labels={'Score': 'Score', 'Labels': 'Sentiment'})
        fig.update_layout(height=500, width=200)
        return fig
    def _display_graph_emotion(self,customer_emotion_score):
        
        fig = px.pie(customer_emotion_score, values='Score', names='Emotion', title='Emotion Distribution', hover_data=['Score'])
        #fig.update_traces(texttemplate='Emotion', textposition='outside')
        fig.update_layout(height=500, width=200)
        return fig
    def _history_of_chat(self):
        history = history_state.value
        formatted_history = ""
        client=""
        agent=""
        for entry in history:
            customer_text, agent_text = entry
            client+=customer_text
            formatted_history += f"Job Seeker: {customer_text}\n"
            if agent_text:
                agent+=agent_text
                formatted_history += f"Human Resource Assistant Chatbot: {agent_text}\n"
        return client,agent


    def _suggested_answer(self,text):
      try:
        history = self._chat_history()
        prompt = f"""Looking for a new job? I'm here to help. First, let's discuss your background and what you're aiming for career-wise.
            Here are some job-hunt tips:
            First Impressions: Be on time, dress well, and stay positive during interviews.
            Preparation: Learn about the company, remember your interviewer's name, carry extra resumes, and have good questions ready.
            Presentation: Be neat and professional in your appearance.
            Resume: Pick a clear format, include your contact details and summary. List your experiences, achievements, skills, and tailor each application.
            Attitude: Highlight your transferable skills and eagerness to learn. Don't apologize for lack of experience - focus on what you can offer.
            With patience and strategy, you'll find a job that suits you. What kind of job are you seeking?
            Please note, I'll be professional and respectful during our chat. Expect a polite closing even when you thank me. Remember, I won't repeat my replies.
            So, how can I help with your job search today?
            Chat History:['''{history}'''] 
            Job Seeker: ['''{text}''']
            Perform as Human Resource Assistant Chatbot
                 """
        response = openai.Completion.create(
            model="text-davinci-003",
            prompt=prompt,
            temperature=0,
            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 "Hi, How can I help you?"



    def _text_box(self,customer_emotion,customer_sentiment_score):  
        sentiment_str = ', '.join([f'{label}: {score}' for label, score in zip(customer_sentiment_score['Sentiment'], customer_sentiment_score['Score'])])
        #emotion_str = ', '.join([f'{emotion}: {score}' for emotion, score in zip(customer_emotion['Emotion'], customer_emotion['Score'])])
        return f"Sentiment: {sentiment_str},\nEmotion: {customer_emotion}"

    def _on_sentiment_btn_click(self):
        client=self._history_of_chat()

        customer_emotion=sentiment.emotion_analysis(client)
        customer_sentiment_score = sentiment.analyze_sentiment_for_graph(client)

        scores=self._text_box(customer_emotion,customer_sentiment_score)

        customer_fig=self._display_graph(customer_sentiment_score)
        customer_fig.update_layout(title="Sentiment Analysis",width=800)

        customer_emotion_score = sentiment.emotion_analysis_for_graph(customer_emotion)

        customer_emotion_fig=self._display_graph_emotion(customer_emotion_score)
        customer_emotion_fig.update_layout(title="Emotion Analysis",width=800)
        return scores,customer_fig,customer_emotion_fig


    def clear_func(self):
      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://upload.wikimedia.org/wikipedia/commons/thumb/1/10/Randstad_Logo.svg/2560px-Randstad_Logo.svg.png" alt="Image" width="210" height="210">
            <img align="right" class="rightimage brightness" src="https://workllama.com/wp-content/uploads/2022/05/WL_Logo.svg" alt="Image" width="230" height="230" >
            <br>""")
          with gr.Row():
            gr.HTML("""<center><h1>Job Seeker Assistant ChatBot</h1></center>""")
          chatbot = gr.Chatbot([], elem_id="chatbot").style(height=360)
          with gr.Row():
              with gr.Column(scale=0.8):
                  txt = gr.Textbox(
                      show_label=False,
                      placeholder="Job_Seeker").style(container=False)
              with gr.Column(scale=0.2):
                  emptyBtn = gr.Button("🧹 Clear")
          with gr.Row():
            with gr.Column(scale=0.80):
                txt3 =gr.Textbox(
                      show_label=False,
                      placeholder="HR Assistant Suggesstion").style(container=False)
            with gr.Column(scale=0.20, min_width=0):
                button=gr.Button(value="🚀send")
          with gr.Row():
              with gr.Column(scale=0.50):
                  txt4 =gr.Textbox(
                      show_label=False,
                      lines=4,
                      placeholder="Summary").style(container=False)
              with gr.Column(scale=0.50):
                  txt5 =gr.Textbox(
                      show_label=False,
                      lines=4,
                      placeholder="Sentiment").style(container=False)
          with gr.Row():
              with gr.Column(scale=0.50, min_width=0):
                  end_btn=gr.Button(value="End")
              with gr.Column(scale=0.50, 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=1, min_width=0):
                  plot =gr.Plot(label="Job_Seeker", size=(500, 600))
          with gr.Row():
              with gr.Column(scale=1, min_width=0):
                  plot_3 =gr.Plot(label="Job_Seeker_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)
          end_btn.click(self._display_history, [], txt4)
          emptyBtn.click(self.clear_func,[],[])
          emptyBtn.click(lambda: None, None, chatbot, queue=False)

          Sentiment_btn.click(self._on_sentiment_btn_click,[],[txt5,plot,plot_3])
 
      demo.title = "AI HR ChatBot"
      demo.launch()
document_qa =LangChain_Document_QA()
document_qa.gradio_interface()