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
from transformers import pipeline
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 Mental Healthcare Doctor Chatbot and patient 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 Mental Healthcare Doctor Chatbot and patient 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"Patient: {customer_text}\n"
            if agent_text:
                formatted_history += f"Mental Healthcare Doctor 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"Patient: {customer_text}\n"
            if agent_text:
                agent+=agent_text
                formatted_history += f"Mental Healthcare Doctor Chatbot: {agent_text}\n"
        return client,agent


    def _suggested_answer(self,text):
      try:
        history = self._chat_history()
        try:
          file_path = "patient_details.json"
          with open(file_path) as file:
              patient_details = json.load(file)
        except:
          pass

        prompt = f"""Analyse the patient json If asked for information take it from {patient_details} \
            you first get patient details : <get name,age,gender,contact,address from patient> if not match patient json information start new chat else match patient \
            json information ask previous: <description,symptoms,diagnosis,treatment talk about patient> As an empathic AI Mental Healthcare Doctor Chatbot, provide effective solutions to patients' mental health concerns. \
            first start the conversation ask existing patient or new patient. if new patient get name,age,gender,contact,address from the patient and start. \
            if existing customer get name,age,gender,contact,address details and start the chat about existing issues and current issues. \
            if patient say thanking tone message to end the conversation with a thanking greeting when the patient expresses gratitude. \
            Chat History:['''{history}'''] 
            Patient: ['''{text}''']
            Perform as Mental Healthcare Doctor 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 "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='JohnSmith9982/small_and_pretty') as demo:
          with gr.Row():
            gr.HTML("""<center><img class="image" src="https://www.syrahealth.com/images/SyraHealth_Logo_Dark.svg" alt="Image" width="210" height="210"></center>
            """)
          with gr.Row():
               gr.HTML("""<center><h1>AI Mental Healthcare ChatBot</h1></center>""")
          with gr.Row():
              with gr.Column(scale=1):
                  with gr.Row():
                      chatbot = gr.Chatbot([], elem_id="chatbot").style(height=360)
                  with gr.Row():
                      with gr.Column(scale=0.90):
                          txt = gr.Textbox(show_label=False,placeholder="Patient").style(container=False)
                      with gr.Column(scale=0.10):
                          emptyBtn = gr.Button("🧹 Clear")
              # with gr.Column(scale=0.30):
              #     with gr.Row():
              #         txt3 =gr.Textbox(
              #         show_label=False,
              #         placeholder="AI Healthcare Suggesstion",
              #         elem_id="suggestheight").style(container=False)
              #     with gr.Row():
              #         button=gr.Button(value="🚀send")
        
          # with gr.Row():
          #   with gr.Column(scale=0.80):
                
          #   with gr.Column(scale=0.20, min_width=0):
          with gr.Accordion("Conversational AI Analytics", open = False):
              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="Patient", size=(500, 600))
              with gr.Row():
                  with gr.Column(scale=1, min_width=0):
                      plot_3 =gr.Plot(label="Patient_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,chatbot)
          #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 Mental Healthcare ChatBot"
      demo.launch()
document_qa =LangChain_Document_QA()
document_qa.gradio_interface()