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


class SentimentAnalyzer:
 def __init__(self):
 # self.model="facebook/bart-large-mnli"
 openai.api_key=os.getenv("OPENAI_API_KEY")
 def analyze_sentiment(self, text):
 prompt = f""" Your task is find the top 3 setiments : <labels = positive, negative, neutral> and it's sentiment score for the Mental Healthcare Doctor Chatbot and patient conversation text.\
 your are analyze the text and provide the output in the following json order: \"\"\"
 <\{result['labels'][0]: result['scores'][0], result['labels'][1]: result['scores'][1], result['labels'][2]: result['scores'][2] \}>\"\"\" \
 analyze the text : '''{text}'''
 """
 response = openai.Completion.create(
 model="text-davinci-003",
 prompt=prompt,
 temperature=1,
 max_tokens=60,
 top_p=1,
 frequency_penalty=0,
 presence_penalty=0
 )

 # Extract the generated text
 sentiment_scores_str = response.choices[0].text.strip()
 print(sentiment_scores_str)
 return sentiment_scores_str

 
 def emotion_analysis(self,text):
 prompt = f""" Your task is find the top 3 emotion : <Sadness, Happiness, Joy, Fear, Disgust, Anger> and it's emotion score for the Mental Healthcare Doctor Chatbot and patient conversation text.\
 your 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 1 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.\
 analyze the text : '''{text}'''
 """
 response = openai.Completion.create(
 model="text-davinci-003",
 prompt=prompt,
 temperature=1,
 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 top 3 setiments : <labels = positive, negative, neutral> and it's sentiment score for the Mental Healthcare Doctor Chatbot and patient conversation text.\
 your are analyze the text and provide the output in the following json format heigher to lower order: \"\"\"<"label1": score1,"label2":score2,"label3":score3>\"\"\" \
 analyze the text : '''{text}'''
 """
 response = openai.Completion.create(
 model="text-davinci-003",
 prompt=prompt,
 temperature=1,
 max_tokens=60,
 top_p=1,
 frequency_penalty=0,
 presence_penalty=0
 )

 # Extract the generated text
 sentiment_scores = response.choices[0].text.strip()
 #sentiment_scores={sentiment_scores_list}
 print(sentiment_scores)
 return sentiment_scores

 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):
 labels = sentiment_scores.keys()
 scores = sentiment_scores.values()
 fig = px.bar(x=scores, y=labels, orientation='h', color=labels, color_discrete_map={"Negative": "red", "Positive": "green", "Neutral": "gray"})
 fig.update_traces(texttemplate='%{x:.2f}%', textposition='outside')
 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):
 customer_score = ", ".join([f"{key}: {value:.2f}" for key, value in customer_sentiment_score.items()])
 return f"customer_emotion:{customer_emotion}\nCustomer_sentiment_score:{customer_score}"

 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.Glass()) 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>""")
 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="Patient").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="AI Healthcare 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="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,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 Mental Healthcare ChatBot"
 demo.launch()
document_qa =LangChain_Document_QA()
document_qa.gradio_interface()