# from pydantic import NoneStr import os import mimetypes # import validators import requests import tempfile import gradio as gr from openai import OpenAI import re import json from transformers import pipeline import matplotlib.pyplot as plt import plotly.express as px import pandas as pd import json import plotly.graph_objects as go client = OpenAI() class SentimentAnalyzer: def __init__(self): pass def emotion_analysis(self,text): # Create a conversation for the OpenAI chat API conversation = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": f""" Your task is find the emotions for this converstion. Conversation: {text} lables : [Sadness, Happiness, Fear, Anger] provide emotion score for each label for given conversation. Return answer should be in valid JSON format only. """} ] # Call OpenAI GPT-3.5-turbo chat_completion = client.chat.completions.create( model = "gpt-3.5-turbo", messages = conversation, max_tokens=500, temperature=0 ) response = chat_completion.choices[0].message.content print("emotion_analysis", response) return response def analyze_sentiment_for_graph(self, text): # Create a conversation for the OpenAI chat API conversation = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": f""" Your task is analyse the conversarion to provide the sentiment analysis. ```converstion: {text}``` ```labels :[ positive, negative, neutral]``` provide sentiment score for each label for given conversation. Return answer should be in valid JSON format only. """} ] # Call OpenAI GPT-3.5-turbo chat_completion = client.chat.completions.create( model = "gpt-3.5-turbo", messages = conversation, max_tokens=500, temperature=0 ) response = chat_completion.choices[0].message.content print("analyze_sentiment_for_graph", response) return response class Summarizer: def __init__(self): # openai.api_key=os.getenv("OPENAI_API_KEY") pass def generate_summary(self, text): # Create a conversation for the OpenAI chat API conversation = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": f"""summarize the following conversation delimited by triple backticks. write within 60 words.```{text}``` """} ] # Call OpenAI GPT-3.5-turbo chat_completion = client.chat.completions.create( model = "gpt-3.5-turbo-1106", messages = conversation, max_tokens=500, temperature=0 ) response = chat_completion.choices[0].message.content return response history_state = gr.State() summarizer = Summarizer() sentiment = SentimentAnalyzer() class LangChain_Document_QA: def __init__(self): # openai.api_key=os.getenv("OPENAI_API_KEY") pass def _display_history(self): formatted_history=self._chat_history() # formatted_history = _suggested_answer() summary=summarizer.generate_summary(formatted_history) return summary def _display_graph(self,json_string): # Parse the JSON string into a dictionary json_data = json.loads(json_string) sentiments = list(json_data.keys()) scores = list(json_data.values()) fig = go.Figure(data=[go.Bar(x=sentiments, y=scores, marker_color=['green', 'red', 'blue'])]) fig.update_layout( title='Sentiment Analysis Scores', xaxis=dict(title='Sentiment'), yaxis=dict(title='Score'), ) return fig def _display_graph_emotion(self,json_string): # Parse the JSON string into a dictionary json_data = json.loads(json_string) sentiments = list(json_data.keys()) scores = list(json_data.values()) fig = go.Figure(data=[go.Bar(x=sentiments, y=scores, marker_color=['green', 'red', 'blue'])]) fig.update_layout( title='Emotion Analysis Scores', xaxis=dict(title='Emotions'), yaxis=dict(title='Score'), ) return fig def _suggested_answer(self, text, chat_history): try: file_path = "patient_details.json" with open(file_path) as file: patient_details = json.load(file) except: pass # Create a conversation for the OpenAI chat API conversation = [ {"role": "system", "content": "You are a Mental Healthcare Chatbot."}, {"role": "user", "content": f"""You are a Mental Healthcare Chatbot. Ask more about the patient's problem as step by step. Then give the short mental healthcare solution for patient's problems. ```Chat History:{chat_history}``` Patient Query:{text}. Mental Healthcare Chatbot: """} ] # Call OpenAI GPT-3.5-turbo chat_completion = client.chat.completions.create( model = "gpt-3.5-turbo", messages = conversation, max_tokens=300, temperature=0 ) response = chat_completion.choices[0].message.content chat_history.append((text, response)) return "", chat_history def _on_sentiment_btn_click(self, history): # client=self._history_of_chat() customer_emotion=sentiment.emotion_analysis(history) customer_sentiment_score = sentiment.analyze_sentiment_for_graph(history) sentiment_graph = self._display_graph(customer_sentiment_score) emotion_graph = self._display_graph_emotion(customer_emotion) return sentiment_graph, emotion_graph def gradio_interface(self): with gr.Blocks(css="style.css",theme='JohnSmith9982/small_and_pretty') as demo: with gr.Row(): gr.HTML("""
Image
""") with gr.Row(): gr.HTML("""

AI Mental Healthcare ChatBot

""") with gr.Row(): with gr.Column(scale=1): with gr.Row(): chatbot = gr.Chatbot() with gr.Row(): with gr.Column(scale=0.90): txt = gr.Textbox(show_label=False,placeholder="Patient") with gr.Column(scale=0.10): emptyBtn = gr.ClearButton([txt, chatbot]) # emptyBtn = gr.Button() with gr.Accordion("Conversational AI Analytics", open = False): with gr.Row(): with gr.Column(scale=1.0): txt4 =gr.Textbox( show_label=False, lines=4, placeholder="Summary") 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="📊") with gr.Row(): gr.HTML("""

Sentiment and Emotion Score Graph

""") with gr.Row(): with gr.Column(scale=1, min_width=0): plot =gr.Plot(label="Patient") with gr.Row(): with gr.Column(scale=1, min_width=0): plot_3 =gr.Plot(label="Patient_Emotion") # txt_msg = txt.submit(self._add_text, [chatbot, txt], [chatbot, txt]).then( # self._suggested_answer, [chatbot,txt],chatbot) # txt_msg.then(lambda: gr.update(interactive=True), None, [txt]) # txt.submit(self._suggested_answer, [chatbot,txt],chatbot) # button.click(self._agent_text, [chatbot,txt3], chatbot) txt.submit(self._suggested_answer, [txt,chatbot],[txt,chatbot]) print("chatbot", chatbot) end_btn.click(summarizer.generate_summary,chatbot, txt4) # emptyBtn.click(self.clear_func,[],[]) # emptyBtn.click(lambda: None, None, chatbot, queue=False) Sentiment_btn.click(self._on_sentiment_btn_click,chatbot,[plot,plot_3]) demo.title = "AI Mental Healthcare ChatBot" demo.launch(debug = True) document_qa =LangChain_Document_QA() document_qa.gradio_interface()