Duplicate from Syrahealthorg/HealthCare_workforce

.gitattributes

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README.md

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+---
+title: HealthCare Workforce
+emoji: 👁
+colorFrom: indigo
+colorTo: green
+sdk: gradio
+sdk_version: 3.35.2
+app_file: app.py
+pinned: false
+duplicated_from: Syrahealthorg/HealthCare_workforce
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+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=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()

patient_details.json

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+{
+  "patients": [
+    {
+      "id": "P001",
+      "name": "John Smith",
+      "age": 32,
+      "gender": "Male",
+      "contact": "+1 123-456-7890",
+      "address": "123 Main Street, City, Country",
+      "issue": {
+        "description": "The patient is experiencing symptoms of anxiety and panic attacks.",
+        "symptoms": ["restlessness", "shortness of breath", "heart palpitations"],
+        "diagnosis": "Panic Disorder",
+        "treatment": "Prescribed anti-anxiety medication and advised cognitive-behavioral therapy."
+      }
+    },
+    {
+      "id": "P002",
+      "name": "Sarah Johnson",
+      "age": 41,
+      "gender": "Female",
+      "contact": "+1 234-567-8901",
+      "address": "456 Elm Street, City, Country",
+      "issue": {
+        "description": "The patient is experiencing persistent sadness and loss of appetite.",
+        "symptoms": ["low mood", "fatigue", "weight loss"],
+        "diagnosis": "Major Depressive Disorder",
+        "treatment": "Prescribed antidepressant medication and recommended psychotherapy sessions."
+      }
+    },
+    {
+      "id": "P003",
+      "name": "Michael Williams",
+      "age": 27,
+      "gender": "Male",
+      "contact": "+1 345-678-9012",
+      "address": "789 Oak Avenue, City, Country",
+      "issue": {
+        "description": "The patient is experiencing difficulty concentrating and irritability.",
+        "symptoms": ["inability to focus", "mood swings", "agitation"],
+        "diagnosis": "Attention Deficit Hyperactivity Disorder (ADHD)",
+        "treatment": "Prescribed stimulant medication and advised behavioral therapy."
+      }
+    },
+    {
+      "id": "P004",
+      "name": "Emily Davis",
+      "age": 35,
+      "gender": "Female",
+      "contact": "+1 456-789-0123",
+      "address": "987 Maple Lane, City, Country",
+      "issue": {
+        "description": "The patient is experiencing intrusive thoughts and compulsive behaviors.",
+        "symptoms": ["obsessions", "rituals", "anxiety"],
+        "diagnosis": "Obsessive-Compulsive Disorder (OCD)",
+        "treatment": "Prescribed selective serotonin reuptake inhibitor (SSRI) medication and recommended exposure and response prevention therapy."
+      }
+    },
+    {
+      "id": "P005",
+      "name": "David Wilson",
+      "age": 48,
+      "gender": "Male",
+      "contact": "+1 567-890-1234",
+      "address": "321 Pine Street, City, Country",
+      "issue": {
+        "description": "The patient is experiencing mood swings and episodes of elevated energy.",
+        "symptoms": ["mania", "irritability", "decreased need for sleep"],
+        "diagnosis": "Bipolar Disorder",
+        "treatment": "Prescribed mood stabilizer medication and advised regular therapy sessions."
+      }
+    },
+    {
+      "id": "P006",
+      "name": "Olivia Thompson",
+      "age": 29,
+      "gender": "Female",
+      "contact": "+1 678-901-2345",
+      "address": "543 Cedar Road, City, Country",
+      "issue": {
+        "description": "The patient is experiencing social withdrawal and lack of interest in activities.",
+        "symptoms": ["isolation", "anhedonia", "low motivation"],
+        "diagnosis": "Major Depressive Disorder",
+        "treatment": "Prescribed antidepressant medication and recommended supportive counseling."
+      }
+    },
+    {
+      "id": "P007",
+      "name": "Daniel Anderson",
+      "age": 36,
+      "gender": "Male",
+      "contact": "+1 789-012-3456",
+      "address": "876 Oak Street, City, Country",
+      "issue": {
+        "description": "The patient is experiencing recurrent nightmares and flashbacks.",
+        "symptoms": ["nightmares", "hypervigilance", "emotional distress"],
+        "diagnosis": "Post-Traumatic Stress Disorder (PTSD)",
+        "treatment": "Prescribed selective serotonin reuptake inhibitor (SSRI) medication and advised trauma-focused therapy."
+      }
+    },
+    {
+      "id": "P008",
+      "name": "Sophia Martinez",
+      "age": 42,
+      "gender": "Female",
+      "contact": "+1 890-123-4567",
+      "address": "234 Elm Avenue, City, Country",
+      "issue": {
+        "description": "The patient is experiencing excessive worry and physical tension.",
+        "symptoms": ["chronic worrying", "muscle tension", "restlessness"],
+        "diagnosis": "Generalized Anxiety Disorder",
+        "treatment": "Prescribed anti-anxiety medication and advised relaxation techniques."
+      }
+    }
+  ]
+}
+

requirements.txt

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+gradio
+tensorflow
+transformers
+openai
+tiktoken
+langchain
+requests
+validators
+pytesseract
+tabulate
+nltk
+python-dotenv
+faiss-cpu
+plotly
+pandas
+plotly-express

style.css

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+.leftimage{
+  padding-top:75px;
+  margin-left:250px;
+}
+.rightimage{
+  margin-right:260px;
+  margin-top:15px;
+}
+
+.height{
+  height:60px;
+}