Upload code (#1)

- Upload code (2e34456ea0ea3ff76c6a0bf3469f63c2a75f4a76)


Co-authored-by: Umar <umar-ts@users.noreply.huggingface.co>

.gitignore

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+.env

app.py

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+import gradio as gr
+from service import GPT_Service, Stage
+
+
+title_html = """
+<div style="display: flex; align-items: center; width=100%; background-color:#FFC90E;">
+    <img src='https://i.ibb.co/gPFskvg/LOGO-1.png' style='height: 90px; width:90px; margin-left: 20px;'>
+    <span style="margin-left: 20px;font-size:30px">
+        <span>Patient Assessment Documentation</span>
+    </span>
+</div>
+
+"""
+
+css = """
+#subButton, #rephraseBtn {
+    background-color: #EA580C !important;
+    background: #EA580C !important;
+    border: none !important; /* Remove the border */
+    transition: background-color 0.3s ease; /* Add transition for smoother hover effect */
+}
+
+#subButton:hover, #rephraseBtn:hover {
+    background-color: #FFC90E !important; /* Change to a lighter background color on hover */
+}
+
+"""
+
+def split_based_on_commas(string):
+    return [substring.strip() for substring in string.split(",")]
+
+def process_text(keywords, stage, medical_history):
+    if(keywords.strip()!='' and stage!=None and len(stage)!=0 and  len(split_based_on_commas(keywords))<=5):
+        try:
+            gptService=GPT_Service()
+            if stage == Stage.INITIAL_ASSESSMENT.value:
+                return gptService.getInitialAssessment(keywords,medical_history)
+            elif stage == Stage.FOLLOWUP_ASSESSMENT.value:
+                return gptService.getFollowUpAssessment(keywords,medical_history)
+            elif stage == Stage.EVALUATION.value:
+                return gptService.getEvaluation(keywords,medical_history)
+            elif stage == Stage.DETAILED_EVALUATION.value:
+                return gptService.getDetailedEvaluation(keywords,medical_history)
+            elif stage == Stage.PROGRESS_NOTE.value:
+                return gptService.getProgressNote(keywords,medical_history)
+            elif stage == Stage.DISCHARGE_SUMMARY.value:
+                return gptService.getDischargeSummary(keywords,medical_history)
+            elif stage == Stage.SHORT_TERM_GOALS.value:
+                return gptService.getShortTermGoals(keywords,medical_history)
+            elif stage == Stage.LONG_TERM_GOALS.value:
+                return gptService.getLongTermGoals(keywords,medical_history)
+            elif stage == Stage.RECOMMENDATION.value:
+                return gptService.getRecommendation(keywords,medical_history)
+            else:
+                print("Unknown stage")
+                raise gr.Error('Unknown stage encountered')
+        except:
+            print("GPT error encounters")
+            raise gr.Error('Something went wrong please try again')
+    else:
+        print("Unknown stage parameters")
+        raise gr.Error('Provide appropriate parameters and try again')
+
+
+def select_handler(prevResponse, newResponse):
+    if newResponse.strip() not in prevResponse:
+        return f"{prevResponse + (' ' if prevResponse.strip() != '' else '')} {newResponse}"
+    else:
+        return prevResponse
+
+
+def getRephrasedSentences(sentences):
+      if sentences.strip() == '':
+          raise gr.Error('Provide appropriate parameters and try again')
+      else:
+           try:
+                print(sentences)
+                gptService=GPT_Service()
+                return gptService.getRephrasedSentences(sentences)
+           except:
+                raise gr.Error('Something went wrong please try again')
+          
+
+with gr.Blocks(css=css) as demo:
+    gr.HTML(title_html)
+    gr.HTML("<h2>Patient Assessment Input</h2>")
+    with gr.Row():
+        keywords = gr.Textbox(label="Assessment Keywords", placeholder="Write comma separated sample keywords (max=5)")
+        medicalHistory = gr.Textbox(label="Medical History", placeholder="Enter patient history")
+        stage_choices = [Stage.INITIAL_ASSESSMENT.value,Stage.SHORT_TERM_GOALS.value,Stage.LONG_TERM_GOALS.value, Stage.RECOMMENDATION.value]
+        stage = gr.Dropdown(choices=stage_choices, label="Assessment Stage")
+    
+    with gr.Row():
+        clearInputFields = gr.ClearButton(value="Clear Inputs",components=[keywords,medicalHistory,stage])
+        responseButton = gr.Button("Submit",elem_id="subButton")
+
+    gr.HTML("<h2 style=\"margin-top:10\">Suggested Assessment Documentation</h2>")
+    with gr.Column():
+        label1 = gr.Markdown(value="Keyword 1",show_label=False)
+        with gr.Row() as firstKeywordResponses:
+            firstKeywordResponseUtterance1 = gr.Textbox(label="Variant 1",interactive=False, show_copy_button=True)
+            firstKeywordResponseUtterance2 = gr.Textbox(label="Variant 2", interactive=False, show_copy_button=True)
+            firstKeywordResponseUtterance3 = gr.Textbox(label="Variant 3", interactive=False, show_copy_button=True)
+
+        label2 = gr.Markdown(value="Keyword 2",show_label=False)
+        with gr.Row() as secondKeywordResponses:
+            secondKeywordResponseUtterance1 = gr.Textbox(label="Variant 1", interactive=False, show_copy_button=True)
+            secondKeywordResponseUtterance2 = gr.Textbox(label="Variant 2", interactive=False, show_copy_button=True)
+            secondKeywordResponseUtterance3 = gr.Textbox(label="Variant 3", interactive=False, show_copy_button=True)
+
+        label3 = gr.Markdown(value="Keyword 3",show_label=False)
+        with gr.Row() as thirdKeywordResponses:
+            thirdKeywordResponseUtterance1 = gr.Textbox(label="Variant 1", interactive=False, show_copy_button=True)
+            thirdKeywordResponseUtterance2 = gr.Textbox(label="Variant 2", interactive=False, show_copy_button=True)
+            thirdKeywordResponseUtterance3 = gr.Textbox(label="Variant 3", interactive=False, show_copy_button=True)
+
+        label4 = gr.Markdown(value="Keyword 4",show_label=False)
+        with gr.Row() as fourthKeywordResponses:
+            fourthKeywordResponseUtterance1 = gr.Textbox(label="Variant 1", interactive=False, show_copy_button=True)
+            fourthKeywordResponseUtterance2 = gr.Textbox(label="Variant 2", interactive=False, show_copy_button=True)
+            fourthKeywordResponseUtterance3 = gr.Textbox(label="Variant 3", interactive=False, show_copy_button=True)
+
+        label5 = gr.Markdown(value="Keyword 5",show_label=False)
+        with gr.Row() as fifthKeywordResponses:
+            fifthKeywordResponseUtterance1 = gr.Textbox(label="Variant 1", interactive=False, show_copy_button=True)
+            fifthKeywordResponseUtterance2 = gr.Textbox(label="Variant 2", interactive=False, show_copy_button=True)
+            fifthKeywordResponseUtterance3 = gr.Textbox(label="Variant 3", interactive=False, show_copy_button=True)
+
+    gr.HTML("<h2>Documentation Generator</h2>")
+    with gr.Row():
+        # clearSelectedSentences = gr.ClearButton(value="Clear Inputs",components=[selectedSentence])
+        with gr.Column():
+            selectedSentence=gr.Textbox(label="Selected Sentences", show_copy_button=True,lines=5)
+            rephraseButton = gr.Button("Generate Documentation",elem_id="rephraseBtn",)
+
+        rephrasedSentence=gr.Textbox(label="Patient Visit Documentation", interactive=False, show_copy_button=True,lines=7)
+
+
+    # First response event listeners
+    firstKeywordResponseUtterance1.select(select_handler,[selectedSentence,firstKeywordResponseUtterance1],selectedSentence)
+    firstKeywordResponseUtterance2.select(select_handler,[selectedSentence,firstKeywordResponseUtterance2],selectedSentence)
+    firstKeywordResponseUtterance3.select(select_handler,[selectedSentence,firstKeywordResponseUtterance3],selectedSentence)
+
+    # Second response event listeners
+    secondKeywordResponseUtterance1.select(select_handler,[selectedSentence,secondKeywordResponseUtterance1],selectedSentence)
+    secondKeywordResponseUtterance2.select(select_handler,[selectedSentence,secondKeywordResponseUtterance2],selectedSentence)
+    secondKeywordResponseUtterance3.select(select_handler,[selectedSentence,secondKeywordResponseUtterance3],selectedSentence)
+
+    # Third response event listeners
+    thirdKeywordResponseUtterance1.select(select_handler,[selectedSentence,thirdKeywordResponseUtterance1],selectedSentence)
+    thirdKeywordResponseUtterance2.select(select_handler,[selectedSentence,thirdKeywordResponseUtterance2],selectedSentence)
+    thirdKeywordResponseUtterance3.select(select_handler,[selectedSentence,thirdKeywordResponseUtterance3],selectedSentence)
+
+    # Fourth response event listeners
+    fourthKeywordResponseUtterance1.select(select_handler,[selectedSentence,fourthKeywordResponseUtterance1],selectedSentence)
+    fourthKeywordResponseUtterance2.select(select_handler,[selectedSentence,fourthKeywordResponseUtterance2],selectedSentence)
+    fourthKeywordResponseUtterance3.select(select_handler,[selectedSentence,fourthKeywordResponseUtterance3],selectedSentence)
+
+    # Fifth response event listeners
+    fifthKeywordResponseUtterance1.select(select_handler,[selectedSentence,fifthKeywordResponseUtterance1],selectedSentence)
+    fifthKeywordResponseUtterance2.select(select_handler,[selectedSentence,fifthKeywordResponseUtterance2],selectedSentence)
+    fifthKeywordResponseUtterance3.select(select_handler,[selectedSentence,fifthKeywordResponseUtterance3],selectedSentence)
+
+    responseButton.click(process_text,[keywords,stage,medicalHistory],[label1,firstKeywordResponseUtterance1,firstKeywordResponseUtterance2,firstKeywordResponseUtterance3,
+                                                                    label2,secondKeywordResponseUtterance1,secondKeywordResponseUtterance2,secondKeywordResponseUtterance3,
+                                                                    label3,thirdKeywordResponseUtterance1,thirdKeywordResponseUtterance2,thirdKeywordResponseUtterance3,
+                                                                    label4,fourthKeywordResponseUtterance1,fourthKeywordResponseUtterance2,fourthKeywordResponseUtterance3,
+                                                                    label5,fifthKeywordResponseUtterance1,fifthKeywordResponseUtterance2,fifthKeywordResponseUtterance3])
+    rephraseButton.click(getRephrasedSentences,selectedSentence,rephrasedSentence)
+    
+        
+demo.launch()

prompts.py

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+from enum import Enum
+
+class Prompts(Enum):
+    INITIAL_ASSESSMENT_PROMPT = """
+    "Act as a mental health professional conducting an 'Initial Assessment.' 
+    User will provide some keywords said by the patient and optionally, the patient's history. 
+    Respond to the query by considering these keywords and the patient's history (if provided). 
+    Your response will be crafted three precise variant sentences with numbering for each keyword 
+    separately by seamlessly integrating that specific mentioned emotional state provided by the patient, 
+    employing precise related medical terminology. Each keyword should be described separately in it's relevant 
+    sentence.The objective is to accurately document the patient's mental health status in clear, professional language. 
+    Your response should directly address the patient's condition without introductory or concluding comments, 
+    focusing solely on providing a professional yet comprehensible account of the symptoms reported. 
+    Ensure that the use of medical terms does not compromise the overall accessibility of the text to 
+    those outside the medical field. Strictly return the below JSON object in response; it must not be a 
+    JSON string. Use the following structure for your response:   
+    {\"name of keyword1\" : {\"variant1\": \"...\", \"variant2\": \"...\", \"variant3\": \"...\"}, {\"name of keyword2\" : 
+    {\"variant1\": \"...\", \"variant2\": \"...\", \"variant3\": \"...\"}, ........}. Don't add any additional ',\n' 
+    in the JSON object."
+    """
+
+    RECOMMENDATION_PROMPT = """
+    Act as a mental health professional and provide recommendations to patients. User will provide some keywords 
+    and optionally, the patient's history. Respond to the query by considering these keywords and the patient's history 
+    (if provided). Your response will be crafted three precise variant sentences with numbering for each keyword separately 
+    by seamlessly outlining the  recommendations based on the provided keyword of user and patient history(if provided). 
+    Each keyword should be described separately in it's relevant sentence.The objective is to document the recommendation 
+    of the doctor for the patient based provided kerword and patient history(if provided).Your response should focus solely 
+    on providing a professional yet comprehensible account of doctor recommendations. Strictly return the below JSON object 
+    in response; it must not be a JSON string. Use the following structure for your response:   
+    {\"name of keyword1\" : {\"variant1\": \"...\", \"variant2\": \"...\", \"variant3\": \"...\"}, {\"name of keyword2\" : 
+    {\"variant1\": \"...\", \"variant2\": \"...\", \"variant3\": \"...\"}, ........}. Don't add any additional ',\n' 
+    in the JSON object.
+    """
+    
+    SHORT_TERM_GOALS_PROMPT = """
+   Act as a mental health professional conducting the second last step of defining 'Short-Term Goals'. 
+   User will provide some keywords and optionally, the patient's history. Respond to the query by considering 
+   these keywords and the patient's history (if provided). Your response will be crafted three precise variant 
+   sentences with numbering for each keyword searately by seamlessly outlining the short term goals based on the 
+   provided keyword and patient history. Each keyword should be described separately in it's relevant sentence. 
+   The objective is to document the short term goals of  the patient. Your response should focus solely on providing 
+   a professional yet comprehensible account of patient short term goals. Strictly return the below JSON object in response; 
+   it must not be a JSON string. Use the following structure for your response:   
+   {\"name of keyword1\" : {\"variant1\": \"...\", \"variant2\": \"...\", \"variant3\": \"...\"}, {\"name of keyword2\" : 
+   {\"variant1\": \"...\", \"variant2\": \"...\", \"variant3\": \"...\"}, ........}. Don't add any additional ',\n' 
+   in the JSON object.
+    """
+    
+    LONG_TERM_GOALS_PROMPT = """
+    Act as a mental health professional conducting the second last step of defining 
+    'Long-Term Goals'.User will provide some keywords and optionally, the patient's history. 
+    Respond to the query by considering these keywords and the patient's history (if provided). 
+    Your response will be crafted three precise variant sentences with numbering for each keyword 
+    separately by seamlessly outlining the Long term goals based on the provided keyword and patient history. 
+    Each keyword should be described separately in it's relevant sentence.The objective is to document the 
+    Long term goals of  the patient. Your response should focus solely on providing a professional yet comprehensible 
+    account of patient short term goals. Strictly return the below JSON object in response; it must not be a JSON string. 
+    Use the following structure for your response:   
+    {\"name of keyword1\" : {\"variant1\": \"...\", \"variant2\": \"...\", \"variant3\": \"...\"}, 
+    {\"name of keyword2\" : {\"variant1\": \"...\", \"variant2\": \"...\", \"variant3\": \"...\"}, ........}. 
+    Don't add any additional ',\n' in the JSON object.
+    """
+
+    REPHRASE_PROMPT = """
+    Act as mental Health care provider.Given the following sentences about healthcare patient treatment steps, rephrase 
+    and combine them into a clear, coherent, and easily understandable paragraph. Aim to improve readability and ensure 
+    that the essential information and sequence of steps remain accurate and straightforward. Additionally, please adjust 
+    the tone to be supportive, reassuring for patients and caregivers reading this information. Furthermore,Your response 
+    must use 'patient' instead of personal pronouns like 'you' to refer to the individual receiving care.
+    """
+
+    # Not added stages prompts
+
+
+    FOLLOWUP_ASSESSMENT_PROMPT = """
+    Act as a mental health professional conducting an 'Follow-up Assessment'. User will provide 
+    some keywords that reveals the patient's progress since the last evaluation  any ongoing issues or 
+    changes in symptoms  and also  user can provide  patient's history. Respond to the query by considering 
+    these keywords and the patient's history (if provided). Your response will be crafted three precise variant 
+    paragraphs with numbering by seamlessly integrating all mentioned emotional states provided by the patient, 
+    employing precise medical terminology and patient's current status based on their previous assessment, patient history,  
+    highlighting any improvements, setbacks, or new developments. The objective is to accurately document the patient's 
+    mental health status in clear, professional language at stage of Follow-up Assessment. Your response should directly 
+    address the patient's condition without introductory or concluding comments, focusing solely on providing a professional 
+    yet comprehensible account of the symptoms reported. Ensure that the use of medical terms does not compromise the overall 
+    accessibility of the text to those outside the medical field. Strictly return the below JSON object in response; it must 
+    not be a JSON string. Use the following structure for your response:   
+    {\"variant1\": \"...\", \"variant2\": \"...\", \"variant3\": \"...\"}. 
+    Don't add any additional ',\n' in the JSON object.
+    """
+    
+    DETAILED_EVALUATION_PROMPT = """
+    Act as a mental health professional conducting an 'detailed evaluation.' User will provide some keywords said 
+    by the patient and optionally, the patient's history. Respond to the query by considering these keywords and 
+    the patient's history (if provided). Your response will be crafted three precise variant paragraphs with numbering 
+    by seamlessly  writing a detailed paragraph outlining the findings of your evaluation, including observations, 
+    diagnostic considerations, and preliminary treatment recommendations. The objective is to accurately document 
+    the patient's mental health status in clear, professional language. Your response should directly address the 
+    patient's condition without introductory or concluding comments, focusing solely on providing a professional yet 
+    comprehensible account of patient detail evaluation. Ensure that the use of medical terms does not compromise the 
+    overall accessibility of the text to those outside the medical field. Strictly return the below JSON object in response; 
+    it must not be a JSON string. Use the following structure for your response:   
+    {\"variant1\": \"...\", \"variant2\": \"...\", \"variant3\": \"...\"}. 
+    Don't add any additional ',\n' in the JSON object."
+    """
+    
+    DISCHARGE_SUMMARY_PROMPT = """
+    Act as a mental health professional conducting the 'Discharge Summary' of mental health patient. 
+    This summary should  provides a comprehensive overview of the patient's mental health journey, treatment outcomes, 
+    and future recommendations. User will provide some keywords and optionally, the patient's history. Respond to the 
+    query by considering these keywords and the patient's history (if provided). Your response will be  crafted three 
+    detailed variant paragraphs of Discharge summery, summarizing the patient's treatment progress, including improvements, 
+    relapses, or unresolved issues. Outline post-discharge plans and recommendations for continued care or follow-up. 
+    Also during crafting consider integrating all mentioned keywords of user and  use  precise medical terminology. 
+    The objective is to accurately document the patient's Discharge Summary status in clear, professional language. 
+    Your response should directly address the patient's  Discharge Summary without introductory or concluding comments, 
+    focusing solely on providing a professional yet comprehensible account of the symptoms reported. Ensure that the use of 
+    medical terms does not compromise the overall accessibility of the text to those outside the medical field. 
+    Strictly return the below JSON object in response; it must not be a JSON string. Use the following structure for your 
+    response: {\"variant1\": \"...\", \"variant2\": \"...\", \"variant3\": \"...\"}. Don't add any additional ',\n' in the JSON object.
+    """
+
+    PROGRESS_NOTE_PROMPT = """
+    Act as a mental health professional conducting an 'Progress Note' 
+    User will provide some keywords said by the patient and optionally, 
+    the patient's history. Respond to the query by considering these keywords and the patient's history (if provided). 
+    Your response will be crafted three precise variant paragraphs with numbering by seamlessly integrating all mentioned 
+    emotional states provided by the patient, employing precise medical terminology. The objective is to accurately document 
+    the patient's mental health status in clear, professional language. Your response should directly address the patient's 
+    condition without introductory or concluding comments, focusing solely on providing a professional yet comprehensible 
+    account of the symptoms reported. Ensure that the use of medical terms does not compromise the overall accessibility 
+    of the text to those outside the medical field. Strictly return the below JSON object in response; it must 
+    not be a JSON string. Use the following structure for your response: 
+    {\"variant1\": \"...\", \"variant2\": \"...\", \"variant3\": \"...\"}. 
+    Don't add any additional ',\n' in the JSON object.
+    """
+
+    EVALUATION_PROMPT = """
+    Act as a mental health professional conducting an 'Evaluation' 
+    User will provide some keywords said by the patient and optionally, 
+    the patient's history. Respond to the query by considering these keywords and the patient's history (if provided). 
+    Your response will be crafted three precise variant paragraphs with numbering by seamlessly integrating all mentioned 
+    emotional states provided by the patient, employing precise medical terminology. The objective is to accurately document 
+    the patient's mental health status in clear, professional language. Your response should directly address the patient's 
+    condition without introductory or concluding comments, focusing solely on providing a professional yet comprehensible 
+    account of the symptoms reported. Ensure that the use of medical terms does not compromise the overall accessibility 
+    of the text to those outside the medical field. Strictly return the below JSON object in response; it must 
+    not be a JSON string. Use the following structure for your response: 
+    {\"variant1\": \"...\", \"variant2\": \"...\", \"variant3\": \"...\"}. 
+    Don't add any additional ',\n' in the JSON object.
+    """

service.py

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+import os
+import requests
+from enum import Enum
+from dotenv import load_dotenv
+from prompts import Prompts
+import json
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Access environment variables
+API_KEY = os.environ['CHAT_GPT_KEY']
+
+class Stage(Enum):
+    INITIAL_ASSESSMENT = "Initial Assessment"
+    FOLLOWUP_ASSESSMENT = "Follow Up Assessment"
+    EVALUATION = "Evaluation"
+    DETAILED_EVALUATION = "Detailed Evaluation"
+    PROGRESS_NOTE = "Progress Note"
+    RECOMMENDATION="Recommendations"
+    DISCHARGE_SUMMARY = "Discharge Summary"
+    SHORT_TERM_GOALS = "Short Term Goals"
+    LONG_TERM_GOALS = "Long Term Goals"
+
+
+class GPT_Service:
+    def __init__(self):
+        self.api_key = API_KEY
+        self.base_url = 'https://api.openai.com/v1/chat/completions'
+
+    def request_gpt_for_response(self, system_prompt, userPrompt):
+        headers = {
+            'Content-Type': 'application/json',
+            'Authorization': f'Bearer {self.api_key}'
+        }
+        data = {
+            'model': 'gpt-3.5-turbo-1106',
+            'temperature': 0.3,
+            'messages': [
+                {
+                    'role': 'system',
+                    'content': system_prompt
+                },
+                {
+                    'role': 'user',
+                    'content': userPrompt
+                }
+            ]
+        }
+        try:
+            response = requests.post(
+                self.base_url,
+                json=data,
+                headers=headers,
+            )
+            response.raise_for_status()
+            return response.json()['choices'][0]['message']['content']
+        except requests.exceptions.RequestException as e:
+            raise ValueError(f"Error: {e}")
+    
+    def formulateUserPrompt(self, assessmentPhase, keywords, medicalHistory):
+        patientData= f"""
+        Keywords: {keywords}.
+        {f"Patient has the following medical history: {medicalHistory}" if medicalHistory is not None and medicalHistory != ""
+        else ""}
+        """
+        print(patientData)
+        return patientData
+    
+    def processOutputResults(self, jsonOutput):
+        preprocessed_json_string = jsonOutput.replace('\n', '')
+        data = json.loads(preprocessed_json_string)
+        dataLength=len(data.keys())
+        result_list = []
+        
+        for key, value in data.items():
+            result_list.append(key)
+            for variant, sentence in value.items():
+                result_list.append(sentence)
+                
+        if dataLength < 5:
+            additional_keys_needed = 5 - dataLength
+            for key in range(additional_keys_needed):
+                result_list.append(f"Keyword {dataLength+key+1}")  # Empty key
+                for _ in range(3):
+                    result_list.append("")  # Empty sentence
+            return result_list
+
+        return result_list
+
+    def getInitialAssessment(self, keywords,medicalHistory):
+        userPrompt= self.formulateUserPrompt(Stage.INITIAL_ASSESSMENT.value,keywords,medicalHistory)
+        response= self.request_gpt_for_response(Prompts.INITIAL_ASSESSMENT_PROMPT.value,userPrompt)
+        return self.processOutputResults(response)
+
+    def getFollowUpAssessment(self, keywords, medicalHistory):
+        userPrompt= self.formulateUserPrompt(Stage.FOLLOWUP_ASSESSMENT.value,keywords,medicalHistory)
+        response= self.request_gpt_for_response(Prompts.FOLLOWUP_ASSESSMENT_PROMPT.value,userPrompt)
+        return self.processOutputResults(response)
+
+    def getEvaluation(self, keywords, medicalHistory):
+        userPrompt= self.formulateUserPrompt(Stage.EVALUATION.value,keywords,medicalHistory)
+        response= self.request_gpt_for_response(Prompts.EVALUATION_PROMPT.value,userPrompt)
+        return self.processOutputResults(response)
+    
+    def getRecommendation(self, keywords, medicalHistory):
+        userPrompt= self.formulateUserPrompt(Stage.RECOMMENDATION.value,keywords,medicalHistory)
+        response= self.request_gpt_for_response(Prompts.RECOMMENDATION_PROMPT.value,userPrompt)
+        return self.processOutputResults(response)
+
+    def getDetailedEvaluation(self, keywords,medicalHistory):
+        userPrompt= self.formulateUserPrompt(Stage.DETAILED_EVALUATION.value,keywords,medicalHistory)
+        response= self.request_gpt_for_response(Prompts.DETAILED_EVALUATION_PROMPT.value,userPrompt)
+        return self.processOutputResults(response)
+
+    def getProgressNote(self, keywords,medicalHistory):
+        userPrompt= self.formulateUserPrompt(Stage.PROGRESS_NOTE.value,keywords,medicalHistory)
+        response = self.request_gpt_for_response(Prompts.PROGRESS_NOTE_PROMPT.value,userPrompt)
+        return self.processOutputResults(response)
+
+    def getDischargeSummary(self, keywords,medicalHistory):
+        userPrompt= self.formulateUserPrompt(Stage.DISCHARGE_SUMMARY.value,keywords,medicalHistory)
+        response = self.request_gpt_for_response(Prompts.DISCHARGE_SUMMARY_PROMPT.value,userPrompt)
+        return self.processOutputResults(response)
+    
+    def getShortTermGoals(self, keywords,medicalHistory):
+        userPrompt= self.formulateUserPrompt(Stage.SHORT_TERM_GOALS.value,keywords,medicalHistory)
+        response =  self.request_gpt_for_response(Prompts.SHORT_TERM_GOALS_PROMPT.value,userPrompt)
+        return self.processOutputResults(response)
+    
+    def getLongTermGoals(self, keywords,medicalHistory):
+        userPrompt= self.formulateUserPrompt(Stage.LONG_TERM_GOALS.value,keywords,medicalHistory)
+        response = self.request_gpt_for_response(Prompts.LONG_TERM_GOALS_PROMPT.value,userPrompt)
+        return self.processOutputResults(response)
+    
+    def getRephrasedSentences(self, sentences):
+        response = self.request_gpt_for_response(Prompts.REPHRASE_PROMPT.value,sentences)
+        return response