Upload requirements.txt

app.py

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+import gradio as gr
+from service import GPT_Service, Stage
+
+# Define the function to be called when the button is clicked
+def process_text(keywords, stage, medical_history):
+    if(keywords.strip()!='' and stage!=None and len(stage)!=0):
+        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)
+            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 pasteFunctionality():
+    print("Hello word")
+
+# Create text input components with labels
+input_text = gr.Textbox(lines=7, label="Sample keywords", placeholder="Enter sample keywords here")
+medical_history = gr.Textbox(lines=7, label="Medical History", placeholder="Enter medical history here")
+
+# Populating stage choices and creating dropdown component with label
+
+# To add all the stages
+# stage_choices = [stage.value for stage in Stage]
+
+# To add few stages
+stage_choices = [Stage.INITIAL_ASSESSMENT.value,Stage.FOLLOWUP_ASSESSMENT.value,Stage.DETAILED_EVALUATION.value,Stage.DISCHARGE_SUMMARY.value,Stage.LONG_TERM_GOALS.value]
+
+stage = gr.Dropdown(choices=stage_choices, label="Stage")
+
+# Create a button component to copy goals
+output_text_1 = gr.Textbox(lines=6, label="Variant 1", show_copy_button=True)
+output_text_2 = gr.Textbox(lines=6, label="Variant 2", show_copy_button=True)
+output_text_3 = gr.Textbox(lines=6, label="Variant 3", show_copy_button=True)
+
+
+# Create a Gradio interface
+gr.Interface(fn=process_text,
+             inputs=[input_text, stage, medical_history],
+             outputs=[output_text_1,output_text_2,output_text_3],
+             title="Medical Model Interface",
+             allow_flagging='never').launch()

requirements.txt

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+gradio
+python-dotenv

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"
+    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"""
+        Given we are in the {assessmentPhase} phase with a patient who has articulated experiences of {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)
+        variants = [value for value in data.values()]
+        # Fetch first three items from the array or pad with empty strings if not enough items are available
+        paddedVariants = variants[:3] + [''] * (3 - len(variants))
+        return paddedVariants
+
+    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 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)