add name field

bot/panda_bot.py

@@ -23,7 +23,7 @@ Patient Care Focus: As an assistant, Panda should regularly check in on the pati
 
 Patient Information:
 
-- Patient Name: '{username}'
+- Patient Name: '{name}'
 - Hemodialysis Schedule 1: {schedule_one}
 - Hemodialysis Schedule 2: {schedule_two}
 - Liquid Intake: {liquid_intake}
@@ -120,6 +120,7 @@ class CustomPromptTemplate(StringPromptTemplate):
     blood_pressure: str
     food_intake: str
     current_time: str
+    name: str
 
     def format(self, **kwargs) -> str:
         # Get the intermediate steps (AgentAction, Observation tuples)
@@ -146,6 +147,7 @@ class CustomPromptTemplate(StringPromptTemplate):
         kwargs["blood_pressure"] = self.blood_pressure
         kwargs["food_intake"] = self.food_intake
         kwargs["current_time"] = self.current_time
+        kwargs["name"] = self.name
 
         return self.template.format(**kwargs)
     
@@ -197,7 +199,7 @@ class PandaBot:
         # os.environ["LANGCHAIN_PROJECT"] = 'LLM Patient Monitoring'
 
 
-    def query(self, input_text: str, username: str, memory: any, schedule_one:any, schedule_two:any, liquid_intake:any, temperature:any, blood_pressure:any, food_intake:any, current_time:any) -> str:
+    def query(self, input_text: str, username: str, memory: any, schedule_one:any, schedule_two:any, liquid_intake:any, temperature:any, blood_pressure:any, food_intake:any, current_time:any, name:any) -> str:
         prompt = CustomPromptTemplate(
             input_variables=["input", "intermediate_steps", "chat_history"], 
             template=template, 
@@ -210,7 +212,8 @@ class PandaBot:
             temperature=temperature,
             blood_pressure=blood_pressure,
             food_intake=food_intake,
-            current_time=current_time)
+            current_time=current_time,
+            name=name)
         
         self.memory = memory
 

server.py

@@ -43,7 +43,7 @@ Output:
 #     llm.memory.chat_memory.add_ai_message(AIMessage(response))
 #     return jsonify({"response": translate})
 
-def simulate_llm_query(user_input, username, schedule_one, schedule_two, liquid_intake, temperature, blood_pressure, food_intake):
+def simulate_llm_query(user_input, username, schedule_one, schedule_two, liquid_intake, temperature, blood_pressure, food_intake, name):
     """
     Simulates querying a language model.
     Replace this function's logic with actual LLM querying.
@@ -53,7 +53,7 @@ def simulate_llm_query(user_input, username, schedule_one, schedule_two, liquid_
 
     now = datetime.now()
     current_time = now.strftime("%A, %I:%M %p")
-    return llm.query(user_input, username, memory, schedule_one, schedule_two, liquid_intake, temperature, blood_pressure, food_intake, current_time)
+    return llm.query(user_input, username, memory, schedule_one, schedule_two, liquid_intake, temperature, blood_pressure, food_intake, current_time, name)
 
 @app.route('/query', methods=['POST'])
 def query_llm():
@@ -66,13 +66,14 @@ def query_llm():
     temperature = str(data.get('temperature'))
     blood_pressure = str(data.get('blood_pressure'))
     food_intake = str(data.get('food_intake'))
+    name = str(data.get('name'))
 
     input_translate = GoogleTranslator(source='id', target='en').translate(user_input)
     
     if not user_input:
         return jsonify({"error": "No input provided"}), 400
     
-    response = simulate_llm_query(input_translate, username, schedule_one, schedule_two, liquid_intake, temperature, blood_pressure, food_intake)
+    response = simulate_llm_query(input_translate, username, schedule_one, schedule_two, liquid_intake, temperature, blood_pressure, food_intake, name)
 
     output_translate = GoogleTranslator(source='en', target='id').translate(response)