cleaned code

Server.py

@@ -66,7 +66,7 @@ def save_send_email(call,user_email,user_name):
 
               #path_pdf ="/content/drive/MyDrive/study/Projects/CodeBuddyAI/tmp.pdf"
               PDF_generator_Node(call['answer'],title,path_pdf)
-              EMAIL_sender_Node(user_email,email_txt,title,path_pdf)
+              #EMAIL_sender_Node(user_email,email_txt,title,path_pdf)
               import os
               os.remove(path_pdf)
               print("Done")

agent/agent_graph/Graph_Nodes.py

@@ -10,7 +10,6 @@ from agent.agent_graph.Graph_Utils import get_egp_to_usd
 from agent.rag.rag import *
 
 def answer_question(state:ProblemState):
-  print("Answering...")
   question = state["question"]
 
 
@@ -25,15 +24,11 @@ def answer_question(state:ProblemState):
       guide_prompt = Tasks_prompts.RAG.value
 
   
-  answer_prompt = ((guide_prompt + "طلب المستخدم:\n" + question + "\nاهم معلومات المستخدم لاستخدامها ف الدلالة (بالنسبة للسعر هو نفس السعر لكن بالدولار فدائما ركز على السعر بالدولار\n)"+ str(state)  + Route_prompts.FINALIZER_PROMPT.value))
 
-  print("ANSWER-PROMPT",answer_prompt)
 
   state["answer"] = get_llm_answer(model_llm=state["llm"],messages=state["memory"] + [HumanMessage(content=(guide_prompt + "طلب المستخدم:\n" + question + "\nاهم معلومات المستخدم لاستخدامها ف الدلالة (بالنسبة للسعر هو نفس السعر لكن بالدولار فدائما ركز على السعر بالدولار\n)"+ str(state) + Route_prompts.FINALIZER_PROMPT.value))])
 
-  print("Answer ",state["answer"])
-  print("STATE ",state)
-  print("----------------------------")
+
   return state
 
 
@@ -73,11 +68,9 @@ def update_context(state:ProblemState):
     # Make the prompt
     prompt_llm_new_info = Route_prompts.Context_UPDATOR.value + "\n <KEYS> \n" +str(keys_modifiable) +"\n </KEYS> <Text>"+state["question"]+"</Text>"
 
-    print("UPDATE - PROMPT",prompt_llm_new_info)
 
    
     llm_new_info = get_llm_answer(model_llm=state["llm"],messages = [HumanMessage(prompt_llm_new_info)])
-    print("ANSWER ", llm_new_info)
 
 
     # Save and Process the returned json to prevent hallucination
@@ -102,32 +95,24 @@ def update_context(state:ProblemState):
             "رجع فقط BOOL (True/False)"
         )
 
-        print("UPDATE 2 - PROMPT ",check_finalized_prompt)
         check_finalized = get_llm_answer(model_llm=state["llm"], messages = [HumanMessage(
             check_finalized_prompt
         )])
-        print("ANSWER ", check_finalized)
         state['all_ok'] =  check_finalized.strip().lower() == "true" # If wrong parsed it's false
-        print("Context updated")
 
 
 
     except Exception as e:
        print("Context was not updated due to error : ",e)
 
-    print("STATE ",state)
-    print("----------------------------")
 
     return state
 
 
 
 def convertPriceToDollar(state:ProblemState):
-    print("Converting price to dollar...")
     if "price" in state.keys():
       state["price"] = get_egp_to_usd(state["price"])
-      print("STATE ",state)
-      print("----------------------------")
 
     return state
 
@@ -142,22 +127,16 @@ def step(state:ProblemState):
         for i in task_steps[state.get("question_type")]:
             if i not in state.keys():
                 next_topic = i
-                print("Next topic ",next_topic)
                 break
     # Only after finishing the to do list of the question type we can ask for all_ok to confirm
     if (not next_topic) and ("all_ok" not in state.keys() or (state["all_ok"]==False))  and "question_type" in state.keys():
 
             next_topic = "all_ok"
-            print("Next topic ",next_topic)
 
 
 
     step_prompt = (System_prompts.STATE_DESCRIBE.value + f"<order>{next_topic} </order> <state>{state}</state>" + Route_prompts.FINALIZER_PROMPT_STEP.value)
-    print("STEP PROMPT",step_prompt)
     state['answer'] =  get_llm_answer(model_llm=state["llm"],messages = [HumanMessage(step_prompt)])
-    print("ANSWER ", state['answer'])
-    print("STATE ",state)
-    print("----------------------------")
 
     return state