app.py

import time
import json
import gradio as gr
from openai import OpenAI

OPENAI_API_KEY = "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"
client = OpenAI(api_key=OPENAI_API_KEY)
OPEN_AI_MODEL = "gpt-4-1106-preview"

thread = client.beta.threads.create()
thread_id = thread.id

def wait_on_run(run, thread):
    while run.status == "queued" or run.status == "in_progress":
        run = client.beta.threads.runs.retrieve(
            thread_id=thread.id,
            run_id=run.id,
        )
        time.sleep(0.5)
    return run

def get_openai_assistant(assistant_name, instructions, model=OPEN_AI_MODEL):
  assistant = client.beta.assistants.create(
      name=assistant_name,
      instructions=instructions,
      model=model,
  )
  return assistant.id


def show_json(obj):
    json.loads(obj.model_dump_json())


def abstract_assistant(pre_context_to_the_instruction, instruction, name, thread_id, thread, query):
  assistant_id = get_openai_assistant(name, pre_context_to_the_instruction + instruction)
  message = client.beta.threads.messages.create(
      thread_id=thread_id, role="user", content=query)
  run = client.beta.threads.runs.create(
      thread_id=thread_id,
      assistant_id=assistant_id,
  )
  wait_on_run(run, thread)
  messages = client.beta.threads.messages.list(
      thread_id=thread_id, order="asc", after=message.id
  )
  data = json.loads(messages.model_dump_json())
  response = data['data'][0]['content'][0]['text']['value']

  return response

def get_response_for_case_t0(thread_id, query, question_text, input, output, example):

  pre_context_to_the_instruction = f"""```QUESTION:{question_text}```\n```INPUT:{input}```\n```OUTPUT:{output}```\n```EXAMPLE:{example}```"""
  instruction = f"""Act as a Coding Mentor for a student who is currently learning Data Structures and algorithms(DSA). The student is asking a QUERY: {query} based on the
  coding question : QUESTION which have input : INPUT output: OUTPUT and examples: EXAMPLE. The student have not implemented any code. The student might be asking
  how to solve the question or what should be the coding approach or how to write the code/logic. Your task is to :
  1.) Ask the student about what he/she is thinking about the problem statement or what logic the student will implement or what approach he/she will follow
  to code the solution he/she is thinking.
  2.) Please be in a motivational tone and never give student a solution approach just ask the students approach.
  3.) Always answer in short and crunch way not more than 200 words. Always be to the point."""

  response = abstract_assistant(pre_context_to_the_instruction,instruction, "GPT_t0", thread_id, thread, query)

  return response


def opening_statement(thread_id, question_text, input, output, example):
  pre_context_to_the_instruction = f"""```QUESTION:{question_text}```\n```INPUT:{input}```\n```OUTPUT:{output}```\n```EXAMPLE:{example}```"""
  instruction = f"""Act as a Coding Mentor for a student who is currently learning Data Structures and algorithms(DSA). Now the student
  is stuck in the question for long amount of time so Ask him in a gentle motivational tone to tell or to start think where
  he/she might be stuck or might be thinking. Output only one line not more than that be short and crunch!"""

  query = ""

  response = abstract_assistant(pre_context_to_the_instruction,instruction, "GPT_open", thread_id, thread, query)

  return response


def response_evaluation_for_case_tx(thread_id, query, question_text, input, output, example, user_code):
  pre_context_to_the_instruction = f"""```QUESTION:{question_text}```\n```INPUT:{input}```\n```OUTPUT:{output}```\n```EXAMPLE:{example}```"""
  instruction = f"""Act as a Coding Mentor for a student who is currently learning Data Structures and algorithms(DSA). The student is asking a QUERY: {query} based on the
  coding question : QUESTION which have input : INPUT output: OUTPUT and examples: EXAMPLE. Now follow the following instrutions:
  NEVER PROVIDE COMPLETE SOLUTION CODE AND ALL THE STEPS TO SOLVE IN ONE RESPONSE. BE SELECTIVE AND GIVE IMMEDIATE STEP ONLY ONE
  * Always answer in short and crunch way not more than 200 words. Always be to the point
  1.) Understand what user is thinking about to code.
  2.) Take time to think and understand
  3.) Analyse the Code written by the user : {user_code}
  4.) Again take time to think and understand
  5.) Now check if the explaination of the user is aligning with the code or not
  6.) If not then suggest the user to align, by providing unblocking hints only!
  7.) Never give complete solution logic or code to the student , never ! Always talk with the student let the student write code with small hints only and reach to a solution
  8.) If user is doing wrong approach suggest correct approach slowly with step by st\ep hints only!
  9.) At the end also suggest user about how to improve code and logic at the end"""

  response = abstract_assistant(pre_context_to_the_instruction,instruction, "GPT_tx", thread_id, thread, query)

  return response


def run_chat_in_all_cases(message, history, question_text,input, output, examples, code_written, thread_id=thread.id):

  if not message and not code_written:
    ai_message = opening_statement(thread_id, question_text, input, output, examples)
  if not code_written:
    ai_message = get_response_for_case_t0(thread_id, message, question_text, input, output, examples)
  else:
    ai_message = response_evaluation_for_case_tx(thread_id, message, question_text, input, output, examples, code_written)

  return ai_message

additional_inputs=[
    gr.Textbox(
        label="Question Text",
        max_lines=10,
        interactive=True,
    ),
     gr.Textbox(
        label="Input",
        max_lines=10,
        interactive=True,
    ),
    gr.Textbox(
        label="Output",
        max_lines=10,
        interactive=True,
    ),
    gr.Textbox(
        label="Examples",
        max_lines=10,
        interactive=True,
    ),
    gr.Code(
        label="Code",
        interactive=True,
    )
]

gr.ChatInterface(
    fn=run_chat_in_all_cases,
    chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
    additional_inputs=additional_inputs,
    title="Mentor Mode",
    concurrency_limit=20,
).launch(show_api=False)