app.py

import random
import gradio as gr
import openai
import os
import re

openai.api_key = os.environ.get("open_ai_key")

prompt = ['''
You are a ''', 
'''
machine learning developer, trying to debug this code:

StackTrace: 

    Traceback (most recent call last):
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/multiprocessing/process.py”, line 258, in _bootstrap
    self.run()
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/multiprocessing/process.py”, line 93, in run
    self._target(*self._args, **self._kwargs)
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/site-packages/torch/utils/data/dataloader.py”, line 61, in _worker_loop
    data_queue.put((idx, samples))
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/multiprocessing/queues.py”, line 341, in put
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/multiprocessing/reduction.py”, line 51, in dumps
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/site-packages/torch/multiprocessing/reductions.py”, line 121, in reduce_storage
    RuntimeError: unable to open shared memory object </torch_54163_3383444026> in read-write mode at /opt/conda/conda-bld/pytorch_1525909934016/work/aten/src/TH/THAllocator.c:342

    During handling of the above exception, another exception occurred:

    Traceback (most recent call last):
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/multiprocessing/util.py”, line 262, in _run_finalizers
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/multiprocessing/util.py”, line 186, in call
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/shutil.py”, line 476, in rmtree
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/shutil.py”, line 474, in rmtree
    OSError: [Errno 24] Too many open files: ‘/tmp/pymp-sgew4xdn’
    Process Process-1:
    Traceback (most recent call last):
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/multiprocessing/process.py”, line 258, in _bootstrap
    self.run()
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/multiprocessing/process.py”, line 93, in run
    self._target(*self._args, **self._kwargs)
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/site-packages/torch/utils/data/dataloader.py”, line 61, in _worker_loop
    data_queue.put((idx, samples))
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/multiprocessing/queues.py”, line 341, in put
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/multiprocessing/reduction.py”, line 51, in dumps
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/site-packages/torch/multiprocessing/reductions.py”, line 121, in reduce_storage
    RuntimeError: unable to open shared memory object </torch_54163_3383444026> in read-write mode at /opt/conda/conda-bld/pytorch_1525909934016/work/aten/src/TH/THAllocator.c:342
    Traceback (most recent call last):
    File “/home/nlpgpu3/LinoHong/FakeNewsByTitle/main.py”, line 25, in
    for mini_batch in trainloader :
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/site-packages/torch/utils/data/dataloader.py”, line 280, in next
    idx, batch = self._get_batch()
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/site-packages/torch/utils/data/dataloader.py”, line 259, in _get_batch
    return self.data_queue.get()
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/multiprocessing/queues.py”, line 335, in get
    res = self._reader.recv_bytes()
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/multiprocessing/connection.py”, line 216, in recv_bytes
    buf = self._recv_bytes(maxlength)
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/multiprocessing/connection.py”, line 407, in _recv_bytes
    buf = self._recv(4)
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/multiprocessing/connection.py”, line 379, in _recv
    chunk = read(handle, remaining)
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/site-packages/torch/utils/data/dataloader.py”, line 178, in handler
    _error_if_any_worker_fails()
    RuntimeError: DataLoader worker (pid 54163) exited unexpectedly with exit code 1.

    Process finished with exit code 1

Question: Any idea how I can solve this problem?
Are follow up questions needed here: Yes
Follow up: Does your code run with less num_workers or num_workers=0? 
Intermediate Answer: It worked when I set num_workers equals to 0, but doesn’t work greater or equal to 1
Follow up: Could you try to increase the shared memory and try setting num_workers>0 again?
Intermediate Answer: It worked! Can you explain what happened here? 
So the final answer is: The error usually means that your system doesn’t provide enough shared memory for multiple workers (used via num_workers>0). Check the shared memory limitation of your system and try to increase it.

StackTrace: 

Traceback (most recent call last):
  File "main.py", line 39, in <module>
    request = create_request(page)
  File "main.py", line 15, in create_request
    url = base_url + data
TypeError: can only concatenate str (not "bytes") to str


Question: How do I fix this? 
Are follow up questions needed here: Yes
Follow up: Could you try to decode the data before passing it to the url? 
Intermediate Answer: Yes, it made the data a string and worked!
So the final answer is: You can try to decode the data before passing it to the url like this: 

data = urllib.parse.unquote(data)

StackTrace: ''',
'''
Question: ''', 
'''
Are follow up questions needed here:''',]



def extract_answer(generated):
    if '\n' not in generated:
        last_line =  generated
    else: 
        last_line = generated.split('\n')[-1]

    if ':' not in last_line:
        after_colon = last_line
    else:
        after_colon = generated.split(':')[-1]
    
    if ' ' == after_colon[0]:
        after_colon = after_colon[1:]
    if '.' == after_colon[-1]:
        after_colon = after_colon[:-1]

    return after_colon

def extract_question(generated):
    if '\n' not in generated:
        last_line =  generated
    else: 
        last_line = generated.split('\n')[-1]

    if 'Follow up:' not in last_line:
      print('we probably should never get here...' + generated)

    if ':' not in last_line:
        after_colon = last_line
    else:
        after_colon = generated.split(':')[-1]
    
    if ' ' == after_colon[0]:
        after_colon = after_colon[1:]
    if '?' != after_colon[-1]:
        print('we probably should never get here...' + generated)

    return after_colon

def get_last_line(generated):
    if '\n' not in generated:
        last_line =  generated
    else: 
        last_line = generated.split('\n')[-1]


    return last_line
  
def greenify(input):
  return "\x1b[102m" + input + "\x1b[0m"

def yellowfy(input):
  return "\x1b[106m" + input + "\x1b[0m"

def call_gpt(cur_prompt, stop):
    ans = openai.Completion.create(
                model="text-davinci-002",
                max_tokens=256,
                stop=stop,
                prompt=cur_prompt,
                temperature=0.7,
                top_p=1,
                frequency_penalty=0,
                presence_penalty=0
              )  
    returned = ans['choices'][0]['text']
    print( greenify(returned), end='')
    return returned

def initial_query_builder(language, code, question, intermediate = "\nIntermediate Answer:", followup = "\nFollow up:", finalans= '\nSo the final answer is:'):
  cur_prompt = prompt[0] +  language + prompt[1] + code + prompt[2] + question + prompt[3]

  # print("prompt: ", cur_prompt, end ='')
  ## check if follow up in the query, if not, make sure it contains the final answer. otherwise re-run until at least one of the 2 is in the response. break after 3 attempts. 
  attempts = 0
  ret_text = ''
  while followup not in ret_text and finalans not in ret_text:
    attempts +=1 
    ret_text = call_gpt(cur_prompt, intermediate)
    print(str(attempts) + " ret_text:", ret_text)
    if attempts == 3: 
      break
  if "final answer is" in ret_text: 
    updated_prompt = cur_prompt + re.findall(r".*?(?=is:)", ret_text)[0] + " is: Let's think step-by-step. "
    ret_text = call_gpt(updated_prompt, intermediate)
  return ret_text

def subsequent_query_builder(curr_prompt, external_answer, intermediate = "\nIntermediate Answer:", followup = "\nFollow up:", finalans= '\nSo the final answer is:'):
  print("curr_prompt: ", curr_prompt)
  curr_prompt += intermediate + ' ' + external_answer + '.'
   ## check if follow up in the query, if not, make sure it contains the final answer. otherwise re-run until at least one of the 2 is in the response. break after 3 attempts. 
  attempts = 0
  ret_text = ''
  while followup not in ret_text and finalans not in ret_text:
    attempts +=1 
    ret_text = call_gpt(curr_prompt, intermediate)
    print("subsequent query " + str(attempts) + " ret_text:", ret_text)
    if attempts == 3: 
      break
  print("ret_text: ", ret_text)
  if "final answer is" in ret_text: 
    updated_prompt = curr_prompt + re.findall(r".*?(?=is:)", ret_text)[0] + " is: Let's think step-by-step. "
    # print("updated_prompt: ", updated_prompt)
    ret_text = call_gpt(updated_prompt, intermediate)
  return ret_text

"""subsequent query builder: 

the way to rebuild the prompt for each subsequent call: 

1. every user response is 'intermediate answer' 
2. until you hit 'so the final answer is: ' you're good
3. 
"""

def prompt_builder(history, intermediate = "\nIntermediate Answer:", followup = "\nFollow up:", finalans= '\nSo the final answer is:'): 
  #set language 
  language = history[1][0]
  #set stack trace 
  stacktrace = history[0][0]
  #set question (hardcoded)
  question = "Any idea how I can solve this problem?"

  # initial prompt
  curr_prompt = prompt[0] +  language + prompt[1] + stacktrace + prompt[2] + question + prompt[3]

  #set subsequent conversation thread
  if len(history) >= 2: #subsequent conversations have occurred 
    curr_prompt += history[1][1] ## get the first response to the stacktrace prompt
    for conversation in history[2:]:
      #grab intermediate answer
      curr_prompt += intermediate + ' ' + conversation[0] + '.'
      #grab the follow up
      curr_prompt += conversation[1]
  return curr_prompt

def chat(message, history):
    history = history or []
    print(len(history))
    if len(history) == 0: ## just the stacktrace
      response = "which language is this in? (python, java, c++, kotlin, etc.)"
    elif len(history) == 1: ## stacktrace + just entered the language 
      # get stacktrace
      stacktrace = history[0][0]
      # get language
      language = message
      # set question (hardcoded for v1)
      question = "Any idea how I can solve this problem?"
      response = initial_query_builder(language, stacktrace, question)
    else: # subsequent prompts
      # get stacktrace
      stacktrace = history[0][0]
      # get language
      language = history[1][0]
      # set question (hardcoded for v1)
      question = "Any idea how I can solve this problem?"
      curr_prompt = prompt_builder(history)
      response = subsequent_query_builder(curr_prompt, message)
      # response = query_builder(language, stacktrace, question)
      print("response: ", response)
    history.append((message, response))
    return history, history
    
def clear(arg): 
  return "Enter your response - feel free to elaborate further, ask questions, etc." 

with gr.Blocks() as demo:
    user_state=gr.State([])
    gr.Markdown("""# StackTrace QA Bot""")
    with gr.Row():
      with gr.Column():
        inp = gr.Textbox(placeholder="enter your stacktrace here")
        btn = gr.Button("Enter message")
        output = gr.Chatbot().style(color_map=("green", "pink"))
        # allow_flagging="never",
        inp.submit(chat, [inp, user_state], [output, user_state])
        inp.submit(clear, inp, inp)
        btn.click(chat, [inp, user_state], [output, user_state])
        btn.click(clear, inp, inp)
    gr.Markdown("""### need help? got feedback? have thoughts? etc. ➜ Join the [Discord](https://discord.gg/KvG3azf39U)""")
    gr.Examples(examples=['''PYTORCH: ---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
/var/folders/49/9g9lxm9d3f3br8zlg2l2fmz80000gn/T/ipykernel_1349/2634282627.py in <module>
----> 1 torch.onnx.export(model, x, "output.onnx")

/opt/anaconda3/lib/python3.9/site-packages/torch/onnx/utils.py in export(model, args, f, export_params, verbose, training, input_names, output_names, operator_export_type, opset_version, do_constant_folding, dynamic_axes, keep_initializers_as_inputs, custom_opsets, export_modules_as_functions)
    502     """
    503 
--> 504     _export(
    505         model,
    506         args,

/opt/anaconda3/lib/python3.9/site-packages/torch/onnx/utils.py in _export(model, args, f, export_params, verbose, training, input_names, output_names, operator_export_type, export_type, opset_version, do_constant_folding, dynamic_axes, keep_initializers_as_inputs, fixed_batch_size, custom_opsets, add_node_names, onnx_shape_inference, export_modules_as_functions)
   1527             _validate_dynamic_axes(dynamic_axes, model, input_names, output_names)
   1528 
-> 1529             graph, params_dict, torch_out = _model_to_graph(
   1530                 model,
   1531                 args,

/opt/anaconda3/lib/python3.9/site-packages/torch/onnx/utils.py in _model_to_graph(model, args, verbose, input_names, output_names, operator_export_type, do_constant_folding, _disable_torch_constant_prop, fixed_batch_size, training, dynamic_axes)
   1113 
   1114     try:
-> 1115         graph = _optimize_graph(
   1116             graph,
   1117             operator_export_type,

/opt/anaconda3/lib/python3.9/site-packages/torch/onnx/utils.py in _optimize_graph(graph, operator_export_type, _disable_torch_constant_prop, fixed_batch_size, params_dict, dynamic_axes, input_names, module)
    580     _C._jit_pass_lint(graph)
    581     _C._jit_pass_onnx_autograd_function_process(graph)
--> 582     C._jit_pass_lower_all_tuples(graph)
    583 
    584     # we now record some ops like ones/zeros

RuntimeError: outerNode->outputs().size() == node->inputs().size() INTERNAL ASSERT FAILED at "/Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/jit/passes/dead_code_elimination.cpp":140, please report a bug to PyTorch.''', '''RUST: error[E0382]: use of moved value: `primes`
 --> src/main.rs:9:31
  |
9 |         if vectorIsPrime(num, primes) {
  |                               ^^^^^^ value moved here, in previous iteration of loop
  |
  = note: move occurs because `primes` has type `std::vec::Vec<u64>`, which does not implement the `Copy` trait
''', "REACT: Uncaught Error: Invariant Violation: Element type is invalid: expected a string (for built-in components) or a class/function (for composite components) but got: object."],inputs=inp, cache_examples=False,)
if __name__ == "__main__":
    demo.launch(debug=True)