app.py

from langchain.llms import OpenAI
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.prompts.few_shot import FewShotPromptTemplate

import os

os.environ["OPENAI_API_KEY"] = os.environ.get("open_ai_key") #openai key

llm = OpenAI(temperature=.7)

"""# Model Implementation"""



"""## Debugger Model v1"""

DMTemplate = """
STACKTRACE: {StackTrace}
CONTEXT: {Context}
USER RESPONSE: {UserResponse}
isFollowUpNeeded: {isFollowUpNeeded}
SYSTEM RESPONSE: {SystemResponse}
"""

DMPromptTemplate = PromptTemplate(input_variables=["StackTrace", "Context", "isFollowUpNeeded", "UserResponse", "SystemResponse"], template=DMTemplate)

DMExamples = [
    {
        "StackTrace": """$ sudo cog predict r8.im/allenhung1025/looptest@sha256:f5cd715e99046e0513fe2b4034e8f7d8c102525b02f49efb52b05f46fcb9ea83
Starting Docker image r8.im/allenhung1025/looptest@sha256:f5cd715e99046e0513fe2b4034e8f7d8c102525b02f49efb52b05f46fcb9ea83 and running setup()...
No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
Traceback (most recent call last):
...
AssertionError: 
Found no NVIDIA driver on your system. Please check that you
have an NVIDIA GPU and installed a driver from
http://www.nvidia.com/Download/index.aspx
ⅹ Failed to get container status: exit status 1""",
        "Context": """$ cd /usr/local/cuda/samples/1_Utilities/deviceQuery
$ sudo make
$ ./deviceQuery
Detected 1 CUDA Capable device(s)

Device 0: "NVIDIA GeForce RTX 2080 Ti"
  CUDA Driver Version / Runtime Version          11.4 / 11.2
  CUDA Capability Major/Minor version number:    7.5
  Total amount of global memory:                 11016 MBytes (11551440896 bytes)
  (68) Multiprocessors, ( 64) CUDA Cores/MP:     4352 CUDA Cores
...
Result = PASS""",
        "UserResponse": "How do I fix this?",
        "isFollowUpNeeded": "Yes",
        "SystemResponse": """ Does it work if you run the model with Docker directly?
$ docker run -d -p 5000:5000 --gpus all r8.im/allenhung1025/looptest@sha256:f5cd715e99046e0513fe2b4034e8f7d8c102525b02f49efb52b05f46fcb9ea83

$ curl http://localhost:5000/predict -X POST -F seed=-1""",
    }, 
    {
        "StackTrace":"""ERROR in ./node_modules/react-router-dom/server.mjs 18:15-21
Can't import the named export 'Action' from non EcmaScript module (only default export is available)""",
        "Context": "I am using react-router version 6.0.0. This issue can be reproduced by installing react-router and react-router-dom on rails react app using webpacker.",
        "UserResponse": "Why is this not working?",
        "isFollowUpNeeded": "Yes",
        "SystemResponse": "You probably have an older version of history installed. Install version 5.1.0 and you'll have the needed types."
    },
    {
        "StackTrace":"MissingSchema Traceback (most recent call last)\nin ()\n3 fare = gr.inputs.Slider(minimum=0, maximum=1000, default=100, label=\"Fare (british pounds)\")\n4\n----> 5 gr.Interface(predict_survival, [sex, age, fare], \"label\", live=True).launch();\n\n7 frames\n/usr/local/lib/python3.6/dist-packages/requests/models.py in prepare_url(self, url, params)\n386 error = error.format(to_native_string(url, 'utf8'))\n387\n--> 388 raise MissingSchema(error)\n389\n390 if not host:\n\nMissingSchema: Invalid URL 'None': No schema supplied. Perhaps you meant http://None?`",
        "Context": "The system response is alerting the user to ensure that the URL provided to the gr.Interface() is valid and includes the schema (e.g. http or https). The user must check for this in order to avoid the MissingSchema error.",
        "UserResponse": "Can you show me how I could do this in code?",
        "isFollowUpNeeded": "Yes",
        "SystemResponse": """You can check that the URL is valid and includes the schema by using the Python requests library. You can use the "requests.utils.urlparse" method to check the URL format, and add the schema if needed."""
    },
    # {
    #     "StackTrace":r"""Error: Op payload too many bytes: [133060 > 131072]: {\"opId\":\"op-o4Px8Dt1qX\",\"sequenceNumber\":191,\"objectPath\":\"canvas-e7bqXMiHN4\",\"userId\":1011378,\"version\":64,\"type\":\"SIND\",\"schemaVersion\":170,\"basisOpId\":\"op-PlSlhFwNRQ\",\"diagnostics\":{\"docId\":\"PIhhZ8iQBG\",\"schemaVersion\":170,\"version\":64,\"basisOpId\":\"op-PlSlhFwNRQ\",\"sequenceNumber\":191,\"appVersionInfo\":{\"branch\":\"hemanth-hdoan-packs-crossdoc-fix-forward-20221117-adhoc\",\"hash\":\"3039edad3b42\"},\"appInstanceId\":\"d65642d2-4e6b-4d03-9328-61a39a11ab4f\",\"opGeneration\":1,\"creationMillis\":1668745463573,\"needsBlessing\":false}}\n    at Ir.ensureOpNotTooBig (https://cdn.coda.io/assets/browser.723a8c13ee82fcfe8b99.entry.js:2:6132445)\n    at Ir.applyLocalOperation (https://cdn.coda.io/assets/browser.723a8c13ee82fcfe8b99.entry.js:2:6132496)\n    at gt._handleLocalOperation (https://cdn.coda.io/assets/browser.723a8c13ee82fcfe8b99.entry.js:2:5962142)\n    at https://cdn.coda.io/assets/browser.723a8c13ee82fcfe8b99.entry.js:2:6237790\n    at n.apply (https://cdn.coda.io/assets/browser.723a8c13ee82fcfe8b99.entry.js:2:6237896)\n    at https://cdn.coda.io/assets/browser.723a8c13ee82fcfe8b99.entry.js:2:952844\n    at Object.withoutNormalizing (https://cdn.coda.io/assets/browser.723a8c13ee82fcfe8b99.entry.js:2:933543)\n    at Object.insertNodes (https://cdn.coda.io/assets/browser.723a8c13ee82fcfe8b99.entry.js:2:952043)\n    at Yo (https://cdn.coda.io/assets/browser.723a8c13ee82fcfe8b99.entry.js:2:6268075)\n    at Module.Ko (https://cdn.coda.io/assets/browser.723a8c13ee82fcfe8b99.entry.js:2:6267416)""",
    #     "Context": "The given stacktrace is related to an operation payload that is too large. The context suggests the user has tried to make changes to the Confluence document structure and the system response is to look into the slate API's insertFragment method in order to split the document into smaller chunks and reduce the payload size.",
    #     "UserResponse": "How could I code this?",
    #     "isFollowUpNeeded": "No",
    #     "SystemResponse": "You can use the slate API's insertFragment method to break the document into smaller chunks and reduce the payload size. This method takes a fragment to be inserted, and the path of the target node, and an optional index argument, and inserts the fragment at the given path and index."
    # }
]

DMPrefix = """You are an expert {isLanguage} machine learning developer. Ask clarifying questions and debug the following error message. If 'isFollowUpNeeded' is 'Yes', gather context on the following stacktrace. Set 'isFollowUpNeeded' to 'No' if you feel like you have sufficient context to suggest approaches to solving the problem. If 'isFollowUpNeeded' is set to 'No', , suggest approaches to solving the problem or answering the 'User Response'. Do not repeat approaches, already suggested in 'Context'.:"""

DMSuffix = """
STACKTRACE: {StackTrace}
Context: {Context}
UserResponse: {UserResponse}
isFollowUpNeeded:
"""

DMPrompt = FewShotPromptTemplate(
    examples=DMExamples,
    example_prompt=DMPromptTemplate,
    suffix=DMSuffix,
    prefix=DMPrefix,
    input_variables=["isLanguage","StackTrace", "UserResponse", "Context"]
)

DMChain = LLMChain(llm=llm, prompt=DMPrompt)

"""## Debugger Model v2

### isFurtherContextNeeded
"""

isFurtherContextNeededTemplate = """
STACKTRACE: {StackTrace}
CONTEXT: {Context}
USER RESPONSE: {UserResponse}
isFurtherContextNeeded: {isFurtherContextNeeded}
"""

isFurtherContextNeededPromptTemplate = PromptTemplate(input_variables=["StackTrace", "Context", "isFurtherContextNeeded", "UserResponse"], template=isFurtherContextNeededTemplate)

isFurtherContextNeededExamples = [
    {
        "StackTrace": """$ sudo cog predict r8.im/allenhung1025/looptest@sha256:f5cd715e99046e0513fe2b4034e8f7d8c102525b02f49efb52b05f46fcb9ea83
Starting Docker image r8.im/allenhung1025/looptest@sha256:f5cd715e99046e0513fe2b4034e8f7d8c102525b02f49efb52b05f46fcb9ea83 and running setup()...
No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
Traceback (most recent call last):
...
AssertionError: 
Found no NVIDIA driver on your system. Please check that you
have an NVIDIA GPU and installed a driver from
http://www.nvidia.com/Download/index.aspx
ⅹ Failed to get container status: exit status 1""",
        "Context": """$ cd /usr/local/cuda/samples/1_Utilities/deviceQuery
$ sudo make
$ ./deviceQuery
Detected 1 CUDA Capable device(s)

Device 0: "NVIDIA GeForce RTX 2080 Ti"
  CUDA Driver Version / Runtime Version          11.4 / 11.2
  CUDA Capability Major/Minor version number:    7.5
  Total amount of global memory:                 11016 MBytes (11551440896 bytes)
  (68) Multiprocessors, ( 64) CUDA Cores/MP:     4352 CUDA Cores
...
Result = PASS""",
        "UserResponse": "How do I fix this?",
        "isFurtherContextNeeded": "Yes"}, 
    {
        "StackTrace":"""ERROR in ./node_modules/react-router-dom/server.mjs 18:15-21
Can't import the named export 'Action' from non EcmaScript module (only default export is available)""",
        "Context": "I am using react-router version 6.0.0. This issue can be reproduced by installing react-router and react-router-dom on rails react app using webpacker.",
        "UserResponse": "Why is this not working?",
        "isFurtherContextNeeded": "Yes"},
    {
        "StackTrace":"MissingSchema Traceback (most recent call last)\nin ()\n3 fare = gr.inputs.Slider(minimum=0, maximum=1000, default=100, label=\"Fare (british pounds)\")\n4\n----> 5 gr.Interface(predict_survival, [sex, age, fare], \"label\", live=True).launch();\n\n7 frames\n/usr/local/lib/python3.6/dist-packages/requests/models.py in prepare_url(self, url, params)\n386 error = error.format(to_native_string(url, 'utf8'))\n387\n--> 388 raise MissingSchema(error)\n389\n390 if not host:\n\nMissingSchema: Invalid URL 'None': No schema supplied. Perhaps you meant http://None?`",
        "Context": "The system response is alerting the user to ensure that the URL provided to the gr.Interface() is valid and includes the schema (e.g. http or https). The user must check for this in order to avoid the MissingSchema error.",
        "UserResponse": "Can you show me how I could do this in code?",
        "isFurtherContextNeeded": "Yes"},
]

isFurtherContextNeededPrefix = """You are an expert {isLanguage} machine learning developer. Ask clarifying questions and debug the following error message. You can only set 'isFollowUpNeeded' to either 'Yes' or 'No'. Set 'isFollowUpNeeded' is 'Yes', if further context to answer the 'User Response' or to debug the 'StackTrace'. Set 'isFollowUpNeeded' to 'No' if you feel like you have sufficient context to suggest approaches to solving the problem. Do not repeat approaches, already suggested in 'Context'.:"""

isFurtherContextNeededSuffix = """
STACKTRACE: {StackTrace}
Context: {Context}
UserResponse: {UserResponse}
isFurtherContextNeeded:
"""

isFurtherContextNeededPrompt = FewShotPromptTemplate(
    examples=isFurtherContextNeededExamples,
    example_prompt=isFurtherContextNeededPromptTemplate,
    suffix=isFurtherContextNeededSuffix,
    prefix=isFurtherContextNeededPrefix,
    input_variables=["isLanguage","StackTrace", "Context", "UserResponse"]
)

isFurtherContextNeededChain = LLMChain(llm=llm, prompt=isFurtherContextNeededPrompt)

"""### SystemResponse"""

SystemResponseTemplate = """
STACKTRACE: {StackTrace}
CONTEXT: {Context}
USER RESPONSE: {UserResponse}
isFurtherContextNeeded: {isFurtherContextNeeded}
SYSTEM RESPONSE: {SystemResponse}
"""

SystemResponsePromptTemplate = PromptTemplate(input_variables=["StackTrace", "Context", "isFurtherContextNeeded", "UserResponse", "SystemResponse"], template=SystemResponseTemplate)

SystemResponseExamples = [
    {
        "StackTrace": """$ sudo cog predict r8.im/allenhung1025/looptest@sha256:f5cd715e99046e0513fe2b4034e8f7d8c102525b02f49efb52b05f46fcb9ea83
Starting Docker image r8.im/allenhung1025/looptest@sha256:f5cd715e99046e0513fe2b4034e8f7d8c102525b02f49efb52b05f46fcb9ea83 and running setup()...
No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
Traceback (most recent call last):
...
AssertionError: 
Found no NVIDIA driver on your system. Please check that you
have an NVIDIA GPU and installed a driver from
http://www.nvidia.com/Download/index.aspx
ⅹ Failed to get container status: exit status 1""",
        "Context": """$ cd /usr/local/cuda/samples/1_Utilities/deviceQuery
$ sudo make
$ ./deviceQuery
Detected 1 CUDA Capable device(s)

Device 0: "NVIDIA GeForce RTX 2080 Ti"
  CUDA Driver Version / Runtime Version          11.4 / 11.2
  CUDA Capability Major/Minor version number:    7.5
  Total amount of global memory:                 11016 MBytes (11551440896 bytes)
  (68) Multiprocessors, ( 64) CUDA Cores/MP:     4352 CUDA Cores
...
Result = PASS""",
        "UserResponse": "How do I fix this?",
        "isFurtherContextNeeded": "Yes",
        "SystemResponse": """ Does it work if you run the model with Docker directly?
$ docker run -d -p 5000:5000 --gpus all r8.im/allenhung1025/looptest@sha256:f5cd715e99046e0513fe2b4034e8f7d8c102525b02f49efb52b05f46fcb9ea83

$ curl http://localhost:5000/predict -X POST -F seed=-1""",
    }, 
    {
        "StackTrace":"""ERROR in ./node_modules/react-router-dom/server.mjs 18:15-21
Can't import the named export 'Action' from non EcmaScript module (only default export is available)""",
        "Context": "I am using react-router version 6.0.0. This issue can be reproduced by installing react-router and react-router-dom on rails react app using webpacker.",
        "UserResponse": "Why is this not working?",
        "isFurtherContextNeeded": "No",
        "SystemResponse": "You probably have an older version of history installed. Install version 5.1.0 and you'll have the needed types."
    },
    {
        "StackTrace":"MissingSchema Traceback (most recent call last)\nin ()\n3 fare = gr.inputs.Slider(minimum=0, maximum=1000, default=100, label=\"Fare (british pounds)\")\n4\n----> 5 gr.Interface(predict_survival, [sex, age, fare], \"label\", live=True).launch();\n\n7 frames\n/usr/local/lib/python3.6/dist-packages/requests/models.py in prepare_url(self, url, params)\n386 error = error.format(to_native_string(url, 'utf8'))\n387\n--> 388 raise MissingSchema(error)\n389\n390 if not host:\n\nMissingSchema: Invalid URL 'None': No schema supplied. Perhaps you meant http://None?`",
        "Context": "The system response is alerting the user to ensure that the URL provided to the gr.Interface() is valid and includes the schema (e.g. http or https). The user must check for this in order to avoid the MissingSchema error.",
        "UserResponse": "Can you show me how I could do this in code?",
        "isFurtherContextNeeded": "No",
        "SystemResponse": """You can check that the URL is valid and includes the schema by using the Python requests library. You can use the "requests.utils.urlparse" method to check the URL format, and add the schema if needed."""
    },
]

SystemResponsePrefix = """You are an expert {isLanguage} machine learning developer. Please write your responses in a warm and friendly tone - like a teacher helping a student learn. Ask clarifying questions and debug the following error message. If 'isFurtherContextNeeded' is 'Yes', gather context on the given 'StackTrace' or the 'USER RESPONSE'. If 'isFollowUpNeeded' is set to 'No', suggest approaches to solving the problem. Do not repeat approaches, already suggested in 'Context'.:"""

SystemResponseSuffix = """
STACKTRACE: {StackTrace}
CONTEXT: {Context}
USER RESPONSE: {UserResponse}
isFurtherContextNeeded: {isFurtherContextNeeded}
SYSTEM RESPONSE: 
"""

SystemResponsePrompt = FewShotPromptTemplate(
    examples=SystemResponseExamples,
    example_prompt=SystemResponsePromptTemplate,
    suffix=SystemResponseSuffix,
    prefix=SystemResponsePrefix,
    input_variables=["isLanguage","StackTrace", "Context", "UserResponse", "isFurtherContextNeeded"]
)

SystemResponseChain = LLMChain(llm=llm, prompt=SystemResponsePrompt)

"""## Summarizer Model"""

SummarizerTemplate = """ You are an expert {isLanguage} machine learning developer. Summarize the given context, system response for the stacktrace, for somebody that is trying to debug this stacktrace:
STACKTRACE: {StackTrace}
CONTEXT: {Context}
SYSTEM RESPONSE: {SystemResponse}
SUMMARY:
"""

SummarizerPrompt = PromptTemplate(input_variables=["isLanguage", "StackTrace", "SystemResponse", "Context"], template=SummarizerTemplate)

StackTrace = r"""ImportError                               Traceback (most recent call last)
<ipython-input-13-43eca54f7d45> in <module>
----> 1 import gradio as gr

~\anaconda3\lib\site-packages\gradio\__init__.py in <module>
      1 import pkgutil
      2 
----> 3 import gradio.components as components
      4 import gradio.inputs as inputs
      5 import gradio.outputs as outputs

~\anaconda3\lib\site-packages\gradio\components.py in <module>
     29 from markdown_it import MarkdownIt
     30 
---> 31 from gradio import media_data, processing_utils, utils
     32 from gradio.blocks import Block
     33 from gradio.documentation import document, set_documentation_group

~\anaconda3\lib\site-packages\gradio\processing_utils.py in <module>
     18 from PIL import Image, ImageOps, PngImagePlugin
     19 
---> 20 from gradio import encryptor, utils
     21 
     22 with warnings.catch_warnings():

~\anaconda3\lib\site-packages\gradio\utils.py in <module>
     32 
     33 import aiohttp
---> 34 import fsspec.asyn
     35 import httpx
     36 import requests

~\anaconda3\lib\site-packages\fsspec\asyn.py in <module>
     14 from .exceptions import FSTimeoutError
     15 from .spec import AbstractBufferedFile, AbstractFileSystem
---> 16 from .utils import is_exception, other_paths
     17 
     18 private = re.compile("_[^_]")

ImportError: cannot import name 'is_exception' from 'fsspec.utils' (C:\Users\tompe\anaconda3\lib\site-packages\fsspec\utils.py)

def is_cat(x): return x[0].isupper()"""

SystemResponse = "It looks like the function 'is_exception' is missing from the fsspec.utils module. You can try reinstalling fsspec and updating your dependencies, to make sure you have the latest version of the module."
isLanguage = "python"
Context = ""


SummarizerChain = LLMChain(llm=llm, prompt=SummarizerPrompt)
chainOutput = SummarizerChain({"StackTrace": StackTrace, "Context": Context, "isLanguage": isLanguage, "SystemResponse": SystemResponse})['text']
chainOutput

"""# Gradio Implementation"""

clerkieExamples=['''---------------------------------------------------------------------------
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.''', '''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
''', "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."]

import random
import gradio as gr
import openai
import re

openai.api_key = os.environ["OPENAI_API_KEY"]

chat_variables = {
    "Context": "", 
    "StackTrace": "", 
    "isLanguage": "", 
}
def chat(message, history):
    print(type(message))
    history = history or []
    print("len(history: ", len(history))
    if len(history) == 0: ## just the stacktrace
      response = "which language is this in? (python, java, c++, kotlin, etc.)"
      history.append((message, response))
      return history, history
    elif len(history) == 1: ## stacktrace + just entered the language 
      # get stacktrace
      chat_variables["StackTrace"] = history[0][0]
      # get language
      chat_variables["isLanguage"] = message
      # set question (hardcoded for v1)
      UserResponse = "Any idea how I can solve this problem?"
      # set initial context to empty
      chat_variables["Context"] = ""
    else: # subsequent prompts
      UserResponse = message

    # run the v1 debugger model to see if that improves latency
    initDebuggerModelResponse = DMChain({"StackTrace": chat_variables["StackTrace"], "UserResponse": UserResponse, "isLanguage": chat_variables["isLanguage"], "Context": chat_variables["Context"]})['text']
    print("initDebuggerModelResponse: ", initDebuggerModelResponse)
    if initDebuggerModelResponse == "Yes" or initDebuggerModelResponse == "No": # i.e. doesn't output System Response
      response = SystemResponseChain({"StackTrace": chat_variables["StackTrace"], "UserResponse": UserResponse, "isLanguage": chat_variables["isLanguage"], "Context": chat_variables["Context"], "isFurtherContextNeeded": initDebuggerModelResponse})['text']
    else: 
      response = initDebuggerModelResponse.split("SYSTEM RESPONSE:")[1]
    # summarize the conversation 
    SummarizerChain = LLMChain(llm=llm, prompt=SummarizerPrompt)
    chat_variables["Context"] = SummarizerChain({"StackTrace": chat_variables["StackTrace"], "Context": chat_variables["Context"], "isLanguage": chat_variables["isLanguage"], "SystemResponse": response})['text']
    print("response: ", response)

    # get a code example after stacktrace + user just entered language
    if len(history) == 1:
        response_2 = ""
        UserResponse = "give me an example of the code for this based on the suggested approach" # hardcode this to always give a code example after seeing stacktrace
        initDebuggerModelResponse = DMChain({"StackTrace": chat_variables["StackTrace"], "UserResponse": UserResponse, "isLanguage": chat_variables["isLanguage"], "Context": chat_variables["Context"]})['text']
        response_2 = initDebuggerModelResponse.split("SYSTEM RESPONSE:")[1]
        response += "\n" + response_2
    history.append((message, response))
    return history, history

def set_text(inp):
  return inp

def clear(arg): 
  return "" 

with gr.Blocks() as demo:
    user_state=gr.State([])
    gr.Markdown("""# Welcome to Clerkie 🤖""")
    gr.Markdown("""Use Clerkie to help you debug your complex programming errors (especially the multi-function / extremely verbose ones). Clerkie uses GPT-3 under-the-hood, and can therefore work across several languages / types of errors - including python, linux kernel output, rust, javascript, C, C++, etc. Please feel free to give it a try and let us know what you think!""")
    gr.Markdown("""### 👀 P.S. [Check out our GPT-3 based Chrome Extension that debugs your code](https://chrome.google.com/webstore/detail/clerkie-ai/oenpmifpfnikheaolfpabffojfjakfnn) 🔥🔥🔥""")
    with gr.Row():
      with gr.Column():
        output = gr.Chatbot().style(color_map=("green", "pink"))
        # allow_flagging="never"
        inp = gr.Textbox(placeholder="enter your stacktrace here")
        print(type(inp))
        btn = gr.Button("Enter message")
        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(clerkieExamples,
        inputs=inp,
        cache_examples=False,
    )
if __name__ == "__main__":
    demo.launch(debug=True)