app.py

import json
import gradio as gr
import requests as req

code_nl = "function for db connection"

CT5_URL = "https://api-inference.huggingface.co/models/stmnk/codet5-small-code-summarization-python"
CT5_METHOD = 'POST'
API_URL = CT5_URL
headers = {"Authorization": "Bearer api_UhCKXKyqxJOpOcbvrZurQFqmVNZRTtxVfl"}

def query(payload):
	response = req.post(API_URL, headers=headers, json=payload)
	return response.json()


dfs_code = r"""
def dfs(visited, graph, node):  #function for dfs 
    if node not in visited:
        print (node)
        visited.add(node)
        for neighbour in graph[node]:
            dfs(visited, graph, neighbour)
"""

function_code = r"""
def write_documents(self, documents: Union[List[dict], List[Document]], index: Optional[str] = None,
                        batch_size: int = 10_000, duplicate_documents: Optional[str] = None):

        if index and not self.client.indices.exists(index=index):
            self._create_document_index(index)

        if index is None:
            index = self.index
        duplicate_documents = duplicate_documents or self.duplicate_documents
        assert duplicate_documents in self.duplicate_documents_options, \
            f"duplicate_documents parameter must be {', '.join(self.duplicate_documents_options)}"

        field_map = self._create_document_field_map()
        document_objects = [Document.from_dict(d, field_map=field_map) if isinstance(d, dict) else d for d in documents]
        document_objects = self._handle_duplicate_documents(documents=document_objects,
                                                            index=index,
                                                            duplicate_documents=duplicate_documents)
        documents_to_index = []
        for doc in document_objects:
            _doc = {
                "_op_type": "index" if duplicate_documents == 'overwrite' else "create",
                "_index": index,
                **doc.to_dict(field_map=self._create_document_field_map())
            }  # type: Dict[str, Any]

            # cast embedding type as ES cannot deal with np.array
            if _doc[self.embedding_field] is not None:
                if type(_doc[self.embedding_field]) == np.ndarray:
                    _doc[self.embedding_field] = _doc[self.embedding_field].tolist()

            # rename id for elastic
            _doc["_id"] = str(_doc.pop("id"))

            # don't index query score and empty fields
            _ = _doc.pop("score", None)
            _doc = {k:v for k,v in _doc.items() if v is not None}

            # In order to have a flat structure in elastic + similar behaviour to the other DocumentStores,
            # we "unnest" all value within "meta"
            if "meta" in _doc.keys():
                for k, v in _doc["meta"].items():
                    _doc[k] = v
                _doc.pop("meta")
            documents_to_index.append(_doc)

            # Pass batch_size number of documents to bulk
            if len(documents_to_index) % batch_size == 0:
                bulk(self.client, documents_to_index, request_timeout=300, refresh=self.refresh_type)
                documents_to_index = []

        if documents_to_index:
            bulk(self.client, documents_to_index, request_timeout=300, refresh=self.refresh_type)

"""

task_code = f' Summarize Python: {function_code}'
# task_code = f' Summarize Python: {dfs_code}'

real_docstring = r"""
        Indexes documents for later queries in Elasticsearch.

        Behaviour if a document with the same ID already exists in ElasticSearch:
        a) (Default) Throw Elastic's standard error message for duplicate IDs.
        b) If `self.update_existing_documents=True` for DocumentStore: Overwrite existing documents.
        (This is only relevant if you pass your own ID when initializing a `Document`.
        If don't set custom IDs for your Documents or just pass a list of dictionaries here,
        they will automatically get UUIDs assigned. See the `Document` class for details)

        :param documents: a list of Python dictionaries or a list of Haystack Document objects.
                          For documents as dictionaries, the format is {"content": "<the-actual-text>"}.
                          Optionally: Include meta data via {"content": "<the-actual-text>",
                          "meta":{"name": "<some-document-name>, "author": "somebody", ...}}
                          It can be used for filtering and is accessible in the responses of the Finder.
                          Advanced: If you are using your own Elasticsearch mapping, the key names in the dictionary
                          should be changed to what you have set for self.content_field and self.name_field.
        :param index: Elasticsearch index where the documents should be indexed. If not supplied, self.index will be used.
        :param batch_size: Number of documents that are passed to Elasticsearch's bulk function at a time.
        :param duplicate_documents: Handle duplicates document based on parameter options.
                                    Parameter options : ( 'skip','overwrite','fail')
                                    skip: Ignore the duplicates documents
                                    overwrite: Update any existing documents with the same ID when adding documents.
                                    fail: an error is raised if the document ID of the document being added already
                                    exists.
        :raises DuplicateDocumentError: Exception trigger on duplicate document
        :return: None
"""
    
def docgen_func(function_code):
    req_data = {"inputs": function_code}
    output = query(req_data)
    if type(output) is list:
        return f'"""\n{output[0]["generated_text"]}\n"""'
    else:
        return str(output)

def pygen_func(nl_code_intent):
    pass # TODO: generate code PL from intent NL + search in corpus
    # inputs = {'code_nl': code_nl}    
    # payload = json.dumps(inputs)
    # prediction = req.request(CT5_METHOD, CT5_URL, data=payload)
    # prediction = req.request(CT5_METHOD, CT5_URL, json=req_data)
    # answer = json.loads(prediction.content.decode("utf-8"))
    # return str(answer)
    # CT5_URL = "https://api-inference.huggingface.co/models/nielsr/codet5-small-code-summarization-ruby"

iface = gr.Interface(
    # pygen_func,
    docgen_func,
    [
        # gr.inputs.Textbox(lines=7, label="Code Intent (NL)", default=task_code),
        gr.inputs.Textbox(lines=10, label="Enter Task + Code in Python (PL)", default=task_code),  
    ],
    # gr.outputs.Textbox(label="Code Generated PL")) 
    gr.outputs.Textbox(label="Docstring Generated (NL)"),
    title='Generate a documentation string for Python code',
    description='The application takes as input the python code for a function, or a class, and generates a documentation string, or code comment, for it using codeT5 fine tuned for code2text generation. Code to text generation, or code summarization, is a CodeXGLUE generation, or sequence to sequence, downstream task. CodeXGLUE stands for General Language Understanding Evaluation benchmark *for code*, which includes several datasets for diversified code intelligence downstream tasks.',
    article=r"""CodeXGLLUE task definition (and dataset): 

Code summarization (CodeSearchNet). 
    
A model is given the task to generate natural language comments for a code.
    
For further details, see the [CodeXGLUE](https://github.com/microsoft/CodeXGLUE) benchmark dataset and open challenge for code intelligence. 
""",
    theme='grass',
    # examples=[[dfs_code],['code 2']],
    verbose=True,
    show_tips=True
)
    
iface.launch(share=True)