# AutoTrain Dataset for project: code-explainer

## Dataset Description

This dataset has been automatically processed by AutoTrain for project code-explainer.

### Languages

The BCP-47 code for the dataset's language is unk.

## Dataset Structure

### Data Instances

A sample from this dataset looks as follows:

```json
[
  {
    "text": "def upload_to_s3(local_file, bucket, s3_file):\n    ## This function is responsible for uploading the file into the S3 bucket using the specified credentials. \n    s3 = boto3.client('s3', aws_access_key_id=ACCESS_KEY,\n                      aws_secret_access_key=SECRET_KEY)\n    try:\n        s3.upload_file(local_file, bucket, s3_file)\n        print(\"Upload Successful\")\n        return True\n    except FileNotFoundError:\n        print(\"The file was not found\")\n        return False\n    except NoCredentialsError:\n        print(\"Credentials not available\")\n        return False\n\n\nresult = upload_to_s3(LOCAL_FILE, BUCKET_NAME, S3_FILE_NAME)",
    "target": "Create a function upload_to_s3 the fumction is responsible for uploading the file into the s3 bucket to do so\n1. First creating a client object that will be used to interact with the S3 service using the boto3\n(Boto3 makes it easy to integrate your Python application, library, or script with AWS services including Amazon S3, Amazon EC2, Amazon DynamoDB, and more.)\n2. We make a use of try/catch block to upload the images in s3 bucket \n3. To upload the image we use the upload_file function of s3 client if the upload is successful will return the True with print statement.\n4. In case of exception first is FileNotFoundError will return the false.\n\n(Any message with the contents FileNotFoundError indicates that Python cannot find the file you are referencing. Python raises this error because your program cannot continue running without being able to access the file to which your program refers. )\n\n5. The next except block is NoCredentialsError will return the False along with print statement\n\n(The NoCredentialsError is an error encountered when using the Boto3 library to interface with Amazon Web Services (AWS). Specifically, this error is encountered when your AWS credentials are missing, invalid, or cannot be located by your Python script.)\n",
    "feat_language": "python",
    "feat_status": "annotated",
    "feat_user_created": "6888d00e-fda2-4061-9038-7a86b12c9d9b"
  },
  {
    "text": "def main(username):\n    banner()\n    '''main function accept instagram username\n    return an dictionary object containging profile deatils\n    '''\n\n    url = \"https://www.instagram.com/{}/?hl=en\".format(username)\n    page = requests.get(url)\n    tree = html.fromstring(page.content)\n    data = tree.xpath('//meta[starts-with(@name,\"description\")]/@content')\n\n    if data:\n        data = tree.xpath('//meta[starts-with(@name,\"description\")]/@content')\n        data = data[0].split(', ')\n        followers = data[0][:-9].strip()\n        following = data[1][:-9].strip()\n        posts = re.findall(r'\\d+[,]*', data[2])[0]\n        name = re.findall(r'name\":\"([^\"]+)\"', page.text)[0]\n        aboutinfo = re.findall(r'\"description\":\"([^\"]+)\"', page.text)[0]\n        instagram_profile = {\n            'success': True,\n            'profile': {\n                'name': name,\n                'profileurl': url,\n                'username': username,\n                'followers': followers,\n                'following': following,\n                'posts': posts,\n                'aboutinfo': aboutinfo\n            }\n        }\n    else:\n        instagram_profile = {\n            'success': False,\n            'profile': {}\n        }\n    return instagram_profile\n",
    "target": "Create a function main that accepts an Instagram username and returns a dictionary object containing profile details.\n1. The code first requests the URL of the user's profile from Instagram, then it parses out all of the information on that page into variables.\n2. Then xpath is used to find all tags within this HTML document starting with \"description\" and splitting them by commas until there are no more results found.\n3 we use the findall function of re module and find the post name info and store it in the dictionary and return the dictionary.\n4. Else will just return the dictionary with success is False.\n",
    "feat_language": "python",
    "feat_status": "annotated",
    "feat_user_created": "6888d00e-fda2-4061-9038-7a86b12c9d9b"
  }
]
```

### Dataset Fields

The dataset has the following fields (also called "features"):

```json
{
  "text": "Value(dtype='string', id=None)",
  "target": "Value(dtype='string', id=None)",
  "feat_language": "Value(dtype='string', id=None)",
  "feat_status": "Value(dtype='string', id=None)",
  "feat_user_created": "Value(dtype='string', id=None)"
}
```

### Dataset Splits

This dataset is split into a train and validation split. The split sizes are as follow:

| Split name   | Num samples         |
| ------------ | ------------------- |
| train        | 92 |
| valid        | 23 |

task_categories:summarization region:us
{"task_categories": ["summarization"]}
TAGS
#task_categories-summarization #region-us 

AutoTrain Dataset for project: code-explainer
=============================================


Dataset Description
-------------------


This dataset has been automatically processed by AutoTrain for project code-explainer.


### Languages


The BCP-47 code for the dataset's language is unk.


Dataset Structure
-----------------


### Data Instances


A sample from this dataset looks as follows:


### Dataset Fields


The dataset has the following fields (also called "features"):


### Dataset Splits


This dataset is split into a train and validation split. The split sizes are as follow:




### Languages


The BCP-47 code for the dataset's language is unk.


Dataset Structure
----------------- ### Data Instances


A sample from this dataset looks as follows: ### Dataset Fields


The dataset has the following fields (also called "features"): ### Dataset Splits


This dataset is split into a train and validation split. The split sizes are as follow:
TAGS
#task_categories-summarization #region-us 
 ### Languages


The BCP-47 code for the dataset's language is unk.


Dataset Structure
----------------- ### Data Instances


A sample from this dataset looks as follows: ### Dataset Fields


The dataset has the following fields (also called "features"): ### Dataset Splits


This dataset is split into a train and validation split. The split sizes are as follow:
passage: TAGS
#task_categories-summarization #region-us 
### Languages


The BCP-47 code for the dataset's language is unk.


Dataset Structure
-----------------### Data Instances


A sample from this dataset looks as follows:### Dataset Fields


The dataset has the following fields (also called "features"):### Dataset Splits


This dataset is split into a train and validation split. The split sizes are as follow: