---
license: apache-2.0
language:
- en
metrics:
- accuracy
library_name: transformers
pipeline_tag: text-generation
tags:
- python
- document
- code
- code2doc
- instruction_tuned
- basemodel
- pytorch
- docstring
- documentation
- text-generation-inference
widget:
- text: "<code>def get_np_array_transition_probability_matrix(int_num_states, np_array_A_matrix):print(np_array_A_matrix)np_array_A_matrix += (np.full((int_num_states, int_num_states), float_eps) - (np.identity(int_num_states) * float_eps))print(np_array_A_matrix)np_array_D_matrix = np.diag(np.sum(np_array_A_matrix, axis=1))print(np_array_D_matrix)np_array_D_matrix_inv = np.linalg.inv(np_array_D_matrix)print(np_array_D_matrix_inv)np_array_P_matrix = np.dot(np_array_D_matrix_inv, np_array_A_matrix)print(np_array_P_matrix)print(np.sum(np_array_P_matrix, axis=1))return np_array_P_matrix</code><question>Document the python code above.</question><doc>"
  example_title: "example"
---
# pip-code-to-doc

[pipableAi](https://www.linkedin.com/company/pipable.ai/about/)

[colab_notebook](https://colab.research.google.com/drive/17PyMU_3QN9LROy7x-jmaema0cuLRzBvc?usp=sharing)

## What have we built?

A 1.3 bn code documentation model that outperforms most models on documenting codes and making your in-house libs ready for LLM and RAG pipelines.
We have also open sourced a [parsing lib](https://github.com/PipableAI/pip-library-parser) for the same, together the lib and model can turn your codebase to functional parse tree ready to be consumed by LLMs to execute complex tasks.
This is a further trained version of pip-sql-1.3b.

## How we built it?

We used softmax cross entropy and a modified form of policy grad along with Q loss, optimized in an EM set up.
Loss behaviour in the set up mentioned above - 

## License

The model is open source under apache 2.0. License

## Usage


### Library use
```python
!pip3 install git+https://github.com/PipableAI/pip-library-parser
!pip3 install atlassian-python-api


from pip_library_parser import CodeToDocGenerator
from atlassian import Jira

import torch
torch.set_default_device("cuda")

# Instantiate the CodeToDocGenerator
generator = CodeToDocGenerator()

# Generate docstrings for the module's functions and methods
module = Jira
module_name = "atlassian.Jira"

docs = generator.generate_module_docs(module, module_name)
print(docs)
```

```python
from pip_library_parser import CodeToDocGenerator

# Instantiate the CodeToDocGenerator
generator = CodeToDocGenerator()

code_snippet = """
def example_function(x):
    return x * 2
"""

docstring = generator.generate_docstring_from_pip_model(code_snippet)
print("Generated Docstring:")
print(docstring)
```

### Installation

```bash
pip install transformers
```

### Prompt
```python
prompt = f"""<example_response>{}</example_response><function_code>{code}</function_code>
<question>Give one line description of the python code above in natural language.</question>
<doc>"""

prompt = f"""<example_response>{example of some  --question: , --query}</example_response><schema>{schema with cols described}</schema>
<question>Write a sql query to ....</question>
<sql>"""
```

### PyTorch
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda"
model = AutoModelForCausalLM.from_pretrained("PipableAI/pip-code-to-doc-1.3b").to(device)
tokenizer = AutoTokenizer.from_pretrained("PipableAI/pip-code-to-doc-1.3b")
prompt = f"""<example_response>
--code:def function_2(x): return x / 2
--question:Document the code
--doc:
    Description:This function takes a number and divides it by 2.
    Parameters:
    - x (numeric): The input value to be divided by 2.
    Returns:
    - float: The result of x divided by 2
    Example:
    To call the function, use the following code:
    function2(1.0)</example_response>
<function_code>
def example_function(x):
    return x * 2
</function_code>
<question>Document the python code above giving function description ,parameters and return type and example how to call the function.</question>
<doc>"""
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=300)
tokenizer.decode(outputs[0], skip_special_tokens=True).split('<doc>')[-1].split('</doc>')[0]
```



## Examples

### prompt
```python
text=''' <example_response>
--code:def function_2(x): return x / 2
--question:Document the code
--doc:
    Description:This function takes a number and divides it by 2.
    Parameters:
    - x (numeric): The input value to be divided by 2.
    Returns:
    - float: The result of x divided by 2
    Example:
    To call the function, use the following code:
    function2(1.0)</example_response>
<function_code>def _plot_bounding_polygon(
    polygons_coordinates, output_html_path="bounding_polygon_map.html"
):
    # Create a Folium map centered at the average coordinates of all bounding boxes
    map_center = [
        sum(
            [
                coord[0]
                for polygon_coords in polygons_coordinates
                for coord in polygon_coords
            ]
        )
        / sum([len(polygon_coords) for polygon_coords in polygons_coordinates]),
        sum(
            [
                coord[1]
                for polygon_coords in polygons_coordinates
                for coord in polygon_coords
            ]
        )
        / sum([len(polygon_coords) for polygon_coords in polygons_coordinates]),
    ]

    my_map = folium.Map(location=map_center, zoom_start=12)

    # Add each bounding polygon to the map
    for polygon_coords in polygons_coordinates:
        folium.Polygon(
            locations=polygon_coords,
            color="blue",
            fill=True,
            fill_color="blue",
            fill_opacity=0.2,
        ).add_to(my_map)

    # Add bounding boxes as markers to the map
    marker_cluster = MarkerCluster().add_to(my_map)

    for polygon_coords in polygons_coordinates:
        for coord in polygon_coords:
            folium.Marker(
                location=[coord[0], coord[1]], popup=f"Coordinates: {coord}"
            ).add_to(marker_cluster)

    # Add draw control to allow users to draw additional polygons
    draw = Draw(export=True)
    draw.add_to(my_map)

    # Save the map as an HTML file
    my_map.save(output_html_path)

    return output_html_path
    </function_code>
    <question>Document the python code above giving function description ,parameters and return type and example how to call the function</question><doc>'''
```

### Response
```txt
 Description:This function generates a map of the bounding polygons and saves it as an HTML file.
    Parameters:
    - polygons_coordinates (list of lists of tuples): A list of lists of tuples representing the coordinates of the polygons. Each polygon is a list of coordinates.
    - output_html_path (str, optional): The path where the HTML file should be saved. Defaults to "bounding_polygon_map.html".
    Returns:
    - str: The path to the saved HTML file.
    Example:
    To call the function, use the following code:
    plot_bounding_polygon([[(0, 0), (1, 0), (1, 1), (0, 1)], [(2, 2), (3, 2), (3, 3), (2, 3)]], "my_map.html").
```

### Team
Avi Kothari, Gyan Ranjan, Pratham Gupta, Ritvik Aryan Kalra, Soham Acharya