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Merge pull request #133 from haesleinhuepf/notebook_generation
add notebook-generation
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,166 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "bbd93ac1-f79b-491c-88b4-50807b541b6e", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Generating notebooks\n", | ||
| "When working on complex tasks, it may make sense to generate an entire notebook for the task. \n", | ||
| "Hence, we can ask bob to generate a notebook for a series of tasks." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, | ||
| "id": "51256f2d-e4cb-4c8b-975d-75afad68c837", | ||
| "metadata": { | ||
| "tags": [] | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from bia_bob import bob" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
| "id": "8a1e8db6-a144-4773-a2ab-6e0eac17f94b", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "Task type: 3\n" | ||
| ] | ||
| }, | ||
| { | ||
| "data": { | ||
| "text/markdown": [ | ||
| "A notebook has been saved as [Blob_Segmentation_Analysis.ipynb](Blob_Segmentation_Analysis.ipynb)." | ||
| ], | ||
| "text/plain": [ | ||
| "<IPython.core.display.Markdown object>" | ||
| ] | ||
| }, | ||
| "metadata": {}, | ||
| "output_type": "display_data" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "%%bob \n", | ||
| "Please write a notebook that does the following:\n", | ||
| "* open blobs.tif, \n", | ||
| "* segment the bright objects\n", | ||
| "* measure their area and perimeter\n", | ||
| "* plot the area against perimeter" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "9a13dd39-7c0a-42b9-94c0-a916e21addd9", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Note, if we don't ask for the notebook explicitly, Bob will write a huge code block, which might be less readable." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 3, | ||
| "id": "8b152eb3-326b-4651-9033-0458116be0d7", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "Task type: 1\n" | ||
| ] | ||
| }, | ||
| { | ||
| "data": { | ||
| "text/markdown": [ | ||
| "We will load an image, segment bright objects, measure their area and perimeter, and plot area against perimeter.\n", | ||
| "\n" | ||
| ], | ||
| "text/plain": [ | ||
| "<IPython.core.display.Markdown object>" | ||
| ] | ||
| }, | ||
| "metadata": {}, | ||
| "output_type": "display_data" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "%%bob \n", | ||
| "Please do the following:\n", | ||
| "* open blobs.tif, \n", | ||
| "* segment the bright objects\n", | ||
| "* measure their area and perimeter\n", | ||
| "* plot the area against perimeter" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "6f12df40-04df-42ab-88ca-3691418362b6", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from skimage.io import imread\n", | ||
| "from skimage.filters import threshold_otsu\n", | ||
| "from skimage.measure import label, regionprops_table\n", | ||
| "import matplotlib.pyplot as plt\n", | ||
| "import pandas as pd\n", | ||
| "import stackview\n", | ||
| "\n", | ||
| "# Load the image\n", | ||
| "filename = 'blobs.tif'\n", | ||
| "image = imread(filename)\n", | ||
| "stackview.insight(image)\n", | ||
| "\n", | ||
| "# Segment the bright objects using Otsu's thresholding\n", | ||
| "threshold_value = threshold_otsu(image)\n", | ||
| "binary_image = image > threshold_value\n", | ||
| "stackview.insight(binary_image)\n", | ||
| "\n", | ||
| "# Label the segmented objects\n", | ||
| "labeled_image = label(binary_image)\n", | ||
| "stackview.insight(labeled_image)\n", | ||
| "\n", | ||
| "# Measure area and perimeter of each labeled object\n", | ||
| "properties = regionprops_table(labeled_image, properties=['area', 'perimeter'])\n", | ||
| "df_properties = pd.DataFrame(properties)\n", | ||
| "\n", | ||
| "# Plot area against perimeter\n", | ||
| "plt.scatter(df_properties['area'], df_properties['perimeter'])\n", | ||
| "plt.xlabel('Area')\n", | ||
| "plt.ylabel('Perimeter')\n", | ||
| "plt.title('Area vs Perimeter of Segmented Objects')\n", | ||
| "plt.show()" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3 (ipykernel)", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.9.19" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 5 | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,78 @@ | ||
| def generate_notebook(prompt, filename=None): | ||
| """ | ||
| Generates a notebook from a given prompt and saves it to a file. | ||
| If the filename is not specified, a filename will be generated by the language model. | ||
| If the specified file exists already, it will be overwritten. | ||
| Parameters | ||
| ---------- | ||
| prompt | ||
| filename, optional | ||
| Returns | ||
| ------- | ||
| filename | ||
| """ | ||
| from ._utilities import create_system_prompt, generate_response | ||
| from ._machinery import Context, init_assistant | ||
| import os | ||
| if Context.model is None: | ||
| init_assistant() | ||
|
|
||
| system_prompt = create_system_prompt().split("## Todos")[0] + f""" | ||
| You have been asked to generate a notebook in the ipynb format. | ||
| Start the notebook with an introduction of the task. | ||
| List the steps your are planning to do in the notebook. Do not use Python yet. | ||
| Also labeling is an individual processing step. | ||
| Before starting to write code cells, add the disclaimer "This code is generated by an AI model using the [bia-bob project](https://github.com/haesleinhuepf/bia-bob). It is good scientific practice to check the code and results it produces carefully.". | ||
| Import the libraries you are planning to use. | ||
| Write one code cell for each processing step in the analysis. | ||
| For each individual processing step, write a markdown cell explaining what you are doing. | ||
| By the end of each cell write code for showing the intermediate result: | ||
| * Use stackview.insight() for showing images. | ||
| * Use display() for showing dataframes. | ||
| * Use print() for any other results. | ||
| In the ipynb-json format you write, there is no output in any of the cells you write. | ||
| There are no images displayed in the notebook you write. | ||
| Your answer is in ipynb format. | ||
| """ | ||
|
|
||
| full_response = generate_response(chat_history=[], image=None, model=Context.model, system_prompt=system_prompt, user_prompt=prompt, vision_system_prompt="") | ||
|
|
||
| if filename is None: | ||
| filename_prompt = f""" | ||
| What would be a good filename for a notebook that answers the following prompt? | ||
| Prompt: | ||
| {prompt} | ||
| Respond with the filename and nothing else. | ||
| """ | ||
|
|
||
| filename = generate_response(chat_history=[], image=None, model=Context.model, system_prompt="", user_prompt=filename_prompt, vision_system_prompt="") | ||
| if not filename.endswith(".ipynb"): | ||
| filename += ".ipynb" | ||
|
|
||
| # check if file not exists and suffix a number if it does | ||
| if os.path.exists(filename): | ||
| i = 1 | ||
| while os.path.exists(filename): | ||
| filename = filename.replace(".ipynb", f"_{i}.ipynb") | ||
| i += 1 | ||
|
|
||
| full_response = full_response.strip().strip("\n").strip() | ||
|
|
||
| if full_response.startswith("```python"): | ||
| full_response = full_response[9:] | ||
| if full_response.startswith("```json"): | ||
| full_response = full_response[7:] | ||
| if full_response.startswith("```"): | ||
| full_response = full_response[3:] | ||
| if full_response.endswith("```"): | ||
| full_response = full_response[:-3] | ||
|
|
||
| # write the response to a file in utf-8 encoding | ||
| with open(filename, "w", encoding="utf-8") as file: | ||
| file.write(full_response) | ||
|
|
||
| return filename |
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