# Aider chat conversation:
USER: i want to render my github pages jekyll site locally. in the scripts/ dir, make me a dockerfile and scripts to build and run it.
ASSISTANT: To render your GitHub Pages Jekyll site locally using Docker, we need to create a Dockerfile and two scripts: one to build the Docker image and another to run the container.
Here are the necessary files:
1. `scripts/Dockerfile`
2. `scripts/build.sh`
3. `scripts/run.sh`
Let's create these files:
scripts/Dockerfile
```python
<<<<<<< SEARCH
=======
# Use the official Jekyll image from Docker Hub
FROM jekyll/jekyll:latest
# Set the working directory
WORKDIR /srv/jekyll
# Copy the current directory contents into the container at /srv/jekyll
COPY . /srv/jekyll
# Install any needed packages specified in Gemfile
RUN bundle install
# Expose port 4000 to the host
EXPOSE 4000
# Run Jekyll server
CMD ["jekyll", "serve", "--host", "0.0.0.0"]
>>>>>>> REPLACE
```
scripts/build.sh
```python
<<<<<<< SEARCH
=======
#!/bin/bash
# Build the Docker image
docker build -t my-jekyll-site -f scripts/Dockerfile .
>>>>>>> REPLACE
```
scripts/run.sh
```python
<<<<<<< SEARCH
=======
#!/bin/bash
# Run the Docker container
docker run --rm -v "$PWD:/srv/jekyll" -p 4000:4000 my-jekyll-site
>>>>>>> REPLACE
```
These scripts will allow you to build and run your Jekyll site locally using Docker. To use them, navigate to your project directory and run:
```sh
bash scripts/build.sh
bash scripts/run.sh
```
This will build the Docker image and start a container that serves your Jekyll site on `http://localhost:4000`.
# Aider chat conversation:
USER: draw a scatter plot of the edit_leaderboard models which have `released` dates. With x as the release date, y as the `pass_rate_2` and label each point with the `model` name field. use a chart style similar to the bar graph in the leaderboard.md, but we want a scatter plot for this one. put the scatter plot in the new blog post
ASSISTANT: Let's add the scatter plot to the new blog post in `_posts/2024-05-13-models-over-time.md`.
Here is the *SEARCH/REPLACE* block to add the scatter plot:
_posts/2024-05-13-models-over-time.md
```markdown
<<<<<<< SEARCH
# LLM code editing skill over time
=======
# LLM code editing skill over time
<canvas id="scatterPlot" width="800" height="450" style="margin-top: 20px"></canvas>
<script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
<script>
document.addEventListener('DOMContentLoaded', function () {
var ctx = document.getElementById('scatterPlot').getContext('2d');
var scatterData = {
datasets: [{
label: 'Model Performance',
data: [
{% for row in site.data.edit_leaderboard %}
{% if row.released %}
{
x: new Date('{{ row.released }}'),
y: {{ row.pass_rate_2 }},
label: '{{ row.model }}'
},
{% endif %}
{% endfor %}
],
backgroundColor: 'rgba(54, 162, 235, 0.2)',
borderColor: 'rgba(54, 162, 235, 1)',
borderWidth: 1,
pointRadius: 5,
pointHoverRadius: 7
}]
};
var scatterChart = new Chart(ctx, {
type: 'scatter',
data: scatterData,
options: {
scales: {
xAxes: [{
type: 'time',
time: {
unit: 'month',
tooltipFormat: 'll'
},
scaleLabel: {
display: true,
labelString: 'Release Date'
}
}],
yAxes: [{
scaleLabel: {
display: true,
labelString: 'Pass Rate 2 (%)'
},
ticks: {
beginAtZero: true
}
}]
},
tooltips: {
callbacks: {
label: function(tooltipItem, data) {
var label = data.datasets[tooltipItem.datasetIndex].data[tooltipItem.index].label || '';
return label + ': (' + tooltipItem.xLabel + ', ' + tooltipItem.yLabel + '%)';
}
}
}
}
});
});
</script>
>>>>>>> REPLACE
```
# Aider chat conversation:
USER: implement `find_similar_lines` which will fuzzy search the content for the original lines and return the contiguous chunk of lines from content which are the closest match
ASSISTANT: Here is the implementation of `find_similar_lines` to fuzzy search the content and return the closest matching contiguous chunk of lines:
aider/coders/editblock_coder.py
<source>python
<<<<<<< SEARCH
#did_you_mean = find_similar_lines(original, content)
=======
did_you_mean = find_similar_lines(original, content)
>>>>>>> REPLACE
</source>
aider/coders/editblock_coder.py
<source>python
<<<<<<< SEARCH
=======
def find_similar_lines(search_lines, content_lines, threshold=0.8):
search_lines = search_lines.splitlines()
content_lines = content_lines.splitlines()
best_ratio = 0
best_match = None
for i in range(len(content_lines) - len(search_lines) + 1):
chunk = content_lines[i:i+len(search_lines)]
ratio = SequenceMatcher(None, search_lines, chunk).ratio()
if ratio > best_ratio:
best_ratio = ratio
best_match = chunk
if best_ratio >= threshold:
return "\n".join(best_match)
else:
return ""
>>>>>>> REPLACE
</source>
This adds the `find_similar_lines` function which:
1. Splits the search lines and content lines on newlines
2. Iterates through the content lines, considering each contiguous chunk the same length as the search lines
3. Calculates the SequenceMatcher ratio between the search lines and each chunk
4. Keeps track of the chunk with the highest ratio
5. If the best ratio is above the threshold (default 0.8), it returns the best matching chunk joined back into a string
6. Otherwise it returns an empty string to indicate no close enough match was found
It also uncomments the line to call `find_similar_lines` and store the result in `did_you_mean`.
Let me know if you have any other questions!
# Aider chat conversation:
USER: I did `git reset --hard HEAD~1` to discard the last edits. Please wait for further instructions before attempting that change again. Feel free to ask relevant questions about why the changes were reverted.
ASSISTANT: I understand you reverted the previous changes. Before I attempt to implement the `find_similar_lines` function again, I have a few questions to ensure I follow your guidelines correctly:
1. When proposing changes, should I only modify the `aider/coders/editblock_coder.py` file since that is the only one you have explicitly added to the chat
^C KeyboardInterrupt
USER: implement `find_similar_text` which will fuzzy search the content for the original text and return the part of content which is the closest match
ASSISTANT: Here is the implementation of `find_similar_text` to fuzzy search the content for the closest match to the original text:
aider/coders/editblock_coder.py
<source>python
<<<<<<< SEARCH
=======
from difflib import SequenceMatcher
def find_similar_text(original, content):
content_len = len(content)
original_len = len(original)
best_ratio = 0
best_match = None
for i in range(content_len - original_len + 1):
chunk = content[i:i+original_len]
ratio = SequenceMatcher(None, original, chunk).ratio()
if ratio > best_ratio:
best_ratio = ratio
best_match = chunk
return best_match
>>>>>>> REPLACE
</source>
This function:
1. Iterates through the content, considering each chunk of text the same length as the original
2. Calculates the SequenceMatcher ratio between the original text and each chunk
3. Keeps track of the chunk with the highest ratio
4. Returns the chunk of content that best matches the original text
It will return the portion of `content` that is the closest fuzzy match to `original`.
Let me know if you have any other questions!
