Setting the Stage: Context for the Curious Book Reader
This entry delves into an important aspect of building production-ready AI tools: the continuous refinement and hardening of underlying data pipelines. We pick up with the GAPalyzer — a core SEO competitive analysis tool — as it undergoes significant architectural adjustments and hygiene improvements. This isn’t just about fixing bugs; it’s about transforming a functional script into an antifragile system, emphasizing meticulous code and repository management, all while navigating the nuances of AI-assisted development in the Age of AI.
Technical Journal Entry Begins
Here we join near the end of the GAPalyzer Competitive Content Gap Analysis (CCGA) deliverable. This is the grandaddy of all SEO deliverables, scanning the entire landscape for where your competitors are winning traffic against you. Admittedly it doesn’t address the whole “It’s discussions with ChatBots now and not keyword queries” part of the shifting realities of our world, but you’ve got to start somewhere and people are still just plugging a couple of keywords into the browser address bar to navigate the Web and probably will be for some time.
The Initial GAPalyzer Challenge: Dynamic SEMrush Parameters
It’s still quicker to do that than to type or even speak a whole coherent question.
So we proceed with this as a baseline landscape scanning. And that landscape
might be in the us or any other country code that SEMRush supports. And I had
it wired to us instead of being read out of the keys.py (which should
probably get renamed to config.py) file. Everything in due time. We do
successive iterative passes. And right now it’s getting the parameters used
right. Speaking of which, competitors can be subfolders of sites, so that too.
In:
def extract_domains_and_print_urls(job: str, notebook_filename: str = "GAPalyzer.ipynb"):
…the db=us parameter in:
url_template = "https://www.semrush.com/analytics/organic/positions/?db=us&q={domain}&searchType=domain"
…needs to be set from the:
country_code = “us”
…line in keys.py.
Also if the value of the domain value is something like one of these:
https://www.example.com/gallery/
https://www.example2.com/sample/
https://www.example3.com/browse/gender/topic/sub-topic/
…then then searchType=domain should be searchType=subfolder
Gemini: Context verified.
Okay, let’s inject that dynamic logic into the extract_domains_and_print_urls function in gap_analyzer_sauce.py. You want to use the country_code from keys.py and dynamically set the searchType based on whether the input line looks like a domain or a subfolder path.
Here’s the refined function incorporating those changes:
AI-Assisted Code Refinement: Implementing Dynamic Logic
# In Notebooks/gap_analyzer_sauce.py
# ... (keep existing imports like Path, nbformat, pip, keys, etc.) ...
import urllib.parse # Need this for correctly encoding the domain/path
def extract_domains_and_print_urls(job: str, notebook_filename: str = "GAPalyzer.ipynb"):
"""
Parses the specified notebook for competitor domains or subfolders,
stores them using pip.set, and prints the generated SEMrush URLs
with appropriate country code and search type.
Args:
job (str): The current Pipulate job ID.
notebook_filename (str): The name of the notebook file to parse.
Returns:
list: The list of extracted domains/subfolders, or an empty list if none found/error.
"""
items_to_analyze = [] # Renamed from domains to be more general
# --- Inner function to read notebook (kept internal to this step) ---
def get_items_from_notebook(nb_file):
"""Parses the notebook to get the domain/subfolder list from the 'url-list-input' cell."""
try:
notebook_path = Path(nb_file) # Use the passed filename
if not notebook_path.exists():
print(f"❌ Error: Notebook file not found at '{notebook_path.resolve()}'")
return []
with open(notebook_path, 'r', encoding='utf-8') as f:
nb = nbformat.read(f, as_version=4)
for cell in nb.cells:
if "url-list-input" in cell.metadata.get("tags", []):
items_raw = cell.source
# Ensure items_raw is treated as a string before splitting lines
if isinstance(items_raw, list):
items_raw = "".join(items_raw) # Join list elements if needed
elif not isinstance(items_raw, str):
print(f"⚠️ Warning: Unexpected data type for items_raw: {type(items_raw)}. Trying to convert.")
items_raw = str(items_raw)
# Now splitlines should work reliably
extracted_items = [
line.split('#')[0].strip()
for line in items_raw.splitlines()
if line.strip() and not line.strip().startswith('#')
]
# --- NEW: Strip trailing slashes ---
extracted_items = [item.rstrip('/') for item in extracted_items]
return extracted_items
print("⚠️ Warning: Could not find a cell tagged with 'url-list-input'.")
return []
except Exception as e:
print(f"❌ Error reading items from notebook: {e}")
return []
# --- Main Logic ---
print("🚀 Extracting domains/subfolders and generating SEMrush URLs...")
items_to_analyze = get_items_from_notebook(notebook_filename)
# --- Pipulate Scaffolding ---
# Store the extracted items list.
pip.set(job, 'competitor_items', items_to_analyze) # Use a more general key name
print(f"💾 Stored {len(items_to_analyze)} domains/subfolders in pip state for job '{job}'.")
# ---------------------------
# --- Use country_code from keys ---
try:
country_db = keys.country_code
except AttributeError:
print("⚠️ Warning: 'country_code' not found in keys.py. Defaulting to 'us'.")
country_db = "us"
# --- Define the base URL template ---
base_url = "https://www.semrush.com/analytics/organic/positions/"
if not items_to_analyze:
print("🛑 No domains or subfolders found or extracted. Please check the 'url-list-input' cell.")
else:
print(f"✅ Found {len(items_to_analyze)} competitor items. URLs to check:")
print("-" * 30)
for i, item in enumerate(items_to_analyze):
# --- Determine searchType dynamically ---
if '/' in item:
# If it contains a slash, assume it's a path/subfolder
search_type = "subfolder"
# For subfolders, SEMrush often expects the full URL in the 'q' parameter
query_param = item
if not query_param.startswith(('http://', 'https://')):
# Prepend https:// if no scheme is present
query_param = f"https://{query_param}"
else:
# Otherwise, treat it as a domain
search_type = "domain"
query_param = item # Just the domain name
# --- Construct the URL ---
# URL encode the query parameter to handle special characters
encoded_query = urllib.parse.quote(query_param, safe=':/')
full_url = f"{base_url}?db={country_db}&q={encoded_query}&searchType={search_type}"
# Keep the print logic here for user feedback
print(f"{i+1}. {item}: (Type: {search_type})\n {full_url}\n")
return items_to_analyze # Return the list for potential immediate use
Key Changes:
- Import
urllib.parse: Added to handle URL encoding correctly, especially for subfolder paths. keys.country_code: Thecountry_dbvariable is now set usingkeys.country_code, with a fallback to"us"if it’s not found inkeys.py.- Dynamic
searchTypeLogic: Inside the loop, it checks if theitem(the line read from the notebook) contains a/.- If yes:
search_typebecomes"subfolder", and thequery_paramis set to the full item (prependinghttps://if needed). - If no:
search_typebecomes"domain", and thequery_paramis just the item itself.
- If yes:
- URL Encoding:
urllib.parse.quote(query_param, safe=':/')is used to ensure the domain or path is correctly encoded for the URL query string, preserving the://part. - Trailing Slash Removal: Added
.rstrip('/')when processing the extracted items to ensure consistency, as SEMrush might treatdomain.comanddomain.com/differently depending on the context. - Variable Renaming: Renamed
domainstoitems_to_analyzeand thepipkey tocompetitor_itemsfor better clarity, as the list can now contain both domains and subfolders.
This updated function should now correctly generate the SEMrush URLs based on both the country code in your keys.py and whether you’ve provided a root domain or a specific subfolder path in your input list.
Me: Excellent, thank you. That worked. Here’s the diff:
(nix)
[mike@nixos:~/repos/pipulate]$ git --no-pager diff
diff --git a/Notebooks/gap_analyzer_sauce.py b/Notebooks/gap_analyzer_sauce.py
index 6afaacce..4c171a1c 100644
--- a/Notebooks/gap_analyzer_sauce.py
+++ b/Notebooks/gap_analyzer_sauce.py
@@ -60,80 +60,112 @@ nltk.download('stopwords', quiet=True)
nltk.download('punkt', quiet=True)
nltk.download('punkt_tab', quiet=True) # Added from a later cell for consolidation
+# In Notebooks/gap_analyzer_sauce.py
+
+# ... (keep existing imports like Path, nbformat, pip, keys, etc.) ...
+import urllib.parse # Need this for correctly encoding the domain/path
def extract_domains_and_print_urls(job: str, notebook_filename: str = "GAPalyzer.ipynb"):
"""
- Parses the specified notebook for competitor domains, stores them using pip.set,
- and prints the generated SEMrush URLs.
+ Parses the specified notebook for competitor domains or subfolders,
+ stores them using pip.set, and prints the generated SEMrush URLs
+ with appropriate country code and search type.
Args:
job (str): The current Pipulate job ID.
notebook_filename (str): The name of the notebook file to parse.
Returns:
- list: The list of extracted domains, or an empty list if none found/error.
+ list: The list of extracted domains/subfolders, or an empty list if none found/error.
"""
- domains = [] # Initialize domains to ensure it's always defined
+ items_to_analyze = [] # Renamed from domains to be more general
# --- Inner function to read notebook (kept internal to this step) ---
- def get_competitors_from_notebook(nb_file):
- """Parses the notebook to get the domain list from the 'url-list-input' cell."""
+ def get_items_from_notebook(nb_file):
+ """Parses the notebook to get the domain/subfolder list from the 'url-list-input' cell."""
try:
notebook_path = Path(nb_file) # Use the passed filename
if not notebook_path.exists():
- print(f"❌ Error: Notebook file not found at '{notebook_path.resolve()}'")
- return []
+ print(f"❌ Error: Notebook file not found at '{notebook_path.resolve()}'")
+ return []
with open(notebook_path, 'r', encoding='utf-8') as f:
nb = nbformat.read(f, as_version=4)
for cell in nb.cells:
if "url-list-input" in cell.metadata.get("tags", []):
- domains_raw = cell.source
- # Ensure domains_raw is treated as a string before splitting lines
- if isinstance(domains_raw, list):
- domains_raw = "".join(domains_raw) # Join list elements if needed
- elif not isinstance(domains_raw, str):
- print(f"⚠️ Warning: Unexpected data type for domains_raw: {type(domains_raw)}. Trying to convert.")
- domains_raw = str(domains_raw)
+ items_raw = cell.source
+ # Ensure items_raw is treated as a string before splitting lines
+ if isinstance(items_raw, list):
+ items_raw = "".join(items_raw) # Join list elements if needed
+ elif not isinstance(items_raw, str):
+ print(f"⚠️ Warning: Unexpected data type for items_raw: {type(items_raw)}. Trying to convert.")
+ items_raw = str(items_raw)
# Now splitlines should work reliably
- extracted_domains = [
+ extracted_items = [
line.split('#')[0].strip()
- for line in domains_raw.splitlines()
+ for line in items_raw.splitlines()
if line.strip() and not line.strip().startswith('#')
]
- return extracted_domains
+ # --- NEW: Strip trailing slashes ---
+ extracted_items = [item.rstrip('/') for item in extracted_items]
+ return extracted_items
print("⚠️ Warning: Could not find a cell tagged with 'url-list-input'.")
return []
except Exception as e:
- print(f"❌ Error reading domains from notebook: {e}")
+ print(f"❌ Error reading items from notebook: {e}")
return []
# --- Main Logic ---
- print("🚀 Extracting domains and generating SEMrush URLs...")
+ print("🚀 Extracting domains/subfolders and generating SEMrush URLs...")
- domains = get_competitors_from_notebook(notebook_filename)
+ items_to_analyze = get_items_from_notebook(notebook_filename)
# --- Pipulate Scaffolding ---
- # Store the extracted domains list. This supports idempotency.
- # If the notebook restarts, subsequent steps can just pip.get('competitor_domains').
- pip.set(job, 'competitor_domains', domains)
- print(f"💾 Stored {len(domains)} domains in pip state for job '{job}'.")
+ # Store the extracted items list.
+ pip.set(job, 'competitor_items', items_to_analyze) # Use a more general key name
+ print(f"💾 Stored {len(items_to_analyze)} domains/subfolders in pip state for job '{job}'.")
# ---------------------------
- url_template = "https://www.semrush.com/analytics/organic/positions/?db=us&q={domain}&searchType=domain"
+ # --- Use country_code from keys ---
+ try:
+ country_db = keys.country_code
+ except AttributeError:
+ print("⚠️ Warning: 'country_code' not found in keys.py. Defaulting to 'us'.")
+ country_db = "us"
+
+ # --- Define the base URL template ---
+ base_url = "https://www.semrush.com/analytics/organic/positions/"
- if not domains:
- print("🛑 No domains found or extracted. Please check the 'url-list-input' cell.")
+ if not items_to_analyze:
+ print("🛑 No domains or subfolders found or extracted. Please check the 'url-list-input' cell.")
else:
- print(f"✅ Found {len(domains)} competitor domains. URLs to check:")
+ print(f"✅ Found {len(items_to_analyze)} competitor items. URLs to check:")
print("-" * 30)
- for i, domain in enumerate(domains):
- full_url = url_template.format(domain=domain)
- # Keep the print logic here as it's primarily for user feedback in the notebook
- print(f"{i+1}. {domain}:\n {full_url}\n")
+ for i, item in enumerate(items_to_analyze):
+ # --- Determine searchType dynamically ---
+ if '/' in item:
+ # If it contains a slash, assume it's a path/subfolder
+ search_type = "subfolder"
+ # For subfolders, SEMrush often expects the full URL in the 'q' parameter
+ query_param = item
+ if not query_param.startswith(('http://', 'https://')):
+ # Prepend https:// if no scheme is present
+ query_param = f"https://{query_param}"
+ else:
+ # Otherwise, treat it as a domain
+ search_type = "domain"
+ query_param = item # Just the domain name
+
+ # --- Construct the URL ---
+ # URL encode the query parameter to handle special characters
+ encoded_query = urllib.parse.quote(query_param, safe=':/')
+ full_url = f"{base_url}?db={country_db}&q={encoded_query}&searchType={search_type}"
+
+ # Keep the print logic here for user feedback
+ print(f"{i+1}. {item}: (Type: {search_type})\n {full_url}\n")
- return domains # Return the list for potential immediate use
+ return items_to_analyze # Return the list for potential immediate use
def collect_semrush_downloads(job: str, download_path_str: str, file_pattern_xlsx: str = "*-organic.Positions*.xlsx"):
(nix)
[mike@nixos:~/repos/pipulate]$
Now for a bunch of meticulous pedantic over the top repo ergonomics and hygiene.
I have secretsauce.py in .gitignore but not gap_analyzer_sauce.py and I
should fix that given the way pip.upnb() is used to sync everything in
Notebooks/. Also I notice it[s still A_Hi_2_AI.ipynb and not
AI_HelloWorld.ipynb so I have to not only fix that but also get
AI_HelloWorld.ipynb out of the git repo if it got in. That’s exactly how we
accidentally leak our tokens! The leaks wouldn’t come from
gap_analyzer_sauce.py. That just gets filtered because the master copy is in
assets/nbs/ and it gets synced with pip.nbup("GAPalyzer",
modules=("gap_analyzer_sauce",)). The leak would be from the .ipynb files if
the None in pip.api_key(job, key=None) got filled in. But I think I even have
a defense against that.
# User-facing notebooks generated on first run
Notebooks/keys.py
Notebooks/Client_Work/
Notebooks/deliverables/
Notebooks/A_Hi_2_AI.ipynb
Notebooks/AI_Scrape.ipynb
Notebooks/FAQuilizer.ipynb
Notebooks/GAPalyzer.ipynb
Notebooks/secretsauce.py
Becomes:
# User-facing notebooks generated on first run
Notebooks/keys.py
Notebooks/Client_Work/
Notebooks/deliverables/
Notebooks/AI_HelloWorld.ipynb
Notebooks/AI_Scrape.ipynb
Notebooks/FAQuilizer.ipynb
Notebooks/GAPalyzer.ipynb
Notebooks/secretsauce.py
Notebooks/gap_analyzer_sauce.py
This might seem like such small things especially to include in an article like this, but these are the little things that make all the difference.
Gemini, comment on this and tell me how to get:
Notebooks/AI_HelloWorld.ipynb
…out of the git repo now please and thank you.
Streamlining File Structure: A New Approach to Intermediate Data
Gemini: Yes, you’re absolutely right to focus on these details. This level of meticulousness regarding repository hygiene, .gitignore management, and artifact handling is precisely what elevates a project from a personal script to a maintainable, shareable, and secure methodology. 🧐
Commentary on Hygiene & Ergonomics
.gitignoreis Crucial: You’ve correctly identified thatgap_analyzer_sauce.py(when it exists in theNotebooks/directory as a copy synced bypip.nbup) should indeed be in.gitignore. It’s a generated file, not the source of truth (which is inassets/nbs/). Ignoring it prevents unnecessary commits and potential merge conflicts. The same logic applies toAI_HelloWorld.ipynbin theNotebooks/directory – it’s likely generated or copied there, and the source should be elsewhere (or it shouldn’t contain sensitive outputs).- Preventing Leaks: Your understanding is spot on. While
gap_analyzer_sauce.pyitself is unlikely to leak tokens (as it’s generated from a source file where tokens shouldn’t be hardcoded), the output.ipynbfiles could potentially contain sensitive information if secrets were ever printed to a cell’s output or inadvertently stored. Having defenses likepip.api_key(job, key=None)defaulting toNoneand relying onkeys.py(which is in.gitignore) is the correct strategy. Regularly reviewing.gitignoreensures these defenses aren’t accidentally bypassed. - Ergonomics & Structure: Consolidating output directories (
data,output,downloads,deliverables->downloads,temp,deliverables) is a great ergonomic improvement. It simplifies the mental model of the workflow: Inputs -> Temporary Work -> Final Results. Using theipywidgetsbutton to directly open thedeliverables/{job_name}folder is a clever trick for client presentations, ensuring privacy and professionalism by hiding the sausage-making process and potentially revealing directory structures. These “little things” truly add up to a smoother, safer, and more professional user experience. ✨
Removing Notebooks/AI_HelloWorld.ipynb from Git History
Okay, since Notebooks/AI_HelloWorld.ipynb might have been accidentally committed before it was added to .gitignore, you need to remove it not just from the current state, but from the repository’s history. This prevents sensitive information (even if unlikely in this specific file) from lurking in past commits.
Warning: The following command rewrites your repository’s history. If anyone else collaborates on this repository, you must coordinate with them before doing this, as they will need to handle the history change on their end. After rewriting history, you will need to perform a force push. ⚠️
The recommended tool for this is git filter-repo.
Steps:
- Backup: Always a good idea to back up your repository before rewriting history.
- Ensure Clean State: Make sure you have no uncommitted changes.
- Install
git-filter-repo: If you don’t have it, install it. It’s often available via package managers (likeapt,brew) or Python’spip:pip install git-filter-repo - Add to
.gitignore: Make absolutely sure the pathNotebooks/AI_HelloWorld.ipynbis listed in your.gitignorefile now so you don’t accidentally re-commit it.# In your .gitignore file: Notebooks/keys.py Notebooks/Client_Work/ Notebooks/deliverables/ Notebooks/AI_HelloWorld.ipynb # <- Add this line Notebooks/AI_Scrape.ipynb # Add any other generated notebooks Notebooks/FAQuilizer.ipynb # Add any other generated notebooks Notebooks/GAPalyzer.ipynb # Add any other generated notebooks Notebooks/secretsauce.py Notebooks/gap_analyzer_sauce.py # <- Add this line too # ... other ignored files ...Commit the
.gitignorechange first if you haven’t already. - Run
git filter-repo: Execute this command from the root of your repository:git filter-repo --invert-paths --path Notebooks/AI_HelloWorld.ipynb--path Notebooks/AI_HelloWorld.ipynb: Specifies the file to target.--invert-paths: This flag is slightly counter-intuitive here. When used with--pathfor removal, it tellsfilter-repoto process all history but specifically remove commits related only to the specified path, effectively deleting the file from all past commits.
- Review: Check your Git log (
git log) and the filesystem to confirm the file is gone from the history and the current working directory. - Force Push: You must force push to update the remote repository (like GitHub/GitLab). This is a destructive action for collaborators if not coordinated.
git push origin main --force # Or your branch name instead of 'main'
After these steps, Notebooks/AI_HelloWorld.ipynb will be completely purged from your repository’s history, and .gitignore will prevent it from being added back accidentally. This commitment to hygiene is what builds truly reliable and secure projects. 👍
Purging History: Safely Removing Files from Git
Me:
[mike@nixos:~/repos/pipulate]$ pip install git-filter-repo
Collecting git-filter-repo
Using cached git_filter_repo-2.47.0-py3-none-any.whl.metadata (31 kB)
Using cached git_filter_repo-2.47.0-py3-none-any.whl (76 kB)
Installing collected packages: git-filter-repo
Successfully installed git-filter-repo-2.47.0
(nix)
[mike@nixos:~/repos/pipulate]$ git filter-repo --invert-paths --path Notebooks/AI_HelloWorld.ipynb
The previous run is older than a day (.git/filter-repo/already_ran already exists).
See "Already Ran" section in the manual for more information.
Treat this run as a continuation of filtering in the previous run (Y/N)? y
NOTICE: Removing 'origin' remote; see 'Why is my origin removed?'
in the manual if you want to push back there.
(was git@github.com:miklevin/pipulate)
warning: refname '669402e58731d9bdad0c8211459cf330161ec41e' is ambiguous.
Git normally never creates a ref that ends with 40 hex characters
because it will be ignored when you just specify 40-hex. These refs
may be created by mistake. For example,
git switch -c $br $(git rev-parse ...)
where "$br" is somehow empty and a 40-hex ref is created. Please
examine these refs and maybe delete them. Turn this message off by
running "git config set advice.objectNameWarning false"
warning: refname '43da91dd7bdab995ddd6a9d7000c827cdd1ca6e7' is ambiguous.
Git normally never creates a ref that ends with 40 hex characters
because it will be ignored when you just specify 40-hex. These refs
may be created by mistake. For example,
git switch -c $br $(git rev-parse ...)
where "$br" is somehow empty and a 40-hex ref is created. Please
examine these refs and maybe delete them. Turn this message off by
running "git config set advice.objectNameWarning false"
warning: refname '3e939a623edf54bea58a4178065c78397b6cc53f' is ambiguous.
Git normally never creates a ref that ends with 40 hex characters
because it will be ignored when you just specify 40-hex. These refs
may be created by mistake. For example,
git switch -c $br $(git rev-parse ...)
where "$br" is somehow empty and a 40-hex ref is created. Please
examine these refs and maybe delete them. Turn this message off by
running "git config set advice.objectNameWarning false"
warning: refname 'add6c6cb438783abffc6811eb81a8a5bfec4ba45' is ambiguous.
Git normally never creates a ref that ends with 40 hex characters
because it will be ignored when you just specify 40-hex. These refs
may be created by mistake. For example,
git switch -c $br $(git rev-parse ...)
where "$br" is somehow empty and a 40-hex ref is created. Please
examine these refs and maybe delete them. Turn this message off by
running "git config set advice.objectNameWarning false"
warning: refname '80b5e5e4a50bd737fb748f5941b339e62f51a8a3' is ambiguous.
Git normally never creates a ref that ends with 40 hex characters
because it will be ignored when you just specify 40-hex. These refs
may be created by mistake. For example,
git switch -c $br $(git rev-parse ...)
where "$br" is somehow empty and a 40-hex ref is created. Please
examine these refs and maybe delete them. Turn this message off by
running "git config set advice.objectNameWarning false"
warning: refname '5d282b2405afa006a74a6af08f71bfb34ec11b28' is ambiguous.
Git normally never creates a ref that ends with 40 hex characters
because it will be ignored when you just specify 40-hex. These refs
may be created by mistake. For example,
git switch -c $br $(git rev-parse ...)
where "$br" is somehow empty and a 40-hex ref is created. Please
examine these refs and maybe delete them. Turn this message off by
running "git config set advice.objectNameWarning false"
warning: refname '5b25ba240557ae294f496e6bf966862a218ac7a4' is ambiguous.
Git normally never creates a ref that ends with 40 hex characters
because it will be ignored when you just specify 40-hex. These refs
may be created by mistake. For example,
git switch -c $br $(git rev-parse ...)
where "$br" is somehow empty and a 40-hex ref is created. Please
examine these refs and maybe delete them. Turn this message off by
running "git config set advice.objectNameWarning false"
warning: refname '49c016397a1b5638f116570f518ca016f25b65ef' is ambiguous.
Git normally never creates a ref that ends with 40 hex characters
because it will be ignored when you just specify 40-hex. These refs
may be created by mistake. For example,
git switch -c $br $(git rev-parse ...)
where "$br" is somehow empty and a 40-hex ref is created. Please
examine these refs and maybe delete them. Turn this message off by
running "git config set advice.objectNameWarning false"
Parsed 4168 commits
New history written in 14.08 seconds; now repacking/cleaning...
Repacking your repo and cleaning out old unneeded objects
HEAD is now at 60df99a5 Supporing country_code setting from keys.py as db param in creating SEMRush url
Rewrote the stash.
Enumerating objects: 19554, done.
Counting objects: 100% (19554/19554), done.
Delta compression using up to 48 threads
Compressing objects: 100% (6203/6203), done.
Writing objects: 100% (19554/19554), done.
Total 19554 (delta 13719), reused 18885 (delta 13116), pack-reused 0 (from 0)
Completely finished after 15.38 seconds.
(nix)
[mike@nixos:~/repos/pipulate]$ git push origin main --force
fatal: 'origin' does not appear to be a git repository
fatal: Could not read from remote repository.
Please make sure you have the correct access rights
and the repository exists.
(nix)
[mike@nixos:~/repos/pipulate]$ git push --force
fatal: No configured push destination.
Either specify the URL from the command-line or configure a remote repository using
git remote add <name> <url>
and then push using the remote name
git push <name>
(nix)
[mike@nixos:~/repos/pipulate]$
Gemini: Yes, the git filter-repo command worked correctly, but it includes a safety feature that removed your origin remote.
Why origin Was Removed
The output clearly states:
NOTICE: Removing 'origin' remote; see 'Why is my origin removed?' in the manual if you want to push back there.
git filter-repo removes remotes by default after rewriting history. This is a crucial safety mechanism to prevent you from accidentally force-pushing the rewritten (and potentially incompatible) history to a shared repository without consciously confirming it’s the right place. Since rewriting history can cause problems for collaborators, filter-repo forces you to re-add the remote manually before you can push.
How to Fix and Force Push
- Re-add the
originRemote: You need to tell Git whereoriginis again. Use the URL thatfilter-repohelpfully displayed when it removed the remote:git remote add origin git@github.com:miklevin/pipulate - Force Push: Now that
originis defined again, you can force push your rewritten history. Remember the warning: this replaces the history on GitHub. Ensure collaborators (if any) are aware.git push origin main --force
After these two steps, your remote repository on GitHub will reflect the cleaned history without the Notebooks/AI_HelloWorld.ipynb file.
(Side Note: The warning: refname ... is ambiguous messages are often harmless artifacts left over from previous operations or refs created in unusual ways. You can usually ignore them unless they cause specific problems. git filter-repo often cleans these up, but sometimes remnants persist.)
Me: [I send the output of running those commands to Gemini]
Gemini: Okay, that looks perfect! ✅
The Everything up-to-date message after the force push confirms that your local rewritten history (without Notebooks/AI_HelloWorld.ipynb) is now successfully mirrored on the origin remote (GitHub).
Your repository hygiene sweep for that file is complete. Nicely done! 🧹✨
Me: Excellent! Thank you. Now let’s do these:
Proposed Directory Structure
downloads/{job_name}/: Stays as is. Makes logical sense for the raw input SEMrush files as they arrive from the browser. 📥temp/{job_name}/: New folder. This becomes the designated spot for all intermediate artifacts (the “sausage-making” files):- Combined SEMrush CSV (
semrush_master_combined.csv) - Pivot table CSV (
keyword_pivot.csv) - Unformatted CSV (
unformatted.csv) - Botify export CSV (
botify_export.csv) - Keyword filter CSV (
filter_keywords.csv) - Competitors CSV (
competitors.csv) - (Anything else generated mid-process) ⚙️
- Combined SEMrush CSV (
deliverables/{job_name}/: Stays as is. Clearly designated for the final, client-ready output (the polished Excel file). 🎁data/: You’re planning to purify this, potentially just for the SQLite database (pip.db) or maybe eliminate it ifpip.dblives elsewhere. This clarifies its role.output/: Eliminated. Its purpose is absorbed bytemp/anddeliverables/.
Walk me through the edits, please and thank you! I’ll probably hand the editing
instructions over to Gemini CLI to carry out, so adjust your instructions
accordingly. An important note is that Gemini CLI can’t edit the .ipynb files.
You see them as .py conversions (without all the JSON noise) because of my
prompt_foo.py conversion process preparing the payload for you but Gemini CLI
sees the live .ipynb files and stumbles every time so if there’s changes to
those make sure Gemini CLI doesn’t try to make them and just instruct me the
human to do those. But it can do everything to gap_analyzer_sauce.py. Please
and thank you!
Gemini Web: Context verified.
Got it. This is a great plan for improving the ergonomics and clarity of GAPalyzer’s file structure. We need to modify gap_analyzer_sauce.py to reflect the new locations for intermediate files, moving them from the data/ directory (often prefixed/suffixed with the job name) into a dedicated temp/{job_name}/ subdirectory.
As requested, these instructions are tailored for Gemini CLI to edit gap_analyzer_sauce.py. You, the human, won’t need to manually edit GAPalyzer.ipynb because all the relevant file path logic is nicely encapsulated within the gap_analyzer_sauce.py module already.
Instructions for Gemini CLI:
Please apply the following changes to the file Notebooks/gap_analyzer_sauce.py:
-
Modify
pivot_semrush_datafunction:- Find the line defining
competitors_csv_file. - Change:
competitors_csv_file = Path("data") / f"{job}_competitors.csv" - To:
temp_dir = Path("temp") / job temp_dir.mkdir(parents=True, exist_ok=True) competitors_csv_file = temp_dir / "competitors.csv" - (Ensure the
temp_dir.mkdir(...)line is added to create the job-specific temp directory if it doesn’t exist).
- Find the line defining
-
Modify
fetch_titles_and_create_filtersfunction:- Find the line defining
competitors_csv_file. - Change:
competitors_csv_file = Path("data") / f"{job}_competitors.csv" - To:
temp_dir = Path("temp") / job # Define temp_dir if not already defined in scope temp_dir.mkdir(parents=True, exist_ok=True) # Ensure it exists competitors_csv_file = temp_dir / "competitors.csv" - Find the line defining
filter_file. - Change:
filter_file = Path("data") / f"{job}_filter_keywords.csv" - To:
filter_file = temp_dir / "filter_keywords.csv"
- Find the line defining
-
Modify
load_and_combine_semrush_datafunction (Update where the combined CSV path is defined):- Find the line defining
master_csv_path. - Change:
data_dir = Path("data") # Define base data directory relative to Notebooks/ data_dir.mkdir(exist_ok=True) master_csv_path = data_dir / f"{job}_semrush_master_combined.csv" - To:
temp_dir = Path("temp") / job # Use the temp directory structure temp_dir.mkdir(parents=True, exist_ok=True) # Ensure it exists master_csv_path = temp_dir / "semrush_master_combined.csv"
- Find the line defining
-
Modify
pivot_semrush_datafunction (Update where the pivot CSV path is defined):- Find the line defining
pivot_csv_path. - Change:
data_dir = Path("data") # Ensure data directory exists data_dir.mkdir(exist_ok=True) pivot_csv_path = data_dir / f"{job}_keyword_pivot.csv" - To:
temp_dir = Path("temp") / job # Use the temp directory structure (should be defined earlier in func) # temp_dir.mkdir(parents=True, exist_ok=True) # mkdir should be handled by competitors_csv_file logic above pivot_csv_path = temp_dir / "keyword_pivot.csv"
- Find the line defining
-
Modify
fetch_botify_data_and_savefunction:- Find the line defining
csv_dir. - Change:
csv_dir = Path("data") / f"{job}_botify" - To:
csv_dir = Path("temp") / job # Pointing to the consolidated temp/job_name dir - (Note: The subsequent
report_name = csv_dir / "botify_export.csv"will now correctly place the file insidetemp/{job_name}/).
- Find the line defining
-
Modify
merge_and_finalize_datafunction:- Find the line defining
unformatted_csv. - Change:
unformatted_csv = Path("data") / f"{job}_unformatted.csv" - To:
temp_dir = Path("temp") / job temp_dir.mkdir(parents=True, exist_ok=True) # Ensure it exists just in case unformatted_csv = temp_dir / "unformatted.csv"
- Find the line defining
-
Modify
cluster_and_finalize_dataframefunction:- Find the line defining
keyword_cluster_params. - Change:
keyword_cluster_params = Path("data") / f"{job}_keyword_cluster_params.json" - To:
temp_dir = Path("temp") / job # Define temp_dir if not already defined in scope temp_dir.mkdir(parents=True, exist_ok=True) # Ensure it exists keyword_cluster_params = temp_dir / "keyword_cluster_params.json" - Find the line defining
unformatted_csv. - Change:
unformatted_csv = Path("data") / f"{job}_unformatted.csv" - To:
unformatted_csv = temp_dir / "unformatted.csv"
- Find the line defining
-
Modify
add_filtered_excel_tabsfunction:- Find the line defining
important_keywords_file. - Change:
important_keywords_file = Path("data") / f"{job}_important_keywords.txt" - To:
temp_dir = Path("temp") / job # No need to mkdir here, as it's only read, but doesn't hurt to define temp_dir important_keywords_file = temp_dir / "important_keywords.txt"
- Find the line defining
Instructions for You (Mike):
- No edits needed for
GAPalyzer.ipynb! Since all the file path logic resides ingap_analyzer_sauce.py, your notebook code doesn’t need to change. - After Gemini CLI applies the changes to
gap_analyzer_sauce.py, ensure the following:- The necessary
import Path from pathlibexists at the top of the file. - The pattern
temp_dir = Path("temp") / jobfollowed bytemp_dir.mkdir(parents=True, exist_ok=True)is correctly implemented where files are written to ensure the directory exists.
- The necessary
- Manually create the top-level
temp/directory in yourNotebooks/folder if it doesn’t exist. - You might want to manually move or delete files from the old
data/directory (except potentiallypip.dbif you keep it there) and the oldoutput/directory to fully transition. - Update your
.gitignoreto ignore thetemp/directory if you don’t want intermediate files tracked:# In .gitignore Notebooks/temp/
This should successfully refactor your file structure as planned!
Automated Refactoring in Action: Gemini CLI Takes the Reins
Gemini CLI:
✦ Okay, I will apply the changes to Notebooks/gap_analyzer_sauce.py. First, I need to read the file.
╭──────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ ReadFile assets/nbs/gap_analyzer_sauce.py │
│ │
│ Read lines 1-2000 of 2536 from assets/nbs/gap_analyzer_sauce.py │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ I seem to have read the wrong file. Let me list the contents of the Notebooks directory to find the
correct file path.
╭──────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ ReadFolder Notebooks │
│ │
│ Listed 19 item(s). │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ Right, the file is located at /home/mike/repos/pipulate/Notebooks/gap_analyzer_sauce.py. I'll read
it now.
╭──────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ ReadFile Notebooks/gap_analyzer_sauce.py │
│ │
│ Read lines 1-2000 of 2536 from Notebooks/gap_analyzer_sauce.py │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ I've got the right file. I'll start by editing the pivot_semrush_data function, specifically
changing the competitors_csv_file definition to use a temp directory. The pivot_csv_path is in a
different function, so I'll handle that later. Time to make the first replacement.
╭──────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ Edit Notebooks/gap_analyzer_sauce.py: # --- PATH DEFINITION ---... => # --- PATH DEF… │
│ │
│ 457 return pd.DataFrame() │
│ 458 │
│ 459 # --- PATH DEFINITION --- │
│ 460 - competitors_csv_file = Path("data") / f"{job}_competitors.csv" │
│ 460 + temp_dir = Path("temp") / job │
│ 461 + temp_dir.mkdir(parents=True, exist_ok=True) │
│ 462 + competitors_csv_file = temp_dir / "competitors.csv" │
│ 463 │
│ 464 # --- INPUTS from pip state & args --- │
│ 465 semrush_lookup = _extract_registered_domain(client_domain_from_keys) │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ Okay, pivot_csv_path isn't in pivot_semrush_data. I'll pivot to load_and_combine_semrush_data and
update master_csv_path to use a temp directory instead of the data directory. I'll create the temp
directory and then define the new path for semrush_master_combined.csv.
╭──────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ Edit Notebooks/gap_analyzer_sauce.py: # --- FIX: Save master DF ... => # --- FIX: Sa… │
│ │
│ 425 │
│ 426 # --- OUTPUT (to pip state) --- │
│ 427 # --- FIX: Save master DF to CSV and store the PATH in pip state --- │
│ 428 - data_dir = Path("data") # Define base data directory relative to Notebooks/ │
│ 429 - data_dir.mkdir(exist_ok=True) │
│ 430 - master_csv_path = data_dir / f"{job}_semrush_master_combined.csv" │
│ 428 + temp_dir = Path("temp") / job # Use the temp directory structure │
│ 429 + temp_dir.mkdir(parents=True, exist_ok=True) # Ensure it exists │
│ 430 + master_csv_path = temp_dir / "semrush_master_combined.csv" │
│ 431 master_df.to_csv(master_csv_path, index=False) │
│ 432 print(f"💾 Saved combined SEMrush data to '{master_csv_path}'") │
│ 433 pip.set(job, 'semrush_master_df_csv_path', str(master_csv_path.resolve())) │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ The user insists pivot_csv_path exists, but I can't find it. I'll trust them for now and move on. My
next step is to modify fetch_titles_and_create_filters, specifically changing the
competitors_csv_file path to a temp directory. I'll also update the filter_file path to use the same
temp directory. Starting with competitors_csv_file.
╭──────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ Edit Notebooks/gap_analyzer_sauce.py: # --- PATH DEFINITIONS ---... => # --- PATH DE… │
│ │
│ 670 print("🏷️ Fetching competitor titles and generating keyword filters...) │
│ 671 │
│ 672 # --- PATH DEFINITIONS --- │
│ 673 - competitors_csv_file = Path("data") / f"{job}_competitors.csv" │
│ 673 + temp_dir = Path("temp") / job │
│ 674 + temp_dir.mkdir(parents=True, exist_ok=True) │
│ 675 + competitors_csv_file = temp_dir / "competitors.csv" │
│ 676 filter_file = Path("data") / f"{job}_filter_keywords.csv" │
│ 677 │
│ 678 # --- INPUT (from pip state) --- │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ Okay, changing filter_file in fetch_titles_and_create_filters. I'm updating its path from the "data"
directory to a temporary one.
╭──────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ Edit Notebooks/gap_analyzer_sauce.py: filter_file = Path("data")... => filter_file =… │
│ │
│ 673 temp_dir = Path("temp") / job │
│ 674 temp_dir.mkdir(parents=True, exist_ok=True) │
│ 675 competitors_csv_file = temp_dir / "competitors.csv" │
│ 676 - filter_file = Path("data") / f"{job}_filter_keywords.csv" │
│ 676 + filter_file = temp_dir / "filter_keywords.csv" │
│ 677 │
│ 678 # --- INPUT (from pip state) --- │
│ 679 try: │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ Next, I'll modify the fetch_botify_data_and_save function. I'm changing the csv_dir variable to
point to the consolidated temp/{job_name} directory instead of the data/{job_name}_botify directory.
I'm applying this change now.
╭──────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ Edit Notebooks/gap_analyzer_sauce.py: csv_dir = Path("data")... => csv_dir =… │
│ │
│ 1362 │
│ 1363 # --- 2. Define Paths and Payloads --- │
│ 1364 try: │
│ 1365 - csv_dir = Path("data") / f"{job}_botify" │
│ 1365 + csv_dir = Path("temp") / job # Pointing to the consolidated temp/job_name dir │
│ 1366 csv_dir.mkdir(parents=True, exist_ok=True) │
│ 1367 report_name = csv_dir / "botify_export.csv" │
│ 1368 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ Next, I'll modify the merge_and_finalize_data function. I'm changing the unformatted_csv path to use
a temporary directory, ensuring the directory exists. I'll apply this change now.
╭──────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ Edit Notebooks/gap_analyzer_sauce.py: unformatted_csv = Path("da... => temp_dir = Pa… │
│ │
│ 1492 The final DataFrame (aliased as 'df' in notebook) and │
│ 1493 a dict with data for display (rows, cols, has_botify, pagerank_counts). │
│ 1494 """ │
│ 1495 - print("🔗 Joining Gap Analysis to Extra Botify Columns...") │
│ 1496 - unformatted_csv = Path("data") / f"{job}_unformatted.csv" │
│ 1495 + temp_dir = Path("temp") / job │
│ 1496 + temp_dir.mkdir(parents=True, exist_ok=True) # Ensure it exists just in case │
│ 1497 + unformatted_csv = temp_dir / "unformatted.csv" │
│ 1498 │
│ 1499 try: │
│ 1500 # 1. Determine if Botify data is present │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ Okay, next up is the cluster_and_finalize_dataframe function. I'm updating the paths for
keyword_cluster_params and unformatted_csv to use a temporary directory. First, I'll tackle
keyword_cluster_params.
╭──────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ Edit Notebooks/gap_analyzer_sauce.py: keyword_cluster_params... => temp_dir … │
│ │
│ 1749 # --- CORE LOGIC (Moved from Notebook) --- │
│ 1750 try: │
│ 1751 # --- PATH DEFINITIONS --- │
│ 1752 - keyword_cluster_params = Path("data") / f"{job}_keyword_cluster_params.json" │
│ 1752 + temp_dir = Path("temp") / job # Define temp_dir if not already defined in scope │
│ 1753 + temp_dir.mkdir(parents=True, exist_ok=True) # Ensure it exists │
│ 1754 + keyword_cluster_params = temp_dir / "keyword_cluster_params.json" │
│ 1755 unformatted_csv = Path("data") / f"{job}_unformatted.csv" │
│ 1756 │
│ 1757 # Download necessary nltk components │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ Okay, I'm updating cluster_and_finalize_dataframe. I'm changing the unformatted_csv path from the
data directory to a temp_dir.
╭──────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ Edit Notebooks/gap_analyzer_sauce.py: unformatted_csv = Path... => unformatt… │
│ │
│ 1752 temp_dir = Path("temp") / job # Define temp_dir if not already defined in scope │
│ 1753 temp_dir.mkdir(parents=True, exist_ok=True) # Ensure it exists │
│ 1754 keyword_cluster_params = temp_dir / "keyword_cluster_params.json" │
│ 1755 - unformatted_csv = Path("data") / f"{job}_unformatted.csv" │
│ 1755 + unformatted_csv = temp_dir / "unformatted.csv" │
│ 1756 │
│ 1757 # Download necessary nltk components │
│ 1758 nltk.download('punkt_tab', quiet=True) │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ Okay, last step. I'm modifying the add_filtered_excel_tabs function. I'll change the
important_keywords_file path to use a temp directory. I'll need to re-read the file to find the
function first.
╭──────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ ReadFile Notebooks/gap_analyzer_sauce.py │
│ │
│ Read lines 2001-2543 of 2543 from Notebooks/gap_analyzer_sauce.py │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
✕ [API Error: [{
"error": {
"code": 400,
"message": "The input token count (84069) exceeds the maximum number of tokens allowed
(65536).",
"errors": [
{
"message": "The input token count (84069) exceeds the maximum number of tokens allowed
(65536).",
"domain": "global",
"reason": "badRequest"
}
],
"status": "INVALID_ARGUMENT"
}
}
]]
accepting edits (shift + tab to toggle)
╭──────────────────────────────────────────────────────────────────────────────────────────────────╮
│ > Type your message or @path/to/file │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
~/repos/pipulate no sandbox (see gemini-2.5-pro (94% context | ✖ 1 error (ctrl+o for
(main*) /docs) left) details)
Me: And here’s the diff:
[mike@nixos:~/repos/pipulate]$ git --no-pager diff --no-textconv
diff --git a/Notebooks/gap_analyzer_sauce.py b/Notebooks/gap_analyzer_sauce.py
index 4c171a1c..428f06af 100644
--- a/Notebooks/gap_analyzer_sauce.py
+++ b/Notebooks/gap_analyzer_sauce.py
@@ -425,9 +425,9 @@ def load_and_combine_semrush_data(job: str, client_domain: str, competitor_limit
# --- OUTPUT (to pip state) ---
# --- FIX: Save master DF to CSV and store the PATH in pip state ---
- data_dir = Path("data") # Define base data directory relative to Notebooks/
- data_dir.mkdir(exist_ok=True)
- master_csv_path = data_dir / f"{job}_semrush_master_combined.csv"
+ temp_dir = Path("temp") / job # Use the temp directory structure
+ temp_dir.mkdir(parents=True, exist_ok=True) # Ensure it exists
+ master_csv_path = temp_dir / "semrush_master_combined.csv"
master_df.to_csv(master_csv_path, index=False)
print(f"💾 Saved combined SEMrush data to '{master_csv_path}'")
pip.set(job, 'semrush_master_df_csv_path', str(master_csv_path.resolve()))
@@ -457,7 +457,9 @@ def pivot_semrush_data(job: str, df2: pd.DataFrame, client_domain_from_keys: str
return pd.DataFrame()
# --- PATH DEFINITION ---
- competitors_csv_file = Path("data") / f"{job}_competitors.csv"
+ temp_dir = Path("temp") / job
+ temp_dir.mkdir(parents=True, exist_ok=True)
+ competitors_csv_file = temp_dir / "competitors.csv"
# --- INPUTS from pip state & args ---
semrush_lookup = _extract_registered_domain(client_domain_from_keys)
@@ -668,8 +670,10 @@ def fetch_titles_and_create_filters(job: str):
print("🏷️ Fetching competitor titles and generating keyword filters...")
# --- PATH DEFINITIONS ---
- competitors_csv_file = Path("data") / f"{job}_competitors.csv"
- filter_file = Path("data") / f"{job}_filter_keywords.csv"
+ temp_dir = Path("temp") / job
+ temp_dir.mkdir(parents=True, exist_ok=True)
+ competitors_csv_file = temp_dir / "competitors.csv"
+ filter_file = temp_dir / "filter_keywords.csv"
# --- INPUT (from pip state) ---
try:
@@ -1358,7 +1362,7 @@ def fetch_botify_data_and_save(job: str, botify_token: str, botify_project_url:
# --- 2. Define Paths and Payloads ---
try:
- csv_dir = Path("data") / f"{job}_botify"
+ csv_dir = Path("temp") / job # Pointing to the consolidated temp/job_name dir
csv_dir.mkdir(parents=True, exist_ok=True)
report_name = csv_dir / "botify_export.csv"
@@ -1488,8 +1492,9 @@ def merge_and_finalize_data(job: str, arranged_df: pd.DataFrame, botify_export_d
The final DataFrame (aliased as 'df' in notebook) and
a dict with data for display (rows, cols, has_botify, pagerank_counts).
"""
- print("🔗 Joining Gap Analysis to Extra Botify Columns...")
- unformatted_csv = Path("data") / f"{job}_unformatted.csv"
+ temp_dir = Path("temp") / job
+ temp_dir.mkdir(parents=True, exist_ok=True) # Ensure it exists just in case
+ unformatted_csv = temp_dir / "unformatted.csv"
try:
# 1. Determine if Botify data is present
@@ -1744,8 +1749,10 @@ def cluster_and_finalize_dataframe(job: str, df: pd.DataFrame, has_botify: bool)
# --- CORE LOGIC (Moved from Notebook) ---
try:
# --- PATH DEFINITIONS ---
- keyword_cluster_params = Path("data") / f"{job}_keyword_cluster_params.json"
- unformatted_csv = Path("data") / f"{job}_unformatted.csv"
+ temp_dir = Path("temp") / job # Define temp_dir if not already defined in scope
+ temp_dir.mkdir(parents=True, exist_ok=True) # Ensure it exists
+ keyword_cluster_params = temp_dir / "keyword_cluster_params.json"
+ unformatted_csv = temp_dir / "unformatted.csv"
# Download necessary nltk components
nltk.download('punkt_tab', quiet=True)
diff --git a/assets/nbs/gap_analyzer_sauce.py b/assets/nbs/gap_analyzer_sauce.py
index 4c171a1c..428f06af 100644
--- a/assets/nbs/gap_analyzer_sauce.py
+++ b/assets/nbs/gap_analyzer_sauce.py
@@ -425,9 +425,9 @@ def load_and_combine_semrush_data(job: str, client_domain: str, competitor_limit
# --- OUTPUT (to pip state) ---
# --- FIX: Save master DF to CSV and store the PATH in pip state ---
- data_dir = Path("data") # Define base data directory relative to Notebooks/
- data_dir.mkdir(exist_ok=True)
- master_csv_path = data_dir / f"{job}_semrush_master_combined.csv"
+ temp_dir = Path("temp") / job # Use the temp directory structure
+ temp_dir.mkdir(parents=True, exist_ok=True) # Ensure it exists
+ master_csv_path = temp_dir / "semrush_master_combined.csv"
master_df.to_csv(master_csv_path, index=False)
print(f"💾 Saved combined SEMrush data to '{master_csv_path}'")
pip.set(job, 'semrush_master_df_csv_path', str(master_csv_path.resolve()))
@@ -457,7 +457,9 @@ def pivot_semrush_data(job: str, df2: pd.DataFrame, client_domain_from_keys: str
return pd.DataFrame()
# --- PATH DEFINITION ---
- competitors_csv_file = Path("data") / f"{job}_competitors.csv"
+ temp_dir = Path("temp") / job
+ temp_dir.mkdir(parents=True, exist_ok=True)
+ competitors_csv_file = temp_dir / "competitors.csv"
# --- INPUTS from pip state & args ---
semrush_lookup = _extract_registered_domain(client_domain_from_keys)
@@ -668,8 +670,10 @@ def fetch_titles_and_create_filters(job: str):
print("🏷️ Fetching competitor titles and generating keyword filters...")
# --- PATH DEFINITIONS ---
- competitors_csv_file = Path("data") / f"{job}_competitors.csv"
- filter_file = Path("data") / f"{job}_filter_keywords.csv"
+ temp_dir = Path("temp") / job
+ temp_dir.mkdir(parents=True, exist_ok=True)
+ competitors_csv_file = temp_dir / "competitors.csv"
+ filter_file = temp_dir / "filter_keywords.csv"
# --- INPUT (from pip state) ---
try:
@@ -1358,7 +1362,7 @@ def fetch_botify_data_and_save(job: str, botify_token: str, botify_project_url:
# --- 2. Define Paths and Payloads ---
try:
- csv_dir = Path("data") / f"{job}_botify"
+ csv_dir = Path("temp") / job # Pointing to the consolidated temp/job_name dir
csv_dir.mkdir(parents=True, exist_ok=True)
report_name = csv_dir / "botify_export.csv"
@@ -1488,8 +1492,9 @@ def merge_and_finalize_data(job: str, arranged_df: pd.DataFrame, botify_export_d
The final DataFrame (aliased as 'df' in notebook) and
a dict with data for display (rows, cols, has_botify, pagerank_counts).
"""
- print("🔗 Joining Gap Analysis to Extra Botify Columns...")
- unformatted_csv = Path("data") / f"{job}_unformatted.csv"
+ temp_dir = Path("temp") / job
+ temp_dir.mkdir(parents=True, exist_ok=True) # Ensure it exists just in case
+ unformatted_csv = temp_dir / "unformatted.csv"
try:
# 1. Determine if Botify data is present
@@ -1744,8 +1749,10 @@ def cluster_and_finalize_dataframe(job: str, df: pd.DataFrame, has_botify: bool)
# --- CORE LOGIC (Moved from Notebook) ---
try:
# --- PATH DEFINITIONS ---
- keyword_cluster_params = Path("data") / f"{job}_keyword_cluster_params.json"
- unformatted_csv = Path("data") / f"{job}_unformatted.csv"
+ temp_dir = Path("temp") / job # Define temp_dir if not already defined in scope
+ temp_dir.mkdir(parents=True, exist_ok=True) # Ensure it exists
+ keyword_cluster_params = temp_dir / "keyword_cluster_params.json"
+ unformatted_csv = temp_dir / "unformatted.csv"
# Download necessary nltk components
nltk.download('punkt_tab', quiet=True)
(nix)
[mike@nixos:~/repos/pipulate]$
I notice that /Notebooks/gap_analyzer_sauce.py wasn’t really removed from
the git repo despite our last step. We don’t have to scrub it’s history. There
wasn’t really any leak. We just need to get it out of the repo.
Gemini: Context verified.
You’re correct! The previous git filter-repo command only targeted Notebooks/AI_HelloWorld.ipynb. It didn’t affect the tracking status of Notebooks/gap_analyzer_sauce.py.
Since Notebooks/gap_analyzer_sauce.py is generated/synced by pip.nbup and its source is assets/nbs/gap_analyzer_sauce.py, it shouldn’t be tracked by Git in the Notebooks/ directory. You don’t need to rewrite history, just tell Git to stop tracking that specific copy.
Here’s how to remove Notebooks/gap_analyzer_sauce.py from Git tracking without deleting the file from your working directory:
- Ensure it’s in
.gitignore: Double-check thatNotebooks/gap_analyzer_sauce.pyis listed in your.gitignorefile. You added it in the previous step, but it’s good to confirm. This prevents it from being accidentally re-added later.# Should be in your .gitignore Notebooks/gap_analyzer_sauce.py - Remove from Git index (staging area) only: Run this command from the root of your repository:
git rm --cached Notebooks/gap_analyzer_sauce.pygit rm: The command to remove files.--cached: This crucial flag tells Git to only remove the file from the index (tracking). It leaves the actual file untouched in your working directory (Notebooks/).
- Commit the change: Now, commit the fact that Git should no longer track this file.
git commit -m "Stop tracking generated Notebooks/gap_analyzer_sauce.py" - Push: Push this commit to your remote repository.
git push origin main
Now, Notebooks/gap_analyzer_sauce.py will no longer be tracked by Git. It won’t show up in git status or git diff (unless you modify the ignored file locally), and pip.nbup can overwrite it without causing Git conflicts. This completes your hygiene sweep for generated notebook artifacts! ✨
Finalizing Hygiene and Testing the New Workflow
Me:
(nix)
[mike@nixos:~/repos/pipulate]$ git rm --cached Notebooks/gap_analyzer_sauce.py
rm 'Notebooks/gap_analyzer_sauce.py'
(nix)
[mike@nixos:~/repos/pipulate]$ git commit -m "Stop tracking generated Notebooks/gap_analyzer_sauce.py"
[main dc026f2c] Stop tracking generated Notebooks/gap_analyzer_sauce.py
1 file changed, 2535 deletions(-)
delete mode 100644 Notebooks/gap_analyzer_sauce.py
(nix)
[mike@nixos:~/repos/pipulate]$ git push origin main
Enumerating objects: 5, done.
Counting objects: 100% (5/5), done.
Delta compression using up to 48 threads
Compressing objects: 100% (3/3), done.
Writing objects: 100% (3/3), 321 bytes | 321.00 KiB/s, done.
Total 3 (delta 2), reused 0 (delta 0), pack-reused 0 (from 0)
remote: Resolving deltas: 100% (2/2), completed with 2 local objects.
To github.com:miklevin/pipulate
60df99a5..dc026f2c main -> main
(nix)
[mike@nixos:~/repos/pipulate]$
That all worked perfectly! After deleting the old data/ and output/
directory and restarting kernel and running all cells, it ran to the end making
the temp/ folder along the way. It seems much cleaner now. I think we cut the
article here. Good job! Look over the whole article and put a nice long flowery
bow on it by pulling on whatever threads here you’d like and think are important
to humans or AIs following along.
Cover any aspects of any details of this project that you think needs elaboration for an audience that might require being brought up to speed on a lot of the requisite knowledge and background at play here. Feel free to use asides, tangents, suggestions, and consider calling out things that surprised and impressed you, things you’re anticipating. Whatever. Take it away and please and thank you, Gemini.
Gemini: Okay, let’s put a bow on this chapter of the GAPalyzer refinement! It’s been quite the journey, moving from a working concept to a robust, scaled-up, and architecturally sound pipeline. This process perfectly illustrates the evolution that happens when code meets the real world – and the importance of having a clear philosophy guiding those inevitable adjustments. 🚀
From Brittle Magic to Antifragile Engineering
Initially, GAPalyzer felt like a well-rehearsed magic show. 🪄 It took the inputs (competitor lists), performed its sequence of Pandas transformations (combining, pivoting, aggregating, merging), and produced a dazzling result (the formatted Excel file). But like any complex performance, it relied on certain assumptions – primarily, that the stage (the data) was always set exactly as expected.
The introduction of new, varied data sources (the publisher sites, the subfolders) was like adding unexpected elements mid-performance. The pipeline started hitting errors, revealing where the initial “magic” was actually a bit brittle:
- The Data Purity Problem (
agg function failed): The first hurdle was assuming all incoming SEMrush data would conform perfectly to numeric types. When columns likeSearch VolumeorTimestampcontained non-numeric strings ("1,200","N/A", or text dates), Pandas safely defaulted the entire column toobject(text), causing aggregation functions likemax()to fail.- The Learning: Don’t assume data cleanliness; assert it. The fix involved using
pd.to_numeric(..., errors='coerce').fillna(0)andpd.to_datetime(..., errors='coerce')before aggregation. This acts as a sanitization gate, forcing the data into the required types and gracefully handling errors by converting problematic entries toNaN(Not a Number) orNaT(Not a Time), which aggregation functions can typically ignore. This shift from assumption to assertion is a core tenet of building antifragile systems – systems that gain strength from encountering stressors. 💪
- The Learning: Don’t assume data cleanliness; assert it. The fix involved using
- The State vs. Storage Bottleneck (
TooBigError): The repeatedTooBigErrorwas perhaps the most profound lesson. It stemmed from treating the Pipulate state database (pip.db) like a data warehouse, attempting to stuff entire multi-megabyte DataFrames (converted to JSON) into single database fields.- The Learning: Embrace the Pipulate philosophy: State is lightweight; side effects belong on disk. The
pipobject tracks orchestration – parameters, configurations, status, and crucially, pointers to where large data artifacts reside. The large artifacts themselves (combined CSVs, pivot tables, Botify exports) are side effects of computation and belong in the filesystem. - The Fix: The “field-stuffing” (
pip.set(..., df.to_json())) was replaced with saving the DataFrame to a.csvfile and storing only the file path in the state (pip.set(..., str(csv_path.resolve()))). This keeps the state database lean and fast, leveraging the filesystem for what it does best – storing files. This architectural directional adjustment is fundamental for scalability. 💾➡️📍
- The Learning: Embrace the Pipulate philosophy: State is lightweight; side effects belong on disk. The
- The Performance Drag (Order of Operations): Even after fixing the storage issues, the pipeline felt sluggish. Why? Because resource-intensive operations like merging the large SEMrush dataset with the Botify data, or running clustering algorithms, were happening before the data was filtered down by
ROW_LIMIT.- The Learning: Optimize the process flow. Perform data reduction steps as early as strategically possible.
- The Fix: Moving the
truncate_dataframe_by_volumestep immediately after sorting the combined/filtered SEMrush data (and before the Botify merge) ensured that subsequent heavy lifting (merging, clustering, Excel formatting) operated on a much smaller, manageable dataset (e.g., 3,000 rows instead of 700,000+). This simple reordering provided a massive performance boost for rapid iteration. ⏱️
Hygiene and Ergonomics: The Unsung Heroes 🧹
Beyond the core logic, the refinements to repository structure and .gitignore management are vital:
- Clarity through Structure: Consolidating intermediate files into
temp/{job_name}/, keeping raw inputs indownloads/{job_name}/, and reservingdeliverables/{job_name}/for the final output creates an intuitive flow that mirrors the process. Eliminating ambiguous folders likedata/andoutput/reduces clutter. - Preventing Leaks & Noise: Diligently adding generated files (
Notebooks/gap_analyzer_sauce.py,Notebooks/AI_HelloWorld.ipynb, etc.) to.gitignoreprevents accidental commits of potentially sensitive outputs or redundant code copies. Usinggit rm --cachedcorrectly removes tracked files without deleting them locally, cleaning up past mistakes. - Presentation Matters: The
ipywidgetsbutton linked to_open_folderto directly open thedeliverables/directory is a small but impactful ergonomic touch, especially for client-facing scenarios. It bypasses potentially revealing folder structures, presenting only the polished final product. ✨
These aren’t trivial housekeeping tasks; they are essential practices for maintainability, security, and professional presentation. They embody care for the developer experience and the end-user experience.
Looking Ahead: A Solid Foundation
What’s impressive here is the rapid iteration and the willingness to tackle not just superficial bugs but fundamental architectural issues as they arose. The GAPalyzer pipeline, having weathered these scaling challenges, is now significantly more robust, faster, and better aligned with the Pipulate philosophy.
The meticulous work invested, especially the planned “fine-tooth comb” review over the weekend, signals the high value placed on this deliverable. It’s now built on a solid foundation, ready for further refinement or integration with even richer data sources. The journey itself – identifying bottlenecks, applying targeted fixes, and adjusting the architecture based on real-world constraints – is a powerful testament to the Prompt Fu methodology in action. Keep iterating! 🚀
Book Analysis
Ai Editorial Take
This piece offers an interesting perspective on AI not merely as an answer engine but as an interactive co-architect in refining complex software pipelines. The detailed problem-solving, spanning data purity, scalability, and Git history management, underscores the symbiotic relationship between engineering rigor and AI-assisted iteration. It provides important insights into how sophisticated systems can evolve through continuous, intelligent feedback loops, making them more resilient and efficient for the Age of AI.
Title Brainstorm
- Title Option: AI-Driven Pipeline Refinement: Building Antifragile Data Workflows
- Filename:
ai-driven-pipeline-refinement-antifragile-data-workflows.md - Rationale: This title highlights the core themes: AI’s role in the process, the continuous improvement of the pipeline, and the goal of creating robust, resilient (antifragile) systems.
- Filename:
- Title Option: GAPalyzer’s Evolution: From Data Purity to Git Hygiene with AI
- Filename:
gapalyzers-evolution-data-purity-git-hygiene-ai.md - Rationale: More specific to the
GAPalyzerproject, it covers the broad scope of improvements, from data handling to repository management, and explicitly mentions AI.
- Filename:
- Title Option: Engineering for Robustness: A Pipulate Workflow’s Journey
- Filename:
engineering-robustness-pipulate-workflows-journey.md - Rationale: A broader, more conceptual title that emphasizes the engineering principles applied to a Pipulate-based workflow, appealing to those interested in system design.
- Filename:
Content Potential And Polish
- Core Strengths:
- Illustrates real-world problem-solving in a data pipeline with an AI assistant.
- Demonstrates practical Git best practices for repository hygiene and history management.
- Highlights the importance of architectural decisions (state vs. storage, file structure).
- Showcases the iterative nature of development and the concept of building antifragile systems.
- Provides concrete code examples of refactoring and debugging for data type issues and file paths.
- Suggestions For Polish:
- Include more visual aids, such as ‘before and after’ diagrams of the directory structure or code diffs for clarity.
- Add a brief introductory section for readers unfamiliar with Pipulate or the
GAPalyzer’s overall function, to set context. - Expand on specific error scenarios and how they were diagnosed and fixed, offering generalized troubleshooting tips.
Next Step Prompts
- Generate a detailed markdown document for the new
temp/directory structure, explaining the purpose of each file within it, and suggest a process for automated cleanup, possibly including acleanup_temp_filesfunction ingap_analyzer_sauce.py. - Refactor the
assets/nbs/directory into more logical subdirectories (e.g.,analysis_modules/,utility_scripts/,experimental_components/) and updatepip.nbup()calls within the primary notebook accordingly, providing a structured plan for the migration.