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WalkthroughAdds a new analytics module that computes normalized per‑platform scores and a unified ranking, exposes a DRF endpoint at /analytics/unified/, and adds a frontend UnifiedLeaderboard component, route (/leaderboards), and UI for fetching, filtering, and displaying the unified leaderboard. Changes
Sequence DiagramsequenceDiagram
participant Client
participant Frontend as UnifiedLeaderboard
participant APIView as UnifiedAnalyticsView
participant Analytics as analytics.build_unified_ranking()
participant DB as Models
Client->>Frontend: navigate /leaderboards
Frontend->>APIView: GET /analytics/unified/
APIView->>Analytics: build_unified_ranking()
Analytics->>DB: load_dataframes() (githubUser, codeforcesUser, codechefUser, LeetcodeUser)
DB-->>Analytics: DataFrames
Analytics->>Analytics: github_score(), codeforces_score(), codechef_score(), leetcode_score()
Analytics->>Analytics: join scores, fill NaNs, compute total_score and rank
Analytics-->>APIView: unified ranking DataFrame
APIView-->>Frontend: JSON records
Frontend-->>Client: render table (search, friends filter, badges, bars)
Estimated code review effort🎯 4 (Complex) | ⏱️ ~45 minutes Poem
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Actionable comments posted: 5
🧹 Nitpick comments (4)
api/leaderboard/analytics.py (3)
141-146: Redundantdf.get()afterfillna(0).After the outer merge and
fillna(0), all score columns (github_score,cf_score,cc_score,lt_score) will exist in the DataFrame. Usingdf.get()with a default is unnecessary and inconsistent with direct column access elsewhere.♻️ Proposed simplification
df["total_score"] = ( - df.get("github_score", 0) + - df.get("cf_score", 0) + - df.get("cc_score", 0) + - df.get("lt_score", 0) + df["github_score"] + + df["cf_score"] + + df["cc_score"] + + df["lt_score"] )🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@api/leaderboard/analytics.py` around lines 141 - 146, The total_score calculation uses df.get(...) defaults redundantly after you already fillna(0); replace the df.get calls with direct column access (e.g., use df["github_score"], df["cf_score"], df["cc_score"], df["lt_score"] in the df["total_score"] assignment) so the expression is consistent with the rest of the DataFrame logic and relies on the prior fillna(0).
27-31: Consider using.copy()to avoid mutating input DataFrames.The scoring functions modify the input DataFrame in place by adding new columns. This can cause unintended side effects if the caller reuses the original DataFrame. Making a copy at the start of each function is a safer pattern.
♻️ Proposed fix for github_score (apply similarly to other scoring functions)
def github_score(df): if df.empty: return df - df = df.fillna(0) + df = df.copy() + df = df.fillna(0)🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@api/leaderboard/analytics.py` around lines 27 - 31, The github_score function (and other scoring functions that mutate DataFrames) modifies the input DataFrame in place (e.g., calling df.fillna and adding columns); to avoid side effects, create and operate on a copy at the start of each function (e.g., assign df = df.copy() or df = df.copy(deep=True) before any fills/column assignments) and then return that copy; apply the same pattern to the other scoring functions that currently mutate their input so callers’ original DataFrames are not changed.
59-63:df.get()returns a scalar default, not a Series.When the column doesn't exist,
df.get("easy_solved", 0)returns the scalar0, not a Series of zeros. This will broadcast correctly in arithmetic, but it's non-idiomatic. Prefer direct column access with a fallback check or usedf["col"] if "col" in df.columns else 0.♻️ More explicit fallback pattern
- df["difficulty_score"] = ( - df.get("easy_solved", 0) * 1 + - df.get("medium_solved", 0) * 2 + - df.get("hard_solved", 0) * 3 - ) + easy = df["easy_solved"] if "easy_solved" in df.columns else 0 + medium = df["medium_solved"] if "medium_solved" in df.columns else 0 + hard = df["hard_solved"] if "hard_solved" in df.columns else 0 + df["difficulty_score"] = easy * 1 + medium * 2 + hard * 3🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@api/leaderboard/analytics.py` around lines 59 - 63, The use of df.get("easy_solved", 0) / df.get("medium_solved", 0) / df.get("hard_solved", 0) returns scalar defaults instead of per-row Series; replace these with an explicit per-column fallback so difficulty_score is computed from Series of zeros when columns are missing — e.g. use df["easy_solved"] if "easy_solved" in df.columns else a zero Series matching df.index (same for medium_solved and hard_solved), or reindex/fill missing columns first, then compute df["difficulty_score"] = df["easy_solved"]*1 + df["medium_solved"]*2 + df["hard_solved"]*3 to ensure correct Series arithmetic.api/leaderboard/analytics_views.py (1)
5-12: Consider adding permission classes and caching.The endpoint has no
permission_classesdefined, making it publicly accessible by default (depending onDEFAULT_PERMISSION_CLASSESin settings). If this should require authentication like other endpoints in the project, add appropriate permissions.Additionally,
build_unified_ranking()queries multiple tables and performs computations on every request. Consider adding caching for better performance with larger datasets.♻️ Example with permissions and caching
from rest_framework.views import APIView from rest_framework.response import Response +from rest_framework import permissions +from django.utils.decorators import method_decorator +from django.views.decorators.cache import cache_page from leaderboard.analytics import build_unified_ranking + class UnifiedAnalyticsView(APIView): + permission_classes = [permissions.IsAuthenticated] # or AllowAny if public + + `@method_decorator`(cache_page(60 * 5)) # Cache for 5 minutes def get(self, request): df = build_unified_ranking()🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@api/leaderboard/analytics_views.py` around lines 5 - 12, UnifiedAnalyticsView currently exposes get without explicit permission_classes and runs build_unified_ranking() on every request; add an appropriate permission (e.g., permission_classes = [IsAuthenticated] or the project-specific permission class) on UnifiedAnalyticsView to match other endpoints, and add caching around the expensive computation—either use Django's cache_page decorator on the get method or cache the build_unified_ranking() result with a keyed timeout (and implement cache invalidation when underlying leaderboard data changes) so repeated requests hit the cache instead of recomputing.
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Inline comments:
In `@api/leaderboard/analytics.py`:
- Around line 3-8: The current top-level import "from models import githubUser,
codeforcesUser, codechefUser, LeetcodeUser" is invalid in Django; update the
import to reference the app module (or use a relative import) so Django can
resolve the models—e.g., replace that line with an import from the app's models
module (for example "from leaderboard.models import githubUser, codeforcesUser,
codechefUser, LeetcodeUser" or "from .models import ...") ensuring the
identifiers githubUser, codeforcesUser, codechefUser, and LeetcodeUser are
imported from the correct module.
- Around line 52-56: Change the ambiguous membership check "ranking" in df to an
explicit column check by using "ranking" in df.columns in the block that
computes df["ranking_inv"] and df["ranking_norm"]; specifically, update the
conditional that guards creation of ranking_inv, ranking_norm and the call to
safe_normalize so it uses df.columns to determine presence of the "ranking"
column and preserves the existing logic that sets df["ranking_norm"] = 0 when
the column is absent.
- Around line 111-116: The check for the "Global_rank" column should use
df.columns like the other ranking check; update the conditional from if
"Global_rank" in df: to if "Global_rank" in df.columns:, then keep the existing
conversion to numeric, rank_inv calculation, and call to safe_normalize
(functions/variables: df, "Global_rank", rank_inv, rank_norm, safe_normalize)
and leave the else branch setting df["rank_norm"]=0 as-is so a column is created
when the column is absent.
- Around line 86-90: The current efficiency calculation can divide by zero when
df["total_submissions"] contains 0; change the computation to guard the
denominator by replacing zeros with 1 (or clipping to a minimum of 1) before
dividing. Concretely, derive a safe denominator from df.get("total_submissions",
1) using .replace(0, 1) or .clip(lower=1), then compute df["efficiency"] =
df.get("total_solved", 0) / safe_denominator and continue to call
safe_normalize(df["efficiency"]); update the block around
df["efficiency"]/df["efficiency_norm"] accordingly.
In `@api/leaderboard/urls.py`:
- Line 27: The import for UnifiedAnalyticsView is using an absolute module name;
replace it with a relative import to match the rest of the file (change the
import of UnifiedAnalyticsView from analytics_views to a relative import, e.g.,
import UnifiedAnalyticsView from .analytics_views) so use the symbol
UnifiedAnalyticsView and the module analytics_views to update the import to from
.analytics_views import UnifiedAnalyticsView.
---
Nitpick comments:
In `@api/leaderboard/analytics_views.py`:
- Around line 5-12: UnifiedAnalyticsView currently exposes get without explicit
permission_classes and runs build_unified_ranking() on every request; add an
appropriate permission (e.g., permission_classes = [IsAuthenticated] or the
project-specific permission class) on UnifiedAnalyticsView to match other
endpoints, and add caching around the expensive computation—either use Django's
cache_page decorator on the get method or cache the build_unified_ranking()
result with a keyed timeout (and implement cache invalidation when underlying
leaderboard data changes) so repeated requests hit the cache instead of
recomputing.
In `@api/leaderboard/analytics.py`:
- Around line 141-146: The total_score calculation uses df.get(...) defaults
redundantly after you already fillna(0); replace the df.get calls with direct
column access (e.g., use df["github_score"], df["cf_score"], df["cc_score"],
df["lt_score"] in the df["total_score"] assignment) so the expression is
consistent with the rest of the DataFrame logic and relies on the prior
fillna(0).
- Around line 27-31: The github_score function (and other scoring functions that
mutate DataFrames) modifies the input DataFrame in place (e.g., calling
df.fillna and adding columns); to avoid side effects, create and operate on a
copy at the start of each function (e.g., assign df = df.copy() or df =
df.copy(deep=True) before any fills/column assignments) and then return that
copy; apply the same pattern to the other scoring functions that currently
mutate their input so callers’ original DataFrames are not changed.
- Around line 59-63: The use of df.get("easy_solved", 0) /
df.get("medium_solved", 0) / df.get("hard_solved", 0) returns scalar defaults
instead of per-row Series; replace these with an explicit per-column fallback so
difficulty_score is computed from Series of zeros when columns are missing —
e.g. use df["easy_solved"] if "easy_solved" in df.columns else a zero Series
matching df.index (same for medium_solved and hard_solved), or reindex/fill
missing columns first, then compute df["difficulty_score"] = df["easy_solved"]*1
+ df["medium_solved"]*2 + df["hard_solved"]*3 to ensure correct Series
arithmetic.
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@sujalsom22 Please check the relative import issue and the division by zero issue as mentioned in the comments. |
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Actionable comments posted: 2
🧹 Nitpick comments (1)
api/leaderboard/analytics.py (1)
11-14: Load only scoring columns to reduce ORM + pandas overhead.Line 11-14 currently fetches all model fields, but scoring uses only a subset. Selecting only required columns will reduce payload size and DataFrame memory.
♻️ Proposed refactor
- df_github = pd.DataFrame(list(githubUser.objects.all().values())) - df_cf = pd.DataFrame(list(codeforcesUser.objects.all().values())) - df_cc = pd.DataFrame(list(codechefUser.objects.all().values())) - df_lt = pd.DataFrame(list(LeetcodeUser.objects.all().values())) + df_github = pd.DataFrame(list( + githubUser.objects.values("username", "repositories", "stars", "contributions") + )) + df_cf = pd.DataFrame(list( + codeforcesUser.objects.values("username", "rating", "max_rating", "total_solved", "total_submissions") + )) + df_cc = pd.DataFrame(list( + codechefUser.objects.values("username", "rating", "max_rating", "Global_rank") + )) + df_lt = pd.DataFrame(list( + LeetcodeUser.objects.values("username", "ranking", "easy_solved", "medium_solved", "hard_solved", "total_solved") + ))🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@api/leaderboard/analytics.py` around lines 11 - 14, The current queries build DataFrames from all model fields (df_github, df_cf, df_cc, df_lt) which is expensive; change each queryset call to select only the scoring columns with values(...) instead of values() — e.g. define a SCORING_FIELDS list containing the exact columns your scoring logic uses and replace githubUser.objects.all().values() with githubUser.objects.all().values(*SCORING_FIELDS) (and do the same for codeforcesUser, codechefUser, LeetcodeUser) so the DataFrames only contain the needed fields; keep DataFrame construction (pd.DataFrame(...)) the same.
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Inline comments:
In `@api/leaderboard/analytics.py`:
- Around line 28-29: Several early returns return raw empty DataFrames which can
drop the "username" column and cause the later merge-on-"username" to KeyError;
update each early-return branch (the checks that do "if df.empty: return df" at
the four locations) to instead return a schema-safe empty DataFrame that
includes the expected columns (at minimum "username" and any other columns used
later in that score frame) so downstream merges (the merge-on-"username" around
the later block) do not fail when a source is empty; locate the empty-returning
branches and replace them with a constructor like pd.DataFrame(columns=[...])
containing "username" and the frame's expected columns.
- Around line 52-54: The inversion currently rewards missing/unknown ranks
because you fill missing rank values with 0 before computing df["ranking_inv"]
and df["ranking_norm"]; instead, preserve or mark missing ranks as NaN (or set
them to a sentinel worst rank) before inversion so they don't become
top-scoring. Concretely: stop using fillna(0) for the ranking column(s), ensure
df["ranking"] contains NaN for unknowns (or replace 0/"NA" with np.nan via
df["ranking"].replace(...)), compute df["ranking_inv"] only on non-null values
(e.g., df.loc[df["ranking"].notna(), "ranking_inv"] = max_rank - df["ranking"])
and then call safe_normalize on the non-null subset (or normalize and keep NaNs
as 0 afterward) so missing ranks are not normalized to 1.0; update both places
where ranking inversion occurs (the df["ranking_inv"]/df["ranking_norm"]
computations) and any preceding fillna(0) logic that affects df["ranking"] or
codechefUser.Global_rank.
---
Nitpick comments:
In `@api/leaderboard/analytics.py`:
- Around line 11-14: The current queries build DataFrames from all model fields
(df_github, df_cf, df_cc, df_lt) which is expensive; change each queryset call
to select only the scoring columns with values(...) instead of values() — e.g.
define a SCORING_FIELDS list containing the exact columns your scoring logic
uses and replace githubUser.objects.all().values() with
githubUser.objects.all().values(*SCORING_FIELDS) (and do the same for
codeforcesUser, codechefUser, LeetcodeUser) so the DataFrames only contain the
needed fields; keep DataFrame construction (pd.DataFrame(...)) the same.
🪄 Autofix (Beta)
Fix all unresolved CodeRabbit comments on this PR:
- Push a commit to this branch (recommended)
- Create a new PR with the fixes
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- api/leaderboard/analytics_views.py
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Actionable comments posted: 4
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⚠️ Outside diff range comments (1)
app/src/App.jsx (1)
147-148:⚠️ Potential issue | 🟡 MinorStray
-character after<Profile />.This will render a visible hyphen in the UI.
🐛 Proposed fix
<PrivateRoute> - <Profile />- + <Profile /> </PrivateRoute>🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@app/src/App.jsx` around lines 147 - 148, Remove the stray hyphen after the Profile component in the JSX; inside the PrivateRoute block that renders <Profile /> (the JSX containing Profile and PrivateRoute), delete the trailing "-" so the component renders without a visible hyphen in the UI.
🧹 Nitpick comments (2)
app/src/App.jsx (1)
184-190: Props passed toUnifiedLeaderboardare unused.The component fetches its own data from
/analytics/unified/and doesn't accept any props. These props are dead code.♻️ Proposed fix: remove unused props
<PrivateRoute> - <UnifiedLeaderboard - codeforcesUsers={codeforcesUsers} - codechefUsers={codechefUsers} - leetcodeUsers={leetcodeUsers} - githubUsers={githubUser} - openlakeUsers={openlakeContributor} - /> + <UnifiedLeaderboard /> </PrivateRoute>🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@app/src/App.jsx` around lines 184 - 190, The JSX is passing dead props to UnifiedLeaderboard (codeforcesUsers, codechefUsers, leetcodeUsers, githubUsers/githubUser, openlakeUsers) even though UnifiedLeaderboard fetches its own data and defines no props; remove these props from the UnifiedLeaderboard element in App.jsx and delete any now-unused local variables or imports (e.g., codeforcesUsers, codechefUsers, leetcodeUsers, githubUser, openlakeContributor) to avoid unused-variable warnings, and verify UnifiedLeaderboard remains unchanged since it already performs its own fetch from /analytics/unified/.app/src/components/UnifiedLeaderboard.jsx (1)
178-190: Avatar column will always show fallback.Per the backend analytics endpoint (context snippet 1), the
avatarfield is never returned bybuild_unified_ranking(). The fallback icon will always display. This is handled gracefully but consider either removing the avatar column or enhancing the backend to include avatars if this is intended functionality.🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@app/src/components/UnifiedLeaderboard.jsx` around lines 178 - 190, The Avatar column in UnifiedLeaderboard.jsx currently always shows the fallback because row.original.avatar is never provided by the backend (build_unified_ranking()); either remove the avatar column definition from the columns array (the object with id: "avatar" and cell using AvatarImage/AvatarFallback) or update the data source so build_unified_ranking() returns an avatar URL (or populate row.original.avatar on the client after fetching user profile data using the user id) so AvatarImage receives a real src; locate the avatar column object in UnifiedLeaderboard.jsx (the cell using row.original.avatar) and apply one of these fixes.
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Inline comments:
In `@app/src/App.jsx`:
- Line 25: The import in App.jsx incorrectly uses a default import for
UnifiedLeaderboard while the component is exported as a named export (export
function UnifiedLeaderboard); change the import statement to a named import
(import { UnifiedLeaderboard } from "./components/UnifiedLeaderboard") so the
UnifiedLeaderboard symbol resolves correctly and is not undefined.
In `@app/src/components/HomePage.jsx`:
- Around line 151-157: UnifiedLeaderboard is referenced in the Leaderboards
component but not imported; add an import for UnifiedLeaderboard at the top of
the file with the other imports (e.g., import UnifiedLeaderboard from
'<correct-relative-path>/UnifiedLeaderboard') and ensure you use the correct
default vs named import based on how UnifiedLeaderboard is exported so the
component resolves at runtime.
- Around line 153-155: The UnifiedLeaderboard component is wrapped in a <p> tag
which is semantically incorrect for a complex table; remove the <p> wrapper and
render <UnifiedLeaderboard /> directly (or wrap it in a <div> if you need a
block wrapper), and move any layout/styling classes (e.g., "content-center
text-4xl font-bold") from the <p> to an appropriate container or to the
UnifiedLeaderboard container so the table isn’t constrained by paragraph
semantics; ensure the final structure keeps the existing alignment classes like
"flex size-full justify-center" and does not use a <p> around the table
component.
In `@app/src/components/UnifiedLeaderboard.jsx`:
- Around line 105-108: The Authorization header construction in
UnifiedLeaderboard.jsx uses
JSON.parse(localStorage.getItem("authTokens")).access which will throw if
authTokens is null or malformed; update the code that builds the headers (the
Authorization: "Bearer " + ... expression inside UnifiedLeaderboard) to safely
read and parse localStorage: retrieve localStorage.getItem("authTokens"), verify
it's non-null, parse inside a try/catch or use a safe JSON parse helper, and
fall back to an empty string or null token when parsing fails so no exception is
thrown and requests use a safe default.
---
Outside diff comments:
In `@app/src/App.jsx`:
- Around line 147-148: Remove the stray hyphen after the Profile component in
the JSX; inside the PrivateRoute block that renders <Profile /> (the JSX
containing Profile and PrivateRoute), delete the trailing "-" so the component
renders without a visible hyphen in the UI.
---
Nitpick comments:
In `@app/src/App.jsx`:
- Around line 184-190: The JSX is passing dead props to UnifiedLeaderboard
(codeforcesUsers, codechefUsers, leetcodeUsers, githubUsers/githubUser,
openlakeUsers) even though UnifiedLeaderboard fetches its own data and defines
no props; remove these props from the UnifiedLeaderboard element in App.jsx and
delete any now-unused local variables or imports (e.g., codeforcesUsers,
codechefUsers, leetcodeUsers, githubUser, openlakeContributor) to avoid
unused-variable warnings, and verify UnifiedLeaderboard remains unchanged since
it already performs its own fetch from /analytics/unified/.
In `@app/src/components/UnifiedLeaderboard.jsx`:
- Around line 178-190: The Avatar column in UnifiedLeaderboard.jsx currently
always shows the fallback because row.original.avatar is never provided by the
backend (build_unified_ranking()); either remove the avatar column definition
from the columns array (the object with id: "avatar" and cell using
AvatarImage/AvatarFallback) or update the data source so build_unified_ranking()
returns an avatar URL (or populate row.original.avatar on the client after
fetching user profile data using the user id) so AvatarImage receives a real
src; locate the avatar column object in UnifiedLeaderboard.jsx (the cell using
row.original.avatar) and apply one of these fixes.
🪄 Autofix (Beta)
Fix all unresolved CodeRabbit comments on this PR:
- Push a commit to this branch (recommended)
- Create a new PR with the fixes
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✅ Files skipped from review due to trivial changes (1)
- app/src/components/Navbar.jsx
…ersion in analytics
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🧹 Nitpick comments (1)
api/leaderboard/analytics.py (1)
10-10: Consider adding pandas terminology to spell check dictionary.The pipeline spell checker is flagging
fillnaanddataframesas unrecognized words. These are standard pandas terminology and should be added to the project's spell check configuration file (e.g.,.cspell.jsonor similar) to suppress these false positives.🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@api/leaderboard/analytics.py` at line 10, The spellchecker flags pandas terms used in load_dataframes (e.g., fillna and dataframes) as false positives; update the project spell-check configuration to add those terms (add "fillna" and "dataframes" to the word list in the spell config such as .cspell.json or your repo's spell list) so references in the load_dataframes function and other pandas code are no longer flagged.
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Nitpick comments:
In `@api/leaderboard/analytics.py`:
- Line 10: The spellchecker flags pandas terms used in load_dataframes (e.g.,
fillna and dataframes) as false positives; update the project spell-check
configuration to add those terms (add "fillna" and "dataframes" to the word list
in the spell config such as .cspell.json or your repo's spell list) so
references in the load_dataframes function and other pandas code are no longer
flagged.
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@check-spelling-bot Report🔴 Please reviewSee the 📂 files view, the 📜action log, or 📝 job summary for details.Unrecognized words (37)These words are not needed and should be removedCRA elems signupSome files were automatically ignored 🙈These sample patterns would exclude them: You should consider adding them to: File matching is via Perl regular expressions. To check these files, more of their words need to be in the dictionary than not. You can use To accept these unrecognized words as correct, update file exclusions, and remove the previously acknowledged and now absent words, you could run the following commands... in a clone of the git@github.com:sujalsom22/Leaderboard-Pro.git repository curl -s -S -L 'https://raw.githubusercontent.com/check-spelling/check-spelling/main/apply.pl' |
perl - 'https://github.com/OpenLake/Leaderboard-Pro/actions/runs/23509302098/attempts/1' &&
git commit -m 'Update check-spelling metadata'Available 📚 dictionaries could cover words (expected and unrecognized) not in the 📘 dictionaryThis includes both expected items (592) from .github/actions/spelling/expect.txt and unrecognized words (37)
Consider adding them (in with:
extra_dictionaries: |
cspell:django/dict/django.txt
cspell:software-terms/dict/softwareTerms.txt
cspell:python/src/common/extra.txt
cspell:npm/dict/npm.txt
cspell:html/dict/html.txtTo stop checking additional dictionaries, add (in check_extra_dictionaries: ""Warnings
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| Count | |
|---|---|
| 1 |
See
If you see a bunch of garbage
If it relates to a ...
well-formed pattern
See if there's a pattern that would match it.
If not, try writing one and adding it to the patterns.txt file.
Patterns are Perl 5 Regular Expressions - you can test yours before committing to verify it will match your lines.
Note that patterns can't match multiline strings.
binary-ish string
Please add a file path to the excludes.txt file instead of just accepting the garbage.
File paths are Perl 5 Regular Expressions - you can test yours before committing to verify it will match your files.
^ refers to the file's path from the root of the repository, so ^README\.md$ would exclude README.md (on whichever branch you're using).
Collaborator
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Closed as outdated |
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Summary
This PR introduces a unified cross-platform analytics and normalization layer built on top of the existing leaderboard models.
Key Points
Adds production-safe analytics module
Implements normalized scoring for GitHub, Codeforces, CodeChef, and LeetCode
Introduces unified composite ranking
Handles empty database safely
Does not modify ingestion logic or existing leaderboard endpoints
Impact
Adds cross-platform comparison capability
Keeps existing architecture intact
Ready for future extension (dynamic weights, trend analysis, visualizations)
Summary by CodeRabbit