diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
new file mode 100644
index 0000000..99e1ed4
--- /dev/null
+++ b/.github/workflows/main.yml
@@ -0,0 +1,14 @@
+name: Main
+
+on:
+ push:
+ branches: [ master ]
+
+jobs:
+ run-tests:
+ uses: ./.github/workflows/test.yml
+
+ run-release:
+ needs: run-tests
+ uses: ./.github/workflows/release.yml
+ secrets: inherit
\ No newline at end of file
diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
new file mode 100644
index 0000000..b9bca20
--- /dev/null
+++ b/.github/workflows/release.yml
@@ -0,0 +1,37 @@
+name: Release
+
+on:
+ workflow_call:
+
+permissions:
+ contents: write
+ pull-requests: write
+
+jobs:
+ release-please:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: googleapis/release-please-action@v4
+ id: release
+ with:
+ release-type: python
+
+ - name: Checkout code
+ if: ${{ steps.release.outputs.pr }}
+ uses: actions/checkout@v4
+ with:
+ ref: ${{ fromJson(steps.release.outputs.pr).head_sha }}
+
+ - name: install uv
+ if: ${{ steps.release.outputs.pr }}
+ uses: astral-sh/setup-uv@v5
+
+ - name: update uv lockfile
+ if: ${{ steps.release.outputs.pr }}
+ run: |
+ uv lock
+ git config user.name github-actions[bot]
+ git config user.email github-actions[bot]@users.noreply.github.com
+ git add uv.lock
+ git commit -m "chore: update uv.lock for new version" || echo "no changes"
+ git push
\ No newline at end of file
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index 371054c..92dd98c 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -1,28 +1,32 @@
-name: Run Tests
+name: Test
on:
push:
branches: [ master ]
pull_request:
branches: [ master ]
+ workflow_call:
+
+permissions:
+ contents: write
+ pull-requests: write
jobs:
test:
runs-on: ubuntu-latest
-
steps:
- - uses: actions/checkout@v3
-
- - name: Set up Python
- uses: actions/setup-python@v4
- with:
- python-version: '3.11'
-
- - name: Install dependencies
- run: |
- python -m pip install --upgrade pip
- pip install -r requirements.txt
-
- - name: Run tests
- run: |
- pytest tests/ -v
+ - uses: actions/checkout@v4
+
+ - name: Install uv
+ uses: astral-sh/setup-uv@v5
+ with:
+ enable-cache: true
+
+ - name: set up python
+ run: uv python install 3.11
+
+ - name: install dependencies
+ run: uv sync --all-extras --dev
+
+ - name: run tests
+ run: uv run pytest tests/ -v
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index ade462a..d727020 100644
--- a/.gitignore
+++ b/.gitignore
@@ -17,3 +17,11 @@
# dataset (removing cause I'm no longer hosting on railway and using my second laptop to double as a server)
/data
/predict
+
+# uv migrations
+.python-version
+.ruff_cache
+.venv
+
+# fonts dir for better analytics-overview
+/fonts
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index c9ee536..522ec01 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -1,50 +1,149 @@
-# Written on: 25/12/2025
+# Contributing to Bitsatards Bot
-## Contributions
+## Code-Contributions
-I think this goes without saying but still, you need to have a GitHub account to make any sort of contributions to this codebase which also applies to creating issues as well.
+### last written on 5/2/2026
-If this is your time using GitHub or making code-contributions to open-source projects, no worries! again I am listing how you can still contribute to this project:
+Thank you for considering to contribute, Whether you are fixing a bug or adding a new feature, your help is appreciated highly.
-1. proceed to fork this [repository](https://github.com/PranavU-Coder/bitsatards_bot)
-2. please navigate repositories listed under your profile where you should be able to find this codebase listed as a 'fork', clone that repository under **YOUR** profile and start working on it locally.
-3. install all necessary dependencies by running:
+If you are new to open-source or GitHub, follow the steps below to get started ->
- ```bash
- pip install -r requirements.txt
- ```
+1.proceed to fork this [repository](https://github.com/PranavU-Coder/bitsatards_bot) and clone it locally:
-4. once any changes are made to the codebase from your end, make sure to 'commit' those changes to your forked repository, once the codebase you have modified seems aptly ready for being pushed to main codebase (the one which is under mine) proceed to create a 'pull-request'.
+```bash
+git clone https://github.com/{replace with your username}/bitsatards_bot.git
+cd bitsatards_bot
+```
-**PLEASE NOTE** that a pull-request will be accepted only and only if the code changes have been tested and were successful locally otherwise it will be **CLOSED**, please remove comments and any irrelevant statements that would be asked in template.md at time of creation of pull-request
+2.create a branch for your purpose of work: feat/fix:
+
+```bash
+git checkout -b feat/feat-name
+# or
+git checkout -b fix/bug-name
+```
+
+3.this project has **recently** migrated from pip to uv thus it is important to have uv installed on your system.
+
+```bash
+uv sync
+```
+
+This command will automatically create a virtual environment and install all required packages listed in the uv.lock file.
+
+3.Dev Standards:
+
+(1) Run
+
+```bash
+uv run ruff format .
+```
+
+before committing.
+
+(2) Ensure
+
+```bash
+uv run ruff check .
+```
+
+passes without errors.
+
+(3) Run tests locally
+
+```bash
+uv run pytest
+```
+
+A passing build is required for any merge.
+
+(4) Commit Messages: Conventional Commits Please, prefix your messages accordingly:
+
+```text
+ feat: for new features.
+ fix: for bug fixes.
+ docs: for documentation/non-code changes.
+```
+
+4.commit your changes to your branch.
+
+push your changes to your fork.
+
+open a Pull Request (PR) against the master branch of the original repository.
+
+Wait for the automated tests to pass. Once reviewed they will be merged.
+
+Make sure to appropriately add/remove lines of the PULL_REQUEST_TEMPLATE.md which align to your changes.
-at the moment, I alone am the codeowner of this codebase hence mostly it will be reviewing the pull-requests and approve if it fits with the existing codebase, assuming the scope of this project increases, I'm more than happy to give ownership of this codebase to any enthusiastic contributor who is willing to do code-reviews.
+At the moment, I alone am the codeowner of this codebase hence mostly it will be reviewing the pull-requests and approve if it fits with the existing codebase, assuming the scope of this project increases, I'm more than happy to give ownership of this codebase to any enthusiastic contributor who is willing to do code-reviews.
-please make sure that you are adding docstrings underneath your functions or any classes if you are planning to define as it will be easier for other developers to read and understand, similarly apply strong type annotations wherever possible, although again honestly at the time of writing this, I myself am guilty of not doing this so I will be working on this as well.
+More Recently, I have installed CodeRabbit on this repository to do code-reviews alongside me.
-There are plans of integrating unit-tests using pytest to ensure any code-change can handle edge-cases regardless of what the diffs would be.
+Feature Documentation:
+
+Please add docstrings underneath any new functions or classes. While much of the code is still being documented, it would be a good precedent in terms of open-source projects.
+
+Please consider using strong type hinting for all function signatures and variables. I am of the opinion and in the movement to make this project to be in the direction of 100%-type safe
## Features & Bug Reports
-Starting with a pretty obvious phase of development, which is what features or problems will this project be undertaking so to put it shortly, please check the [roadmap](https://github.com/users/PranavU-Coder/projects/9) page to get an idea of what is being actively worked on and what would be worked on in the future.
+Before contributing, review the [Roadmap](https://github.com/users/PranavU-Coder/projects/9) first to understand current priorities and exisitng issues that are being worked on.
+
+For raising issues, they must follow these criterias to be considered:
-if you want any feature to be implemented please state **explicitly why exactly it is important** under the [issues](https://github.com/PranavU-Coder/bitsatards_bot/issues) tab by creating an issue under the same mentioned, similarly in bug-related issues please state where exactly is the bug occurring and how to reproduce from the developer's side so it can be worked upon to fix it.
+Feature Requests: You must explicitly justify why the feature is necessary to be implemented.
+
+Bug Reports: You must provide a clear "Steps to Reproduce" section (check figure below). If the developer cannot replicate the bug based on your description, the issue will be marked as invalid.
+
+Out of Scope (These will not be considered): Do **NOT** raise issues regarding the website's UI or functionality. This codebase is a **MONO-REPO** and is hence independent of the web project; such issues will be closed without review.
**PLEASE NOTE**:
-(1) any issue with website's ui or functionality shouldn't be raised and as such any issues raised on that request will **NOT** be reviewed and closed as this codebase is independent from the one in which the website is going to be made in.
+This bot is currently self-hosted through my old laptop which doubles as a server rather than a third-party cloud-service provider which leads to uptime problems.
+
+With hostel network dependencies, there is no way to resolve downtime-issues specially if I am not near the laptop which is hosting the bot.
-(2) discord bot being inactive at some portions of the day, now the reason why I chose to clear this up right now is because **I AM NOT USING ANY THIRD-PARTY CLOUD SERVICE PROVIDER** for the continuous deployment and execution of the discord bot and am rather using my old laptop as a full-time server to run my programs 24/7 out of which this bot is one of them, once I return back to hostel there would be issues of running this bot particularly given how bad my hostel wifi is (since it frequently goes out) and as such if I'm not available when this occurs there is no way for me to fix it immediately.
+**Possible Solution**: While orchestration for backup service providers is a goal (using good MLOps principles), it has not yet been implemented.
-there is a fix to the second issue by adopting good MLOps principles and orchestrating deployment of a backup service provider when one goes out, however at the time of writing this I'm not skilled enough to navigate through this and implement it.
+Please do **NOT** open issues regarding temporary bot inactivity. These are known infrastructure limitations, not code problems.
## Pipeline
-The idea of execution-workflow will be greatly discussed in the second-version/iteration of this bot to ensure other open-source maintainers who might not be traditionally from an ML or a DS background can still maintain this codebase actively without much external help/assistance.
+The project follows a structured execution workflow designed for maintainability. Even without a massive background in Data Science, contributors can help updating the bot's by interacting with scripts:
+
+### Data-Acquisition && Machine-Learning
+
+**data_pipeline.py**: This scrapes official BITS-admission sites containing cutoff scores for particular years and converts raw data into standardized CSV files which can be then put in for model-training.
+
+**helper_notebook.ipynb**: This contains code for the "model"—currently focused on min-max scaling applications so ... it is more of a statistical-validator than a traditional machine-learning model to process cutoff trends.
+
+**predictions.py**: This bridges between data-science and ML aspect of this repository with the production-aspect. It takes the constraints (user inputs) and applies the model logic to generate results. Note: This script is currently volatile and subject to change as the model logic matures.
+
+### Production
+
+**main.py**: Contains actual code for the bitsatards-discord-bot. It handles events, commands, and integrates with the prediction and database services.
+
+analytics.py: Handles data visualization for the bot's features.
+
+### Databases
+
+/services and /database concerns with the time-tracking feature which is a misc. feature of this bot using postgres as its database and SQLAlchemy as ORM. There is quite a bit of work to do here, but that will be halted till 3.0.0 release.
+
+**init_db.py** helps initializing database on your system with necessary columns.
+
+How to contribute to the flow:
+
+If you are adding a feature, identify where it sits. For example:
+
+A new way to view/formulate data -> analytics.py.
+
+Model-Improvement -> helper_notebook.ipynb
+
+Bot Command -> Modify main.py.
## Discussions
diff --git a/README.md b/README.md
index d411bd1..1c996eb 100644
--- a/README.md
+++ b/README.md
@@ -1,43 +1,96 @@
+
+
+
+
+
+
# Introducing Bitsatards-Bot
This src aims to be the discord bot for r/Bitsatards server, this bot serves the purpose of predicting cutoffs for upcoming bitsat examinations on basis of past-year trends (taking account from 2013) and can supports all of the commands which was planned in the second release (9/1/26):
-1. plot < campus > - plots the scores of past years cutoffs across diff campuses.
-2. plot < campus >, < branch > - plots the cutoff values wrt a branch of a campus that is required by the end-user.
-3. predict < campus (optional) >, < scenario (optional) > - sends the user the predicted cutoff values for branches of all campuses (truncated due constraints) or specific campus based on worst, best or most-likely scenario.
-4. resources - sends user resources for preparation of this examination.
-5. help - shows all possible commands that are available with example implementation in event that output is not available to the user for some unforseen reason.
+| Category | Command | Usage Example | Description |
+| :--- | :--- | :--- | :--- |
+| **Data Viz** | `!!plot` | `!!plot Pilani` | Generates a trend plot for all branches in a specific campus. |
+| | `!!plot-branch` | `!!plot-branch CSE Pilani` | Plots historical cutoffs for a specific branch at a specific campus. |
+| | `!!select` | `!!select 2024 Goa` | Fetches and displays a snapshot of cutoffs for a specific year. |
+| **Predictions** | `!!predict` | `!!predict Pilani worst` | Returns 2026 predictions based on Worst, Best, or Most-Likely scenarios. |
+| **Exam Info** | `!!sy` | `!!sy` | Provides the official BITSAT syllabus PDF link. |
+| | `!!da` | `!!da` | Displays important dates for BITSAT 2026 Session 1 & 2. |
+| | `!!resources` | `!!resources` | Links to the Reddit wiki and curated Discord resource channels. |
+| **Tracking** | `!!time` | `!!time` | Shows a countdown to your saved exam date. |
+| | `!!time -s` | `!!time -s 15-04-2026` | Sets/Updates your personal BITSAT exam date. |
+| | `!!time -r` | `!!time -r` | Resets your saved exam date from the database. |
+| **Misc** | `!!ypt` | `!!ypt` | Shares the Yeolpumta (YPT) study group link and password. |
Currently the bot is deployed through my second laptop which doubles as a server purely to run programs 24/7 to avoid any middleman (cloud service providers) and charges.
## Results Thus Far!
+
*Pilani campus plot from 2013*
------------------------------------------------------------------------------
+
*CSE plot for Pilani from 2013*
------------------------------------------------------------------------------
+
*probable values for Pilani campus in 2026*
-The model in effect for prediction is a polynomial-regression of degree:2 which was the best methodology among every other approach.
+The model in effect for prediction is a polynomial-regression of degree:2, this approach provided the lowest mean squared error compared to other linear/ensemble approaches.
+
+> [!NOTE]
+> I would like to clarify that this acts more as a statistical-validator than a plain machine-learning model as not much variance factors and features have been computed yet (discussed below).
+
+## Tech Stack
+
+[](https://python.org)
+[](https://discordpy.readthedocs.io/)
+[](https://github.com/astral-sh/uv)
+[](https://pandas.pydata.org/)
+[](https://scikit-learn.org/)
+[](https://matplotlib.org/)
+[](https://numpy.org/)
+[](https://www.postgresql.org/)
+[](https://www.sqlalchemy.org/)
+[](https://github.com/astral-sh/ruff)
+[](https://docs.pytest.org/)
## Future Plans
There are plans to experiment by finding causation and co-relations with cutoffs of other entrance examinations such as: JEE however I don't think I will be able to complete it in time of announcement of this project.
-The dataset is as follows for to work with: https://www.kaggle.com/datasets/pranavunni/bitsat-cutoff-dataset-2017-2025 for anyone willing to have their own spin-off to this.
+The dataset is as follows for to work with: [dataset](!https://www.kaggle.com/datasets/pranavunni/bitsat-cutoff-dataset-2017-2025) for anyone willing to have their own spin-off to this.
By mid-february 2026:
-1. a general-purpose website will be made public for everyone, in specific targetting people who prefer not using discord.
-2. rewriting wiki which explains the thought process, procedure and everything that went in to this project (which will go the working/ section of the website as well) and a reformat the existing wiki to CONTRIBUTIONS.md which will serve as a general guideline on contributing without breaking src.
+1. a general-purpose website will be made public for everyone, in specific targetting people who prefer not using discord (We are almost there!)
+
+After public-announcement & promotion, I will be back to implement various features and factors I have pinpointed thus far which can help in model-predictions more than anything.
+
+> ## Contributing
+> This project is developed hoping to be a community effort. If you'd like to improve the model or add features, please check our [CONTRIBUTING.md](./CONTRIBUTING.md) for general guidelines.
+>
+> ---
+> *Licensed under Apache 2.0*
+
+
+
+
+
+ 
+
+
+
+Contributors till now:
-## Apache 2.0 License
+
+ 
+
-Since this repository is licensed under Apache 2.0 license, we welcome collaborations and any intentions to help the community grow.
+Made with [contrib.rocks](https://contrib.rocks).
\ No newline at end of file
diff --git a/analytics.py b/analytics.py
index 4ede409..472ac97 100644
--- a/analytics.py
+++ b/analytics.py
@@ -2,228 +2,366 @@
import seaborn as sns
import matplotlib
-matplotlib.use('Agg')
+
+matplotlib.use("Agg")
import matplotlib.pyplot as plt
+from matplotlib.ticker import MaxNLocator
+import matplotlib.font_manager as fm
import os
import glob
+import io
+
+import concurrent.futures
+from functools import lru_cache
+
+# seaborn typesettings for more visually-pleasing plots
+sns.set_style("whitegrid")
+sns.set_context("notebook", font_scale=1.1)
+
+
+def load_custom_font(font_dir="fonts", font_name="JetBrainsMono-Regular.ttf"):
+ font_path = os.path.join(font_dir, font_name)
+ if os.path.exists(font_path):
+ fm.fontManager.addfont(font_path)
+ prop = fm.FontProperties(fname=font_path)
+ return prop.get_name()
+ return None
+
-data = glob.glob(os.path.join('data/', '*.csv'))
-df = pd.concat([pd.read_csv(f) for f in data], ignore_index=True)
+custom_font = load_custom_font()
+if custom_font:
+ plt.rcParams["font.family"] = custom_font
+
+# loading so many csv files at once can cause slow startups so parallelizing loadups
+def load_data_parallel(path_pattern):
+ files = glob.glob(path_pattern)
+ if not files:
+ print(f"warning: no files found matching {path_pattern}")
+ return pd.DataFrame(columns=["campus", "branch", "marks", "year"])
+
+ with concurrent.futures.ThreadPoolExecutor() as executor:
+ results = list(executor.map(pd.read_csv, files))
+
+ return pd.concat(results, ignore_index=True)
+
+
+# branch-aliasing for better ux
def load_branch_mappings(filepath="branch_names.txt"):
- """
- load branch name mappings from 'branch_names' txt file.
- """
-
alias_to_actual = {}
actual_to_alias = {}
-
- with open(filepath, 'r') as f:
+ if not os.path.exists(filepath):
+ return {}, {}
+ with open(filepath, "r") as f:
for line in f:
line = line.strip()
- if ':' in line:
- full_name, alias = line.split(':', 1)
+ if ":" in line:
+ full_name, alias = line.split(":", 1)
full_name = full_name.strip()
alias = alias.strip()
-
alias_to_actual[alias.lower()] = full_name
actual_to_alias[full_name.lower()] = alias
-
return alias_to_actual, actual_to_alias
+
def normalize_branch_name(user_input, alias_to_full):
- """
- convert user input (shorthand or full name) to the full branch name.
- returns the full name if found, otherwise None.
- """
-
user_input = user_input.strip().lower()
-
if user_input in alias_to_full:
return alias_to_full[user_input]
-
for alias, full_name in alias_to_full.items():
if full_name.lower() == user_input:
return full_name
-
return None
-alias_to_actual, actual_to_alias = load_branch_mappings('branch_names.txt')
-def plot_marks_by_campus(campus_name):
- """
- plot all branches for a given campus
- """
-
- campus_name = campus_name.strip().lower()
+data_path = os.path.join("data", "analysis_data", "*.csv")
+df = load_data_parallel(data_path)
- filtered_df = df[df['campus'].str.lower() == campus_name]
+alias_to_actual, actual_to_alias = load_branch_mappings("branch_names.txt")
- plt.figure(figsize=(12, 7))
- sns.lineplot(data=filtered_df, x='year', y='marks', hue='branch')
- plt.title(f'Marks Trend for {campus_name.title()}')
- plt.xlabel('Year')
- plt.ylabel('Marks')
- plt.legend(title='Branch', bbox_to_anchor=(1.05, 1), loc='upper left')
- plt.tight_layout()
- plt.savefig(f'{campus_name}_marks_trend.png')
- plt.close()
+PREDICTIONS = {}
+_pred_files = {
+ "worst": "predict/worst_case.csv",
+ "most-likely": "predict/most_likely_case.csv",
+ "best": "predict/best_case.csv",
+}
+for key, filepath in _pred_files.items():
+ if os.path.exists(filepath):
+ try:
+ p_df = pd.read_csv(filepath)
+ if "campus" in p_df.columns:
+ p_df["campus"] = p_df["campus"].str.title()
+ PREDICTIONS[key] = p_df
+ except Exception as e:
+ print(f"error loading {filepath}: {e}")
-def plot_marks_by_branch(campus_name, branch):
- """
- plots marks of a particular branch in a specific BITS campus, uses comma as a separator to indicate respective fields.
- """
- campus_name = campus_name.strip().lower()
-
- normalized_branch = normalize_branch_name(branch, alias_to_actual)
-
- if normalized_branch is None:
- print(f"warning: unknown branch '{branch}'")
- return None
+# this for returning raw bytes
+def _tabulate_to_bytes(data, headers, limit=25):
+ top_rows = data[:limit]
+ num_columns = len(headers)
- filtered_df = df[
- (df['campus'].str.lower() == campus_name) &
- (df['branch'].str.lower() == normalized_branch.lower())
- ]
+ header_color = "#40466e"
+ row_colors = ["#f8f9fa", "#ffffff"]
+ edge_color = "black"
+ text_color = "#333333"
+ header_text_color = "#ffffff"
- if filtered_df.empty:
- return None
+ row_height = 0.5
+ fig_height = (len(top_rows) + 1) * row_height + 0.5
- plt.figure(figsize=(12, 7))
- sns.lineplot(data=filtered_df, x='year', y='marks')
- plt.title(f"Marks Trend for {normalized_branch.title()} in {campus_name.title()}")
- plt.xlabel('Year')
- plt.ylabel('Marks')
- plt.tight_layout()
-
- safe_branch = normalized_branch.replace(' ', '_').replace('.', '')
- filename = f"{campus_name}_{safe_branch}_marks_trend.png"
-
- plt.savefig(filename)
- plt.close()
-
- return filename
+ fig, ax = plt.subplots(figsize=(10, fig_height))
+ ax.axis("off")
+ ax.axis("tight")
-def tabulate(data, headers, limit=25, filename=None):
- """
- general function to create a table plot from data.
- """
+ col_widths = [0.15, 0.6, 0.15, 0.1]
+ if len(col_widths) != num_columns:
+ col_widths = [1.0 / num_columns] * num_columns
- # truncate after the set-limit
- top_rows = data[:limit]
-
- fig_height = len(top_rows) * 0.4 + 1.2
-
- fig, ax = plt.subplots(figsize=(10, fig_height))
- ax.axis('tight')
- ax.axis('off')
-
- num_cols = len(headers)
- col_widths = [0.15, 0.50, 0.15, 0.20]
-
table = ax.table(
cellText=top_rows,
colLabels=headers,
- cellLoc='center',
- loc='center',
- colColours=["#40466e"] * num_cols,
- colWidths=col_widths
+ cellLoc="center",
+ loc="center",
+ colWidths=col_widths,
)
-
+
table.auto_set_font_size(False)
- table.set_fontsize(12)
- table.scale(1.2, 1.8)
-
+ table.set_fontsize(16)
+ table.scale(1, 2.0)
+
for (row, col), cell in table.get_celld().items():
+ cell.set_edgecolor(edge_color)
+ cell.set_linewidth(1)
if row == 0:
- cell.set_text_props(weight='bold', color='white')
+ cell.set_facecolor(header_color)
+ cell.set_text_props(weight="bold", color=header_text_color)
+ cell.set_height(0.08)
+ else:
+ cell.set_facecolor(row_colors[row % 2])
+ cell.set_text_props(color=text_color)
- if filename is None:
- filename = 'table_output.png'
- plt.savefig(filename, bbox_inches='tight', dpi=150, pad_inches=0.1)
+ buf = io.BytesIO()
+ plt.tight_layout()
+ plt.savefig(
+ buf,
+ format="png",
+ bbox_inches="tight",
+ dpi=150,
+ facecolor="white",
+ pad_inches=0.1,
+ )
plt.close()
-
- return filename
+ return buf.getvalue()
-def select(limit=25, year=None, campus_filter=None):
- """
- to get a more detailed-fetch for cutoff values with regards to a particular campus for a specified year.
- """
- if year is None:
- print("please specify a year")
+# using LRU-Caching techniques for faster generation if command has been accessed prior
+@lru_cache(maxsize=32)
+def _get_campus_plot_bytes(campus_name):
+ campus_name = campus_name.strip().lower()
+ filtered_df = df[df["campus"].str.lower() == campus_name]
+
+ if filtered_df.empty:
return None
-
- csv_file = f"data/cutoff_{year}.csv"
-
- try:
- df_cutoff = pd.read_csv(csv_file)
- headers = df_cutoff.columns.tolist()
- data = df_cutoff.values.tolist()
- except FileNotFoundError:
- print(f"file not found: {csv_file}")
- print(f"no cutoff data available for year: {year}")
+
+ fig, ax = plt.subplots(figsize=(12, 8), dpi=150)
+ sns.lineplot(
+ data=filtered_df,
+ x="year",
+ y="marks",
+ hue="branch",
+ marker="o",
+ markersize=6,
+ linewidth=2.5,
+ palette="tab10",
+ ax=ax,
+ )
+ sns.despine()
+ ax.set_title(
+ f"Cutoff Trends: {campus_name.title()}",
+ loc="left",
+ fontsize=16,
+ fontweight="bold",
+ pad=20,
+ )
+ ax.set_xlabel("Year", fontsize=12, labelpad=10)
+ ax.set_ylabel("Marks", fontsize=12, labelpad=10)
+ ax.xaxis.set_major_locator(MaxNLocator(integer=True))
+ ax.legend(title="Branch", bbox_to_anchor=(1.02, 1), loc="upper left", frameon=False)
+
+ buf = io.BytesIO()
+ plt.tight_layout()
+ plt.savefig(buf, format="png", bbox_inches="tight", facecolor="white")
+ plt.close()
+ return buf.getvalue()
+
+
+@lru_cache(maxsize=64)
+def _get_branch_plot_bytes(campus_name, branch_name):
+ normalized_branch = normalize_branch_name(branch_name, alias_to_actual)
+ if not normalized_branch:
return None
-
+
+ campus_name = campus_name.strip().lower()
+ filtered_df = df[
+ (df["campus"].str.lower() == campus_name)
+ & (df["branch"].str.lower() == normalized_branch.lower())
+ ]
+ if filtered_df.empty:
+ return None
+
+ fig, ax = plt.subplots(figsize=(10, 6), dpi=150)
+ sns.lineplot(
+ data=filtered_df,
+ x="year",
+ y="marks",
+ marker="o",
+ markersize=9,
+ linewidth=3,
+ color="#2E86AB",
+ ax=ax,
+ )
+ sns.despine()
+ min_marks = filtered_df["marks"].min()
+ max_marks = filtered_df["marks"].max()
+ ax.set_ylim(min_marks - 15, max_marks + 15)
+ ax.xaxis.set_major_locator(MaxNLocator(integer=True))
+ ax.text(
+ x=0,
+ y=1.02,
+ s=f"{campus_name.title()} Campus",
+ transform=ax.transAxes,
+ fontsize=12,
+ color="gray",
+ )
+ ax.set_xlabel("Year", fontsize=11)
+ ax.set_ylabel("Marks", fontsize=11)
+
+ if len(filtered_df) > 0:
+ ax.axhline(y=min_marks, color="#e63946", linestyle="--", alpha=0.5, linewidth=1)
+ ax.axhline(y=max_marks, color="#2a9d8f", linestyle="--", alpha=0.5, linewidth=1)
+ bbox_props = dict(boxstyle="round,pad=0.3", fc="white", ec="gray", alpha=0.9)
+ ax.text(
+ filtered_df["year"].min(),
+ max_marks,
+ f"Max: {max_marks:.0f}",
+ va="center",
+ ha="right",
+ fontsize=10,
+ color="#2a9d8f",
+ fontweight="bold",
+ bbox=bbox_props,
+ )
+ ax.text(
+ filtered_df["year"].min(),
+ min_marks,
+ f"Min: {min_marks:.0f}",
+ va="center",
+ ha="right",
+ fontsize=10,
+ color="#e63946",
+ fontweight="bold",
+ bbox=bbox_props,
+ )
+
+ buf = io.BytesIO()
+ plt.tight_layout()
+ plt.savefig(buf, format="png", bbox_inches="tight", facecolor="white")
+ plt.close()
+ return buf.getvalue()
+
+
+@lru_cache(maxsize=32)
+def _get_select_table_bytes(year, campus_filter, limit):
+ target_df = df[df["year"] == year]
+ if target_df.empty:
+ return None
+
if campus_filter:
- target = campus_filter.strip().lower()
- filtered_data = [row for row in data if str(row[0]).lower() == target]
-
- if not filtered_data:
- print(f"No data found for campus: {campus_filter}")
+ target_campus = campus_filter.strip().lower()
+ target_df = target_df[target_df["campus"].str.lower() == target_campus]
+ if target_df.empty:
return None
-
- filename = f"cutoff_{year}_{target}.png"
- else:
- filtered_data = data
- filename = f"cutoff_{year}_all.png"
-
- return tabulate(filtered_data, headers, limit=limit, filename=filename)
-
-def get_predictions(limit=25, campus_filter=None, situation=None):
- """
- get predictions now based on hypothetical scenarios: "worst", "most-likely" and "best" case.
- instead of hardcoding data like previously done, we will just use values we obtained from predict/..csv files.
- by default we will consider difficulty: most-likely if user doesn't specify.
- by default we will assume top-entries in the file if user doesn't specify campus.
- """
-
-
- map = {
- 'worst': 'predict/worst_case.csv',
- 'most-likely': 'predict/most_likely_case.csv',
- 'best': 'predict/best_case.csv'
- }
-
- if situation is None:
- situation = 'most-likely'
-
- csv_file = map.get(situation.lower())
-
- if csv_file is None:
- print(f"invalid usage, please use 'worst', 'most-likely' (or leave blank for this), or 'best'")
- return None
-
- try:
- df_pred = pd.read_csv(csv_file)
- headers = df_pred.columns.tolist()
- data = df_pred.values.tolist()
- except FileNotFoundError:
- print(f"file not found: {csv_file}")
+
+ target_df = target_df.sort_values(by="marks", ascending=False)
+
+ table_data = target_df[["campus", "branch", "marks", "year"]].copy()
+ table_data["campus"] = table_data["campus"].str.title()
+ final_data = table_data.values.tolist()
+ headers = ["Campus", "Course", "Marks", "Year"]
+
+ return _tabulate_to_bytes(final_data, headers, limit=limit)
+
+
+@lru_cache(maxsize=32)
+def _get_prediction_bytes(situation, campus_filter, limit):
+ df_pred = PREDICTIONS.get(situation.lower())
+ if df_pred is None:
return None
-
+
+ target_df = df_pred.copy()
+
if campus_filter:
- target = campus_filter.strip().lower()
- filtered_data = [row for row in data if str(row[0]).lower() == target]
-
- if not filtered_data:
+ target = campus_filter.strip().title()
+ if "campus" in target_df.columns:
+ target_df = target_df[target_df["campus"] == target]
+ if target_df.empty:
return None
-
- filename = f"pred_2026_{target}_{situation}.png"
- else:
- filtered_data = data
- filename = f"pred_2026_all_{situation}.png"
-
- return tabulate(filtered_data, headers, limit=limit, filename=filename)
\ No newline at end of file
+
+ if "marks" in target_df.columns:
+ target_df = target_df.sort_values(by="marks", ascending=False)
+
+ table_data = target_df.values.tolist()
+ headers = [h.title() for h in target_df.columns.tolist()]
+
+ return _tabulate_to_bytes(table_data, headers, limit=limit)
+
+
+# these are for recieving cached bytes
+def plot_marks_by_campus(campus_name):
+ img_bytes = _get_campus_plot_bytes(campus_name)
+ if img_bytes is None:
+ return None
+ return io.BytesIO(img_bytes)
+
+
+def plot_marks_by_branch(campus_name, branch):
+ normalized = normalize_branch_name(branch, alias_to_actual)
+ if not normalized:
+ return None
+
+ img_bytes = _get_branch_plot_bytes(campus_name, normalized)
+ if img_bytes is None:
+ return None
+ return io.BytesIO(img_bytes)
+
+
+def select(limit=25, year=None, campus_filter=None):
+ if year is None:
+ return None
+
+ # the cache key MUST BE HASHABLE
+ img_bytes = _get_select_table_bytes(year, campus_filter, limit)
+ if img_bytes is None:
+ return None
+ return io.BytesIO(img_bytes)
+
+
+def get_predictions(limit=25, campus_filter=None, situation="most-likely"):
+ img_bytes = _get_prediction_bytes(situation, campus_filter, limit)
+ if img_bytes is None:
+ return None
+ return io.BytesIO(img_bytes)
+
+
+# this is for discord-CDNs
+URL_CACHE = {}
+
+
+def get_cached_url(key):
+ return URL_CACHE.get(key)
+
+
+def save_url_to_cache(key, url):
+ URL_CACHE[key] = url
diff --git a/data_pipeline.py b/data_pipeline.py
index f319c29..c8a23a0 100644
--- a/data_pipeline.py
+++ b/data_pipeline.py
@@ -13,47 +13,49 @@
year = 2025
html_text = requests.get(URL, verify=False).text
-soup = BeautifulSoup(html_text, 'lxml')
+soup = BeautifulSoup(html_text, "lxml")
# to modify accordingly
-div = soup.find('div', id='2025-2026')
+div = soup.find("div", id="2025-2026")
data = []
-tables = div.find_all('table')
+tables = div.find_all("table")
for table in tables:
- rows = table.find_all('tr')
-
+ rows = table.find_all("tr")
+
# skip any tiny-tables in the way
if len(rows) < 3:
continue
-
+
for row in rows[1:]:
- cols = row.find_all('td')
-
+ cols = row.find_all("td")
+
if len(cols) >= 4:
campus = cols[0].get_text(strip=True)
program = cols[1].get_text(strip=True)
- cutoff = cols[2].get_text(strip=True)
-
- if 'Goa' in campus:
- campus = 'Goa'
- elif campus not in ['Pilani', 'Goa', 'Hyderabad']:
- continue
+ cutoff = cols[2].get_text(strip=True)
+
+ if "Goa" in campus:
+ campus = "Goa"
+ elif campus not in ["Pilani", "Goa", "Hyderabad"]:
+ continue
# validating if data row
- if cutoff.isdigit() and program.lower() != 'program':
- data.append({
- 'campus': campus,
- 'branch': program,
- 'marks': int(cutoff),
- 'year': year
- })
+ if cutoff.isdigit() and program.lower() != "program":
+ data.append(
+ {
+ "campus": campus,
+ "branch": program,
+ "marks": int(cutoff),
+ "year": year,
+ }
+ )
if data:
df = pd.DataFrame(data)
df = df.drop_duplicates()
- df.to_csv('bitsat_cutoffs.csv', index=False)
+ df.to_csv("bitsat_cutoffs.csv", index=False)
print(df.head(10))
else:
print("\ntask failed")
diff --git a/database/connection.py b/database/connection.py
index 56c6c74..10b6368 100644
--- a/database/connection.py
+++ b/database/connection.py
@@ -5,25 +5,22 @@
load_dotenv()
-DATABASE_URL = os.getenv('DATABASE_URL')
+DATABASE_URL = os.getenv("DATABASE_URL")
if not DATABASE_URL:
raise ValueError("DATABASE_URL not found")
engine = create_engine(
DATABASE_URL,
- echo=False,
- pool_size=5,
- max_overflow=10,
+ echo=False,
+ pool_size=5,
+ max_overflow=10,
pool_pre_ping=True,
- pool_recycle=3600
+ pool_recycle=3600,
)
-SessionLocal = sessionmaker(
- bind=engine,
- autocommit=False,
- autoflush=False
-)
+SessionLocal = sessionmaker(bind=engine, autocommit=False, autoflush=False)
+
def get_db():
"""
diff --git a/database/models.py b/database/models.py
index f9df29d..9676089 100644
--- a/database/models.py
+++ b/database/models.py
@@ -3,9 +3,10 @@
Base = declarative_base()
+
class UserExam(Base):
- __tablename__ = 'bitsat'
-
+ __tablename__ = "bitsat"
+
id = Column(Integer, primary_key=True)
user_id = Column(BigInteger, unique=True, nullable=False)
username = Column(String)
diff --git a/database/repository.py b/database/repository.py
index 6105d99..f021924 100644
--- a/database/repository.py
+++ b/database/repository.py
@@ -3,6 +3,7 @@
from .models import UserExam
from .connection import SessionLocal
+
class ExamRepository:
"""
handles database operations for user-exam tracking.
@@ -11,14 +12,14 @@ class ExamRepository:
def __init__(self):
self.db = SessionLocal()
- def set_user_exam(self, user_id: int, username: str, channel_id: int, exam_date: date) -> UserExam:
+ def set_user_exam(
+ self, user_id: int, username: str, channel_id: int, exam_date: date
+ ) -> UserExam:
"""
set/update user's exam date
"""
- user_exam = self.db.query(UserExam).filter(
- UserExam.user_id == user_id
- ).first()
+ user_exam = self.db.query(UserExam).filter(UserExam.user_id == user_id).first()
if user_exam:
user_exam.exam_date = exam_date
@@ -29,7 +30,7 @@ def set_user_exam(self, user_id: int, username: str, channel_id: int, exam_date:
user_id=user_id,
username=username,
channel_id=channel_id,
- exam_date=exam_date
+ exam_date=exam_date,
)
self.db.add(user_exam)
@@ -41,15 +42,13 @@ def get_user_exam(self, user_id: int) -> Optional[UserExam]:
get details.
"""
- return self.db.query(UserExam).filter(
- UserExam.user_id == user_id
- ).first()
+ return self.db.query(UserExam).filter(UserExam.user_id == user_id).first()
def get_all_users(self) -> List[UserExam]:
"""
get all users with exam dates.
"""
-
+
return self.db.query(UserExam).all()
def delete_user_exam(self, user_id: int) -> bool:
diff --git a/helper_notebook.ipynb b/helper_notebook.ipynb
index 129d12a..dadcef2 100644
--- a/helper_notebook.ipynb
+++ b/helper_notebook.ipynb
@@ -43,12 +43,12 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"id": "3222e3cf",
"metadata": {},
"outputs": [],
"source": [
- "data = glob.glob(os.path.join('data/', '*.csv'))\n",
+ "data = glob.glob(os.path.join(\"data/model_data\", \"*.csv\"))\n",
"df = pd.concat([pd.read_csv(f) for f in data], ignore_index=True)"
]
},
@@ -59,8 +59,8 @@
"metadata": {},
"outputs": [],
"source": [
- "feature_cols = ['campus', 'branch', 'year', 'difficulty']\n",
- "target_col = 'marks'\n",
+ "feature_cols = [\"campus\", \"branch\", \"year\", \"difficulty\"]\n",
+ "target_col = \"marks\"\n",
"\n",
"X = df[feature_cols]\n",
"y = df[target_col]"
@@ -73,8 +73,12 @@
"metadata": {},
"outputs": [],
"source": [
- "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3, random_state=42)\n",
- "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)"
+ "X_train, X_temp, y_train, y_temp = train_test_split(\n",
+ " X, y, test_size=0.3, random_state=42\n",
+ ")\n",
+ "X_val, X_test, y_val, y_test = train_test_split(\n",
+ " X_temp, y_temp, test_size=0.5, random_state=42\n",
+ ")"
]
},
{
@@ -94,9 +98,10 @@
"source": [
"preprocessor = ColumnTransformer(\n",
" transformers=[\n",
- " ('cat', OneHotEncoder(handle_unknown='ignore'), ['campus', 'branch']),\n",
- " ('num', StandardScaler(), ['year', 'difficulty'])\n",
- " ])"
+ " (\"cat\", OneHotEncoder(handle_unknown=\"ignore\"), [\"campus\", \"branch\"]),\n",
+ " (\"num\", StandardScaler(), [\"year\", \"difficulty\"]),\n",
+ " ]\n",
+ ")"
]
},
{
@@ -125,10 +130,10 @@
}
],
"source": [
- "print(f\"Dataset Split:\")\n",
- "print(f\"Train: {len(X_train)} ({len(X_train)/len(X)*100:.1f}%)\")\n",
- "print(f\"Val: {len(X_val)} ({len(X_val)/len(X)*100:.1f}%)\")\n",
- "print(f\"Test: {len(X_test)} ({len(X_test)/len(X)*100:.1f}%)\")"
+ "print(\"Dataset Split:\")\n",
+ "print(f\"Train: {len(X_train)} ({len(X_train) / len(X) * 100:.1f}%)\")\n",
+ "print(f\"Val: {len(X_val)} ({len(X_val) / len(X) * 100:.1f}%)\")\n",
+ "print(f\"Test: {len(X_test)} ({len(X_test) / len(X) * 100:.1f}%)\")"
]
},
{
@@ -182,26 +187,25 @@
"outputs": [],
"source": [
"def helper(model, X_train, y_train, X_val, y_val, X_test, y_test, model_name):\n",
- "\n",
" model.fit(X_train, y_train)\n",
- " \n",
+ "\n",
" y_train_pred = model.predict(X_train)\n",
" y_val_pred = model.predict(X_val)\n",
" y_test_pred = model.predict(X_test)\n",
- " \n",
+ "\n",
" metrics = {\n",
- " 'Model': model_name,\n",
- " 'Train RMSE': np.sqrt(mean_squared_error(y_train, y_train_pred)),\n",
- " 'Train MAE': mean_absolute_error(y_train, y_train_pred),\n",
- " 'Train R²': r2_score(y_train, y_train_pred),\n",
- " 'Val RMSE': np.sqrt(mean_squared_error(y_val, y_val_pred)),\n",
- " 'Val MAE': mean_absolute_error(y_val, y_val_pred),\n",
- " 'Val R²': r2_score(y_val, y_val_pred),\n",
- " 'Test RMSE': np.sqrt(mean_squared_error(y_test, y_test_pred)),\n",
- " 'Test MAE': mean_absolute_error(y_test, y_test_pred),\n",
- " 'Test R²': r2_score(y_test, y_test_pred)\n",
+ " \"Model\": model_name,\n",
+ " \"Train RMSE\": np.sqrt(mean_squared_error(y_train, y_train_pred)),\n",
+ " \"Train MAE\": mean_absolute_error(y_train, y_train_pred),\n",
+ " \"Train R²\": r2_score(y_train, y_train_pred),\n",
+ " \"Val RMSE\": np.sqrt(mean_squared_error(y_val, y_val_pred)),\n",
+ " \"Val MAE\": mean_absolute_error(y_val, y_val_pred),\n",
+ " \"Val R²\": r2_score(y_val, y_val_pred),\n",
+ " \"Test RMSE\": np.sqrt(mean_squared_error(y_test, y_test_pred)),\n",
+ " \"Test MAE\": mean_absolute_error(y_test, y_test_pred),\n",
+ " \"Test R²\": r2_score(y_test, y_test_pred),\n",
" }\n",
- " \n",
+ "\n",
" return metrics, y_test_pred"
]
},
@@ -242,8 +246,14 @@
"print(\"1. Linear Regression\")\n",
"model = LinearRegression()\n",
"metrics, trained_model = helper(\n",
- " model, X_train_scaled, y_train, X_val_scaled, y_val, X_test_scaled, y_test, \n",
- " 'Linear Regression'\n",
+ " model,\n",
+ " X_train_scaled,\n",
+ " y_train,\n",
+ " X_val_scaled,\n",
+ " y_val,\n",
+ " X_test_scaled,\n",
+ " y_test,\n",
+ " \"Linear Regression\",\n",
")\n",
"results.append(metrics)"
]
@@ -266,8 +276,14 @@
"print(\"2. Polynomial Regression\")\n",
"model = LinearRegression()\n",
"metrics, trained_model = helper(\n",
- " model, X_train_poly, y_train, X_val_poly, y_val, X_test_poly, y_test, \n",
- " 'Polynomial Regression'\n",
+ " model,\n",
+ " X_train_poly,\n",
+ " y_train,\n",
+ " X_val_poly,\n",
+ " y_val,\n",
+ " X_test_poly,\n",
+ " y_test,\n",
+ " \"Polynomial Regression\",\n",
")\n",
"results.append(metrics)"
]
@@ -290,8 +306,14 @@
"print(\"3. Ridge Regression\")\n",
"model = Ridge(alpha=1.0)\n",
"metrics, trained_model = helper(\n",
- " model, X_train_scaled, y_train, X_val_scaled, y_val, X_test_scaled, y_test, \n",
- " 'Ridge Regression'\n",
+ " model,\n",
+ " X_train_scaled,\n",
+ " y_train,\n",
+ " X_val_scaled,\n",
+ " y_val,\n",
+ " X_test_scaled,\n",
+ " y_test,\n",
+ " \"Ridge Regression\",\n",
")\n",
"results.append(metrics)"
]
@@ -314,8 +336,14 @@
"print(\"4. Random Forest\")\n",
"model = RandomForestRegressor(n_estimators=100, random_state=42, max_depth=10)\n",
"metrics, trained_model = helper(\n",
- " model, X_train_scaled, y_train, X_val_scaled, y_val, X_test_scaled, y_test, \n",
- " 'Random Forest'\n",
+ " model,\n",
+ " X_train_scaled,\n",
+ " y_train,\n",
+ " X_val_scaled,\n",
+ " y_val,\n",
+ " X_test_scaled,\n",
+ " y_test,\n",
+ " \"Random Forest\",\n",
")\n",
"results.append(metrics)"
]
@@ -338,8 +366,14 @@
"print(\"5. Gradient Boosting\")\n",
"model = GradientBoostingRegressor(n_estimators=100, random_state=42, max_depth=5)\n",
"metrics, trained_model = helper(\n",
- " model, X_train_scaled, y_train, X_val_scaled, y_val, X_test_scaled, y_test, \n",
- " 'Gradient Boosting'\n",
+ " model,\n",
+ " X_train_scaled,\n",
+ " y_train,\n",
+ " X_val_scaled,\n",
+ " y_val,\n",
+ " X_test_scaled,\n",
+ " y_test,\n",
+ " \"Gradient Boosting\",\n",
")\n",
"results.append(metrics)"
]
@@ -386,40 +420,44 @@
"source": [
"fig, axes = plt.subplots(2, 2, figsize=(15, 12))\n",
"\n",
- "axes[0, 0].barh(df_results['Model'], df_results['Test RMSE'], color='steelblue', alpha=0.8)\n",
- "axes[0, 0].set_xlabel('Test RMSE')\n",
- "axes[0, 0].set_title('Test RMSE by Model')\n",
+ "axes[0, 0].barh(\n",
+ " df_results[\"Model\"], df_results[\"Test RMSE\"], color=\"steelblue\", alpha=0.8\n",
+ ")\n",
+ "axes[0, 0].set_xlabel(\"Test RMSE\")\n",
+ "axes[0, 0].set_title(\"Test RMSE by Model\")\n",
"axes[0, 0].invert_yaxis()\n",
"\n",
- "axes[0, 1].barh(df_results['Model'], df_results['Test R²'], color='forestgreen', alpha=0.8)\n",
- "axes[0, 1].set_xlabel('Test R² Score')\n",
- "axes[0, 1].set_title('Test R² by Model')\n",
+ "axes[0, 1].barh(\n",
+ " df_results[\"Model\"], df_results[\"Test R²\"], color=\"forestgreen\", alpha=0.8\n",
+ ")\n",
+ "axes[0, 1].set_xlabel(\"Test R² Score\")\n",
+ "axes[0, 1].set_title(\"Test R² by Model\")\n",
"axes[0, 1].invert_yaxis()\n",
"\n",
"x = np.arange(len(df_results))\n",
"width = 0.25\n",
- "axes[1, 0].bar(x - width, df_results['Train RMSE'], width, label='Train', alpha=0.8)\n",
- "axes[1, 0].bar(x, df_results['Val RMSE'], width, label='Val', alpha=0.8)\n",
- "axes[1, 0].bar(x + width, df_results['Test RMSE'], width, label='Test', alpha=0.8)\n",
- "axes[1, 0].set_xlabel('Model')\n",
- "axes[1, 0].set_ylabel('RMSE')\n",
- "axes[1, 0].set_title('RMSE Comparison: Train vs Val vs Test')\n",
+ "axes[1, 0].bar(x - width, df_results[\"Train RMSE\"], width, label=\"Train\", alpha=0.8)\n",
+ "axes[1, 0].bar(x, df_results[\"Val RMSE\"], width, label=\"Val\", alpha=0.8)\n",
+ "axes[1, 0].bar(x + width, df_results[\"Test RMSE\"], width, label=\"Test\", alpha=0.8)\n",
+ "axes[1, 0].set_xlabel(\"Model\")\n",
+ "axes[1, 0].set_ylabel(\"RMSE\")\n",
+ "axes[1, 0].set_title(\"RMSE Comparison: Train vs Val vs Test\")\n",
"axes[1, 0].set_xticks(x)\n",
- "axes[1, 0].set_xticklabels(df_results['Model'], rotation=45, ha='right')\n",
+ "axes[1, 0].set_xticklabels(df_results[\"Model\"], rotation=45, ha=\"right\")\n",
"axes[1, 0].legend()\n",
"\n",
- "axes[1, 1].bar(x - width, df_results['Train R²'], width, label='Train', alpha=0.8)\n",
- "axes[1, 1].bar(x, df_results['Val R²'], width, label='Val', alpha=0.8)\n",
- "axes[1, 1].bar(x + width, df_results['Test R²'], width, label='Test', alpha=0.8)\n",
- "axes[1, 1].set_xlabel('Model')\n",
- "axes[1, 1].set_ylabel('R² Score')\n",
- "axes[1, 1].set_title('R² Comparison: Train vs Val vs Test')\n",
+ "axes[1, 1].bar(x - width, df_results[\"Train R²\"], width, label=\"Train\", alpha=0.8)\n",
+ "axes[1, 1].bar(x, df_results[\"Val R²\"], width, label=\"Val\", alpha=0.8)\n",
+ "axes[1, 1].bar(x + width, df_results[\"Test R²\"], width, label=\"Test\", alpha=0.8)\n",
+ "axes[1, 1].set_xlabel(\"Model\")\n",
+ "axes[1, 1].set_ylabel(\"R² Score\")\n",
+ "axes[1, 1].set_title(\"R² Comparison: Train vs Val vs Test\")\n",
"axes[1, 1].set_xticks(x)\n",
- "axes[1, 1].set_xticklabels(df_results['Model'], rotation=45, ha='right')\n",
+ "axes[1, 1].set_xticklabels(df_results[\"Model\"], rotation=45, ha=\"right\")\n",
"axes[1, 1].legend()\n",
"\n",
"plt.tight_layout()\n",
- "plt.savefig('plots/model_results.png', dpi=150)\n",
+ "plt.savefig(\"plots/model_results.png\", dpi=150)\n",
"plt.close()"
]
},
@@ -1133,10 +1171,12 @@
}
],
"source": [
- "model = Pipeline([\n",
- " ('poly', PolynomialFeatures(degree=2, include_bias=False)),\n",
- " ('regressor', LinearRegression())\n",
- "])\n",
+ "model = Pipeline(\n",
+ " [\n",
+ " (\"poly\", PolynomialFeatures(degree=2, include_bias=False)),\n",
+ " (\"regressor\", LinearRegression()),\n",
+ " ]\n",
+ ")\n",
"model.fit(X_train_scaled, y_train)"
]
},
@@ -1147,10 +1187,10 @@
"metadata": {},
"outputs": [],
"source": [
- "with open('models/model.pkl', 'wb') as f:\n",
+ "with open(\"models/model.pkl\", \"wb\") as f:\n",
" pickle.dump(model, f)\n",
"\n",
- "with open('models/scaler.pkl', 'wb') as f:\n",
+ "with open(\"models/scaler.pkl\", \"wb\") as f:\n",
" pickle.dump(preprocessor, f)"
]
}
diff --git a/init_db.py b/init_db.py
index 099c2d0..2fb237d 100644
--- a/init_db.py
+++ b/init_db.py
@@ -1,9 +1,11 @@
from database.models import Base
from database.connection import engine
+
def init_database():
Base.metadata.create_all(engine)
print("all database-tables were created successfully")
+
if __name__ == "__main__":
init_database()
diff --git a/main.py b/main.py
index cd6609a..5ee253b 100644
--- a/main.py
+++ b/main.py
@@ -2,8 +2,13 @@
from discord.ext import commands, tasks
import logging
from dotenv import load_dotenv
+
import os
-from datetime import date, datetime, time as dt_time, timezone, timedelta
+import re
+
+from datetime import datetime, time as dt_time, timezone, timedelta
+import asyncio
+from functools import partial
from services.exam_tracker import ExamTracker
from services.reminder import Reminder
@@ -11,257 +16,299 @@
load_dotenv()
+CAMPUSES = ["pilani", "goa", "hyderabad"]
+
exam_tracker = ExamTracker()
reminder_service = Reminder()
-token = os.getenv('DISCORD_TOKEN')
+token = os.getenv("DISCORD_TOKEN")
-handler = logging.FileHandler(filename='discord.log',encoding='utf-8',mode='w')
+handler = logging.FileHandler(filename="discord.log", encoding="utf-8", mode="w")
intents = discord.Intents.default()
intents.message_content = True
intents.members = True
-bot = commands.Bot(command_prefix='!!', intents=intents)
-bot.remove_command('help')
+bot = commands.Bot(command_prefix="!!", intents=intents)
+bot.remove_command("help")
DISCLAIMER_MSG = "all scores pre-**2022** have been standardized to **390**, so a score in **2021** which may have been **300** becomes **260** in current standards and settings of exam."
-# default is UTC & I don't live in that region.
IST = timezone(timedelta(hours=5, minutes=30))
-async def display(ctx, filename: str, disclaimer: bool = True, not_found_msg: str = None):
+
+async def display(ctx, cache_key, title, generator_func, filename, disclaimer=True):
"""
- sends a file to discord then cleanup.
+ generates a plot/table based on user-req throught generator_func availed in analytics.py, if cache-hit is found it immediately sends the embed of that to user.
disclaimer message is optional.
"""
- if filename and os.path.exists(filename):
- try:
- with open(filename, 'rb') as f:
- await ctx.send(file=discord.File(f, filename=os.path.basename(filename)))
- if disclaimer:
- await ctx.send(DISCLAIMER_MSG)
- return True
- finally:
- os.remove(filename)
- else:
- if not_found_msg:
- await ctx.send(not_found_msg)
- else:
- await ctx.send(f"error: could not generate or find file.")
- return False
+ cached_url = anal.get_cached_url(cache_key)
+
+ if cached_url:
+ print(f"cache hit: {cache_key}")
+ embed = discord.Embed(title=title)
+ embed.set_image(url=cached_url)
+ await ctx.send(embed=embed)
+ if disclaimer:
+ await ctx.send(DISCLAIMER_MSG)
+ return True
+
+ image_buffer = await generator_func
+
+ if image_buffer is None:
+ return None
+
+ try:
+ image_buffer.seek(0)
+ file = discord.File(fp=image_buffer, filename=filename)
+ sent_message = await ctx.send(file=file)
+ if disclaimer:
+ await ctx.send(DISCLAIMER_MSG)
+
+ if sent_message.attachments:
+ anal.save_url_to_cache(cache_key, sent_message.attachments[0].url)
+ print(f"cached: {cache_key}")
+
+ except Exception as e:
+ await ctx.send(f"error uploading: {e}")
+ return None
+ finally:
+ image_buffer.close()
+
+ return True
+
+
+def parse_campus(args):
+ clean_args = args.lower()
+ for c in CAMPUSES:
+ if c in clean_args:
+ return c, clean_args.replace(c, "", 1)
+ return None, clean_args
+
+
+async def send_dm(user_id, message):
+ try:
+ user = await bot.fetch_user(user_id)
+ await user.send(message)
+ print(f"sent DM fallback to {user.name}")
+ except Exception as e:
+ print(f"failed to DM user {user_id}: {e}")
-# this is for sending user reminders for their exam-time
@tasks.loop(time=dt_time(hour=9, minute=0, tzinfo=IST))
async def send_exam_reminders():
"""
send daily reminders in the channel where interaction with bot was made.
"""
-
+
reminder_service.reset_daily_tracking()
reminders = reminder_service.users_to_remind()
-
+
for reminder in reminders:
- channel = bot.get_channel(reminder['channel_id'])
-
+ channel = bot.get_channel(reminder["channel_id"])
+ msg = f"<@{reminder['user_id']}> {reminder['message']}"
+
if channel:
try:
- await channel.send(
- f"<@{reminder['user_id']}> {reminder['message']}"
+ await channel.send(msg)
+ print(
+ f"sent reminder to user {reminder['user_id']} in channel {channel.name}"
)
- print(f"sent reminder to user {reminder['user_id']} in channel {channel.name}")
except discord.Forbidden:
- print(f"no permission to send in channel {reminder['channel_id']}")
- try:
- user = await bot.fetch_user(reminder['user_id'])
- await user.send(reminder['message'])
- print(f"sent DM fallback to {user.name}")
- except Exception as dm_error:
- print(f"failed to DM user {reminder['user_id']}: {dm_error}")
+ print(f"no permission in channel {reminder['channel_id']}")
+ await send_dm(reminder["user_id"], reminder["message"])
except Exception as e:
- print(f"failed to send reminder to {reminder['user_id']}: {e}")
+ print(f"failed to send reminder: {e}")
else:
- print(f"channel {reminder['channel_id']} not found, falling back to DM")
- try:
- user = await bot.fetch_user(reminder['user_id'])
- await user.send(reminder['message'])
- print(f"sent DM fallback to {user.name}")
- except Exception as e:
- print(f"failed to send to {reminder['user_id']}: {e}")
+ print(f"channel {reminder['channel_id']} not found")
+ await send_dm(reminder["user_id"], reminder["message"])
+
@send_exam_reminders.before_loop
async def before_reminder():
await bot.wait_until_ready()
-# all bot events/commands from here on now
@bot.event
async def on_ready():
- """
- prints to the console screen to my server laptop to signal the bot is up and running.
- """
-
print("ready when you're")
if not send_exam_reminders.is_running():
send_exam_reminders.start()
-@bot.command()
-async def plot(ctx, campus_name: str):
- """
- uses the analytics file's function to generate plots to end-user only based on campus
- """
-
- try:
- await ctx.send(f"generating plot for {campus_name}")
- anal.plot_marks_by_campus(campus_name)
- filename = f"{campus_name.lower()}_marks_trend.png"
-
- await display(ctx, filename,
- not_found_msg=f"no data found for campus: {campus_name}")
- except Exception as e:
- await ctx.send(f"error generating plot: {str(e)}")
-@bot.command(name='plot-branch')
-async def plot_branch(ctx, *, args: str):
- """
- uses the analytics file's function to generate plots to end-user now on both campus and its respective branch.
- """
-
- try:
- if "," not in args:
- await ctx.send("**usage:** `!!plot-branch , `")
- return
-
- campus_raw, branch_raw = args.split(",", 1)
- campus = campus_raw.strip()
- branch = branch_raw.strip()
-
- await ctx.send(f"generating plot for **{branch}** in **{campus}**")
-
- filename = anal.plot_marks_by_branch(campus, branch)
-
- if filename is None:
- await ctx.send(f"no data found for **{branch}** in **{campus}**.")
- return
-
- await display(ctx, filename,
- not_found_msg="error: file generated but not found.")
-
- except Exception as e:
- await ctx.send(f"error: {str(e)}")
+@bot.command(name="plot")
+async def plot(ctx, *, args: str = None):
+ if not args:
+ return await ctx.send("Usage: `!!plot `")
+
+ campus, _ = parse_campus(args)
+
+ if not campus:
+ return await ctx.send("invalid campus. Please use Pilani, Goa, or Hyderabad.")
+
+ loop = asyncio.get_event_loop()
+ generator = loop.run_in_executor(None, partial(anal.plot_marks_by_campus, campus))
+
+ result = await display(
+ ctx,
+ cache_key=f"plot_{campus}",
+ title=f"{campus.title()} Cutoff Trends",
+ generator_func=generator,
+ filename=f"{campus}_plot.png",
+ )
+
+ if result is None:
+ await ctx.send(f"No data found for campus: {campus}")
+
+
+@bot.command(name="plot-branch")
+async def plot_branch(ctx, *, args: str = None):
+ if not args:
+ return await ctx.send("Usage: `!!plot-branch `")
+
+ campus, clean_args = parse_campus(args)
+
+ if not campus:
+ return await ctx.send("please specify a campus (Pilani, Goa, or Hyderabad).")
+
+ branch = clean_args.replace(",", "").strip()
+
+ if not branch:
+ return await ctx.send("please specify a branch name.")
+
+ await ctx.send(f"generating plot for **{branch}** in **{campus.title()}**...")
+
+ loop = asyncio.get_event_loop()
+ generator = loop.run_in_executor(
+ None, partial(anal.plot_marks_by_branch, campus, branch)
+ )
+
+ result = await display(
+ ctx,
+ cache_key=f"plot_branch_{campus}_{branch}",
+ title=f"{branch} - {campus.title()}",
+ generator_func=generator,
+ filename=f"{campus}_{branch}.png",
+ )
-@bot.command(name='select')
+ if result is None:
+ await ctx.send(f"no data found for **{branch}** in **{campus}**.")
+
+
+@bot.command(name="select")
async def select(ctx, *, args: str = None):
- """
- sends cutoffs for a particular year for a specified campus (if any).
- uses select func in analytics file.
- """
-
if not args:
- await ctx.send("**Usage:** `!!select [year]` or `!!select [year], [campus]`\n"
- "Example: `!!select 2024` or `!!select 2024, Pilani`")
- return
-
- # parse-args
- year = None
- campus = None
-
- if "," in args:
- parts = args.split(",", 1)
- year_str = parts[0].strip()
- campus = parts[1].strip() if len(parts) > 1 and parts[1].strip() else None
- else:
- year_str = args.strip()
-
- try:
- year = int(year_str)
- except ValueError:
- await ctx.send(f"**error:** invalid year '{year_str}'. Please provide a valid year such as 2024")
- return
-
- if campus:
- await ctx.send(f"**fetching {year} cutoffs for {campus.title()}**")
- else:
- await ctx.send(f"**fetching {year} cutoffs for all campuses**")
-
- try:
- filename = anal.select(limit=25, year=year, campus_filter=campus)
-
- await display(ctx, filename,
- not_found_msg=f"**No data found**\n"
- f"• Year: {year}\n"
- f"• Campus: {campus if campus else 'All'}\n\n"
- "**Available campuses:** Pilani, Goa, Hyderabad")
- except Exception as e:
- await ctx.send(f"**error:** {str(e)}")
+ return await ctx.send("usage: `!!select 2024` or `!!select 2024 Pilani`")
+
+ year_match = re.search(r"\b(20\d{2})\b", args)
+ if not year_match:
+ return await ctx.send(
+ "could not find a valid year (e.g., 2024) in your command."
+ )
+
+ year = int(year_match.group(1))
+ remaining_args = args.replace(year_match.group(1), "")
+
+ campus, _ = parse_campus(remaining_args)
+ campus_title = campus.title() if campus else None
+
+ filter_msg = f" - {campus_title}" if campus_title else " - All Campuses"
+ await ctx.send(
+ f"fetching **{year}** cutoffs{filter_msg.replace(' - ', ' for ')}..."
+ )
-@bot.command(name='predict')
+ loop = asyncio.get_event_loop()
+ generator = loop.run_in_executor(
+ None, partial(anal.select, limit=25, year=year, campus_filter=campus_title)
+ )
+
+ result = await display(
+ ctx,
+ cache_key=f"select_{year}_{campus_title or 'all'}",
+ title=f"{year} Cutoffs{filter_msg}",
+ generator_func=generator,
+ filename=f"cutoff_{year}.png",
+ )
+
+ if result is None:
+ await ctx.send(f"No data found for {year}.")
+
+
+@bot.command(name="predict")
async def predict(ctx, *, args: str = None):
- """
- sends predicted-data found in csv files in neat and tabular manner.
- uses get_predictions func in analytics file.
- """
-
- # parse-args
+ situation = "most-likely"
campus = None
- situation = 'most-likely'
-
+
if args:
- if "," in args:
- parts = args.split(",", 1)
- campus = parts[0].strip() if parts[0].strip() else None
- situation = parts[1].strip() if len(parts) > 1 and parts[1].strip() else 'most-likely'
- else:
- campus = args.strip()
-
- if campus:
- await ctx.send(f"**generating 2026 {situation} predictions for {campus.title()}**")
- else:
- await ctx.send(f"**generating 2026 {situation} predictions**")
-
- try:
- filename = anal.get_predictions(limit=25, campus_filter=campus, situation=situation)
-
- await display(ctx, filename,
- not_found_msg=f"no data found for campus: **{campus}**\n"
- "available campuses: Pilani, Goa, Hyderabad\n"
- "available situations: best, most-likely (can keep blank), worst")
- except Exception as e:
- await ctx.send(f"critical Error: {e}")
+ raw_args = args.lower().replace("most likely", "most-likely")
+
+ for s in ["worst", "best", "most-likely"]:
+ if s in raw_args:
+ situation = s
+ raw_args = raw_args.replace(s, "")
+ break
+
+ campus = raw_args.replace(",", "").strip() or None
+
+ filter_msg = f" - {campus.title()}" if campus else " - All Campuses"
+ await ctx.send(
+ f"fetching **{situation}** case predictions{filter_msg.replace(' - ', ' for ')}..."
+ )
+
+ loop = asyncio.get_event_loop()
+ generator = loop.run_in_executor(
+ None,
+ partial(
+ anal.get_predictions, limit=25, campus_filter=campus, situation=situation
+ ),
+ )
+
+ result = await display(
+ ctx,
+ cache_key=f"predict_{situation}_{campus or 'all'}",
+ title=f"{situation.title()} Case Predictions{filter_msg}",
+ generator_func=generator,
+ filename=f"pred_2026_{situation}.png",
+ )
+
+ if result is None:
+ await ctx.send("no prediction data found.")
@bot.command()
async def sy(ctx):
- """
- sends syllabus of current year's bitsat examination syllabus to user.
- """
+ await ctx.send(
+ "please check: https://admissions.bits-pilani.ac.in/FD/downloads/BITSAT_Syllabus.pdf?06012025"
+ )
- await ctx.send(f"please check: https://admissions.bits-pilani.ac.in/FD/downloads/BITSAT_Syllabus.pdf?06012025")
@bot.command()
async def ypt(ctx):
- """
- sends information regarding official YPT group of the server to user.
- """
+ await ctx.send(
+ "Group Name: BITSATards, Password: 123\nthe link is as follows: https://link.yeolpumta.com/P3R5cGU9Z3JvdXBJbnZpdGUmaWQ9MzU5OTUzNg=="
+ )
- await ctx.send(f"Group Name: BITSATards, Password: 123\nthe link is as follows: https://link.yeolpumta.com/P3R5cGU9Z3JvdXBJbnZpdGUmaWQ9MzU5OTUzNg==")
@bot.command()
async def da(ctx):
- """
- dates of bitsat-shifts & other such important tasks.
- """
+ await ctx.send(
+ "Session 1 Starts From: Wednesday, 15 April 2026\nSession 2 Starts From: Sunday, 24 May 2026"
+ )
- await ctx.send("Session 1 Starts From: Wednesday, 15 April 2026\nSession 2 Starts From: Sunday, 24 May 2026")
@bot.command()
async def resources(ctx):
- """
- Send study resources for BITSAT exam.
- """
+ await ctx.send(
+ "please follow: https://www.reddit.com/r/Bitsatards/wiki/resources/ \nadditionally please refer to: https://discord.com/channels/1221093390167576646/1224005178106187877"
+ )
+
+
+@bot.command()
+async def src(ctx):
+ await ctx.send(
+ "please consider starring this project if you liked it: https://github.com/PranavU-Coder/bitsatards_bot"
+ )
- await ctx.send(f"please follow: https://www.reddit.com/r/Bitsatards/wiki/resources/ \nadditionally please refer to: https://discord.com/channels/1221093390167576646/1224005178106187877")
@bot.command()
async def time(ctx, flag: str = None, *, date_str: str = None):
@@ -276,75 +323,86 @@ async def time(ctx, flag: str = None, *, date_str: str = None):
try:
if flag is None:
- message = exam_tracker.get_countdown(ctx.author.id)
- await ctx.send(message)
- return
+ return await ctx.send(exam_tracker.get_countdown(ctx.author.id))
- if flag in ['-s', '-set', '--set']:
+ if flag in ["-s", "-set", "--set"]:
if not date_str:
- await ctx.send("please provide a date\nusage: `!!exam -s DD-MM-YYYY`\nExample: `!!exam -s 15-04-2026`")
- return
+ return await ctx.send(
+ "please provide a date\nusage: `!!exam -s DD-MM-YYYY`\nExample: `!!time -s 15-04-2026`"
+ )
try:
- exam_date = datetime.strptime(date_str, '%d-%m-%Y').date()
+ exam_date = datetime.strptime(date_str, "%d-%m-%Y").date()
message = exam_tracker.set_exam_date(
user_id=ctx.author.id,
username=str(ctx.author),
channel_id=ctx.channel.id,
- exam_date=exam_date
+ exam_date=exam_date,
)
await ctx.send(message)
except ValueError:
- await ctx.send("invalid date format!\nplease use: `!!exam -s DD-MM-YYYY`\nExample: `!!exam -s 15-04-2026`")
+ await ctx.send(
+ "invalid date format!\nplease use: `!!exam -s DD-MM-YYYY`\nExample: `!!time -s 15-04-2026`"
+ )
+
+ elif flag in ["-r", "-reset", "--reset"]:
+ await ctx.send(exam_tracker.reset(ctx.author.id))
- elif flag in ['-r', '-reset', '--reset']:
- message = exam_tracker.reset(ctx.author.id)
- await ctx.send(message)
-
except Exception as e:
await ctx.send(f"error: {str(e)}")
+
+@bot.event
+async def on_command_error(ctx, error):
+ if isinstance(error, commands.MissingRequiredArgument):
+ await ctx.send("missing a required argument! check command usage.")
+ elif isinstance(error, commands.BadArgument):
+ await ctx.send(
+ "invalid argument provided, did you type text instead of a year?"
+ )
+ else:
+ print(f"error: {error}")
+ await ctx.send("an unexpected error occurred.")
+
+
@bot.command()
async def help(ctx):
- """
- Show help message with all available commands.
- """
-
helper = """
-**Available Commands**
-
-this bot helps you get a rough idea of BITSAT exam cutoffs for the upcoming year, please note that predictions are estimates and may not reflect actual values.
+This bot helps you get a rough idea of BITSAT exam cutoffs for the upcoming year, please note that predictions are estimates and may not reflect actual values.
**Commands:**
-• `!!plot [campus-name]` - plot marks trend for all branches in a campus
+- `!!plot [campus-name]` - plot marks trend for all branches in a campus
Example: `!!plot Pilani`
-• `!!plot-branch [campus-name], [branch-name]` - plot marks trend for a specific branch
- Example: `!!plot-branch Pilani, B.E. Computer Science`
- Note: comma separator is required and is not optional and now you can also use shortcuts like `cse`, `ece`, `mech`
+- `!!plot-branch [branch-name] [campus-name]` - plot marks trend for a specific branch
+ Example: `!!plot-branch Computer Science Pilani` or `!!plot-branch cse Pilani`
+ Note: you can use shortcuts like `cse`, `ece`, `mech`, etc. Comma-separators are no longer needed.
-• `!!select [year], [campus-name]` - shows values of cutoffs for a particular year in BITSAT exam for all or a specified campus in tabulated manner.
- Example: `!!select 2025`, `!!select 2025, pilani`
+- `!!select [year] [campus-name]` - shows cutoff values for a particular year in tabulated format for all or a specified campus
+ Example: `!!select 2025`, `!!select 2025 pilani`
-• `!!predict [campus-name], [situation]` - show predictions for 2026 BITSAT exam with option to see different scenarios
- Example: `!!predict`, `!!predict Pilani`, `!!predict Pilani, worst`
+- `!!predict [campus-name] [situation]` - shows predictions for 2026 BITSAT exam with different scenarios (worst/best/most-likely)
+ Example: `!!predict`, `!!predict Pilani`, `!!predict Pilani worst`
-• `!!da` - get exam-shift dates for both sessions
+- `!!da` - get exam-shift dates for both sessions
-• `!!time` - track your time till your bitsat-shift, use flags such as -s & -r for your needs
+- `!!time` - track your time till your bitsat-shift, use flags such as -s & -r for your needs
Example: `!!time -s 15-04-2026` or `!!time -r`
-• `!!ypt` - get the link, group-name & password for the official bitsatards-YPT group
+- `!!ypt` - get the link, group-name & password for the official bitsatards-YPT group
+
+- `!!sy` - get syllabus for current BITSAT examination
-• `!!sy` - get syllabus for current BITSAT examination
+- `!!resources` - get study resources for the BITSAT examination
-• `!!resources` - get study resources for the BITSAT examination
+- `!!src` - get the source code of this bot
-• `!!help` - shows this help message
+- `!!help` - shows this help message
**Available Campuses:** Pilani, Goa, Hyderabad
**Branch Shortcuts:** cse, ece, eee, mech, civil, chem, eni, manufacturing, pharm, and more
"""
await ctx.send(helper)
+
bot.run(token, log_handler=handler, log_level=logging.DEBUG)
diff --git a/predictions.py b/predictions.py
index 594dcdd..03d646e 100644
--- a/predictions.py
+++ b/predictions.py
@@ -1,33 +1,42 @@
import pandas as pd
import pickle
-def predict_cutoffs(year, difficulty, reference_df, output_file='predictions.csv'):
- with open("models/scaler.pkl", 'rb') as f:
+
+def predict_cutoffs(year, difficulty, reference_df, output_file="predictions.csv"):
+ with open("models/scaler.pkl", "rb") as f:
preprocessor = pickle.load(f)
- with open("models/model.pkl", 'rb') as f:
+ with open("models/model.pkl", "rb") as f:
model = pickle.load(f)
# since I have dropped newer branches, I could remove this line of code and simply use itertools but this does the job so I'm letting it be.
- prediction_data = reference_df[['campus', 'branch']].drop_duplicates().copy()
- prediction_data['year'] = year
- prediction_data['difficulty'] = difficulty
-
+ prediction_data = reference_df[["campus", "branch"]].drop_duplicates().copy()
+ prediction_data["year"] = year
+ prediction_data["difficulty"] = difficulty
+
prediction_data_scaled = preprocessor.transform(prediction_data)
- prediction_data['marks'] = model.predict(prediction_data_scaled).round().astype(int)
-
- result = prediction_data[['campus', 'branch', 'marks', 'year']].sort_values('marks', ascending=False)
-
- result.to_csv(output_file, index=False)
+ prediction_data["marks"] = model.predict(prediction_data_scaled).round().astype(int)
+
+ result = prediction_data[["campus", "branch", "marks", "year"]].sort_values(
+ "marks", ascending=False
+ )
+
+ result.to_csv(output_file, index=False)
return result
-# I'm taking 0.2 -> best case, 0.5 -> most-likely and 0.8 -> worst case
+
+# I'm taking 0.2 -> best case, 0.5 -> most-likely and 0.8 -> worst case
# to manipulate
coefficient_of_difficulty = 0.5
-df = pd.read_csv('data/cutoff_2025.csv')
-predictions_2026 = predict_cutoffs(year=2026, difficulty = coefficient_of_difficulty , reference_df=df, output_file='predict/most_likely_case.csv')
+df = pd.read_csv("data/model_data/cutoff_2025.csv")
+predictions_2026 = predict_cutoffs(
+ year=2026,
+ difficulty=coefficient_of_difficulty,
+ reference_df=df,
+ output_file="predict/most_likely_case.csv",
+)
print(predictions_2026)
-predictions_2026.to_csv('predict/most_likely_case.csv', index=False)
+predictions_2026.to_csv("predict/most_likely_case.csv", index=False)
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..65ec521
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,23 @@
+[project]
+name = "bitsatards-bot"
+version = "2.5.0"
+description = "Official project-typesettings for the bitsatards-bot project."
+readme = "README.md"
+requires-python = ">=3.14"
+dependencies = [
+ "beautifulsoup4>=4.14.3",
+ "discord-py>=2.6.4",
+ "lxml>=6.0.2",
+ "matplotlib>=3.10.8",
+ "numpy>=2.4.2",
+ "pandas>=3.0.0",
+ "psycopg2-binary>=2.9.11",
+ "pytest>=9.0.2",
+ "pytest-mock>=3.15.1",
+ "python-dotenv>=1.2.1",
+ "requests>=2.32.5",
+ "ruff>=0.14.14",
+ "scikit-learn>=1.8.0",
+ "seaborn>=0.13.2",
+ "sqlalchemy>=2.0.46",
+]
diff --git a/requirements.txt b/requirements.txt
deleted file mode 100644
index 5b7b10a..0000000
--- a/requirements.txt
+++ /dev/null
@@ -1,14 +0,0 @@
-discord.py>=2.6.4
-python-dotenv>=1.0.1
-pandas>=2.2.3
-numpy>=2.2.2
-matplotlib>=3.9.3
-seaborn>=0.13.2
-scikit-learn>=1.6.0
-requests>=2.32.3
-lxml>=5.3.0
-beautifulsoup4>=4.14.3
-sqlalchemy>=2.0.45
-psycopg2-binary>=2.9.9
-pytest>=8.0.0
-pytest-mock>=3.14.0
diff --git a/results/Pilani_CSE_Trend.png b/results/Pilani_CSE_Trend.png
index ea0ea0e..dcfbab3 100644
Binary files a/results/Pilani_CSE_Trend.png and b/results/Pilani_CSE_Trend.png differ
diff --git a/results/Pilani_Most_Likely.png b/results/Pilani_Most_Likely.png
index 55c627c..1e13c4f 100644
Binary files a/results/Pilani_Most_Likely.png and b/results/Pilani_Most_Likely.png differ
diff --git a/results/Pilani_Trend.png b/results/Pilani_Trend.png
index d9b5c64..74ce72d 100644
Binary files a/results/Pilani_Trend.png and b/results/Pilani_Trend.png differ
diff --git a/services/exam_tracker.py b/services/exam_tracker.py
index 0eed3b8..16349b9 100644
--- a/services/exam_tracker.py
+++ b/services/exam_tracker.py
@@ -1,7 +1,7 @@
-from datetime import date, datetime
-from typing import Optional
+from datetime import date
from database.repository import ExamRepository
+
class ExamTracker:
"""
this is what we will use in main.py
@@ -10,7 +10,9 @@ class ExamTracker:
def __init__(self):
self.repo = ExamRepository()
- def set_exam_date(self, user_id: int, username: str, channel_id: int, exam_date: date) -> str:
+ def set_exam_date(
+ self, user_id: int, username: str, channel_id: int, exam_date: date
+ ) -> str:
"""
set user's exam date and return confirmation message
"""
@@ -39,7 +41,7 @@ def get_countdown(self, user_id: int) -> str:
exam_date = user_exam.exam_date
days_left = (exam_date - today).days
- exam_date_str = exam_date.strftime('%d %B %Y')
+ exam_date_str = exam_date.strftime("%d %B %Y")
if days_left > 0:
weeks = days_left // 7
diff --git a/services/reminder.py b/services/reminder.py
index 7024798..5dbc9d4 100644
--- a/services/reminder.py
+++ b/services/reminder.py
@@ -1,20 +1,21 @@
from datetime import date
from database.repository import ExamRepository
+
class Reminder:
"""
sends user reminders when their exam will take place if they have set a date.
"""
-
+
def __init__(self):
self.repo = ExamRepository()
- self.sent_today = set()
-
+ self.sent_today = set()
+
def users_to_remind(self):
"""
get users who need reminders today
"""
-
+
reminders = []
all_users = self.repo.get_all_users()
today = date.today()
@@ -22,19 +23,19 @@ def users_to_remind(self):
for user_exam in all_users:
days_until = (user_exam.exam_date - today).days
user_id = user_exam.user_id
-
+
# if already sent, don't send again :p
if user_id in self.sent_today:
continue
-
+
message = None
-
+
if days_until == 30:
message = f"**30 Days Until BITSAT**\nExam: {user_exam.exam_date.strftime('%d %B %Y')}\nOne month to go"
-
+
elif days_until == 7:
message = f"**1 Week Until BITSAT!**\nExam: {user_exam.exam_date.strftime('%d %B %Y')}\nFinal week"
-
+
elif 1 <= days_until <= 6:
if days_until == 1:
message = f"**Tomorrow is BITSAT!**\n{user_exam.exam_date.strftime('%d %B %Y')}\nGet good sleep for tomorrow"
@@ -43,15 +44,17 @@ def users_to_remind(self):
elif days_until == 0:
message = f"**TODAY IS THE DAY!**\nBITSAT: {user_exam.exam_date.strftime('%d %B %Y')}\nGood luck Soldier"
if message:
- reminders.append({
- 'user_id': user_id,
- 'message': message,
- 'channel_id': user_exam.channel_id,
- })
+ reminders.append(
+ {
+ "user_id": user_id,
+ "message": message,
+ "channel_id": user_exam.channel_id,
+ }
+ )
self.sent_today.add(user_id)
-
+
return reminders
-
+
def reset_daily_tracking(self):
self.sent_today.clear()
diff --git a/tests/conftest.py b/tests/conftest.py
index cab80e0..eb642c6 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -1,28 +1,33 @@
import os
# dummy DATABASE_URL for testing-purposes only.
-os.environ.setdefault('DATABASE_URL', 'postgresql://dummy:dummy@localhost/dummy')
+os.environ.setdefault("DATABASE_URL", "postgresql://dummy:dummy@localhost/dummy")
import pytest
import database.models as models
from unittest.mock import Mock, patch
from services.exam_tracker import ExamTracker
+
@pytest.fixture
def exam_columns():
return models.UserExam.__table__.columns
+
@pytest.fixture
def mock_repo():
return Mock()
+
@pytest.fixture
def tracker(mock_repo):
- with patch('services.exam_tracker.ExamRepository', return_value=mock_repo):
+ with patch("services.exam_tracker.ExamRepository", return_value=mock_repo):
return ExamTracker()
+
@pytest.fixture
def reminder(mock_repo):
- with patch('services.reminder.ExamRepository', return_value=mock_repo):
+ with patch("services.reminder.ExamRepository", return_value=mock_repo):
from services.reminder import Reminder
+
return Reminder()
diff --git a/tests/database_test.py b/tests/database_test.py
index 477bfed..face160 100644
--- a/tests/database_test.py
+++ b/tests/database_test.py
@@ -1,18 +1,17 @@
-import pytest
from sqlalchemy import Integer, BigInteger, String, Date
+
# basic tests to assert datatype & existence.
class TestDatabase:
-
def test_column_types(self, exam_columns):
- assert isinstance(exam_columns['id'].type, Integer)
- assert isinstance(exam_columns['user_id'].type, BigInteger)
- assert isinstance(exam_columns['username'].type, String)
- assert isinstance(exam_columns['exam_date'].type, Date)
- assert isinstance(exam_columns['channel_id'].type, BigInteger)
+ assert isinstance(exam_columns["id"].type, Integer)
+ assert isinstance(exam_columns["user_id"].type, BigInteger)
+ assert isinstance(exam_columns["username"].type, String)
+ assert isinstance(exam_columns["exam_date"].type, Date)
+ assert isinstance(exam_columns["channel_id"].type, BigInteger)
def test_all_columns_exist(self, exam_columns):
- expected_columns = {'id', 'user_id', 'username', 'exam_date', 'channel_id'}
+ expected_columns = {"id", "user_id", "username", "exam_date", "channel_id"}
actual_columns = set(exam_columns.keys())
-
+
assert actual_columns == expected_columns
diff --git a/tests/services_test.py b/tests/services_test.py
index f0ddfd4..73c63c1 100644
--- a/tests/services_test.py
+++ b/tests/services_test.py
@@ -1,11 +1,10 @@
-import pytest
from datetime import date
from unittest.mock import Mock, patch
+
# a lot of unit-tests from here and on.
class TestExamTracker:
-
- @patch('services.exam_tracker.date')
+ @patch("services.exam_tracker.date")
def test_set_exam_date_future(self, mock_date_class, tracker, mock_repo):
mock_date_class.today.return_value = date(2026, 1, 1)
exam_date = date(2026, 4, 15)
@@ -14,21 +13,21 @@ def test_set_exam_date_future(self, mock_date_class, tracker, mock_repo):
assert "exam date set for **15 April 2026**" in result
assert "**104 days** remaining" in result
- @patch('services.exam_tracker.date')
+ @patch("services.exam_tracker.date")
def test_set_exam_date_today(self, mock_date_class, tracker, mock_repo):
today = date(2026, 4, 15)
mock_date_class.today.return_value = today
result = tracker.set_exam_date(123, "testuser", 456, today)
assert "which is btw today, best of luck soldier!" in result
- @patch('services.exam_tracker.date')
+ @patch("services.exam_tracker.date")
def test_past(self, mock_date_class, tracker, mock_repo):
mock_date_class.today.return_value = date(2026, 4, 15)
past_date = date(2026, 1, 1)
result = tracker.set_exam_date(123, "testuser", 456, past_date)
assert "has already been passed" in result
- @patch('services.exam_tracker.date')
+ @patch("services.exam_tracker.date")
def test_future_date(self, mock_date_class, tracker, mock_repo):
mock_date_class.today.return_value = date(2026, 1, 1)
mock_exam = Mock()
@@ -39,7 +38,7 @@ def test_future_date(self, mock_date_class, tracker, mock_repo):
assert "**104 days**" in result
assert "14 weeks and 6 days" in result
- @patch('services.exam_tracker.date')
+ @patch("services.exam_tracker.date")
def test_today(self, mock_date_class, tracker, mock_repo):
today = date(2026, 4, 15)
mock_date_class.today.return_value = today
@@ -49,7 +48,7 @@ def test_today(self, mock_date_class, tracker, mock_repo):
result = tracker.get_countdown(123)
assert "**TODAY**" in result
- @patch('services.exam_tracker.date')
+ @patch("services.exam_tracker.date")
def test_past_date(self, mock_date_class, tracker, mock_repo):
mock_date_class.today.return_value = date(2026, 4, 15)
mock_exam = Mock()
@@ -60,14 +59,14 @@ def test_past_date(self, mock_date_class, tracker, mock_repo):
assert "It's been 104 days since your exam" in result
assert "time -r command" in result
- @patch('services.exam_tracker.date')
+ @patch("services.exam_tracker.date")
def test_less_than_week(self, mock_date_class, tracker, mock_repo):
mock_date_class.today.return_value = date(2026, 4, 10)
mock_exam = Mock()
mock_exam.exam_date = date(2026, 4, 15)
mock_repo.get_user_exam.return_value = mock_exam
result = tracker.get_countdown(123)
-
+
assert "**5 days**" in result
assert "exam date: 15 April 2026" in result
@@ -82,118 +81,118 @@ def test_reset_no_record(self, tracker, mock_repo):
result = tracker.reset(123)
assert result == "no exam has been set."
-class TestReminder:
- @patch('services.reminder.date')
+class TestReminder:
+ @patch("services.reminder.date")
def test_reminder_30_days(self, mock_date_class, reminder, mock_repo):
mock_date_class.today.return_value = date(2026, 3, 16)
-
+
mock_user = Mock()
mock_user.user_id = 123
mock_user.exam_date = date(2026, 4, 15)
mock_user.channel_id = 456
-
+
mock_repo.get_all_users.return_value = [mock_user]
reminders = reminder.users_to_remind()
assert len(reminders) == 1
- assert reminders[0]['user_id'] == 123
- assert "**30 Days Until BITSAT**" in reminders[0]['message']
- assert "15 April 2026" in reminders[0]['message']
- assert reminders[0]['channel_id'] == 456
+ assert reminders[0]["user_id"] == 123
+ assert "**30 Days Until BITSAT**" in reminders[0]["message"]
+ assert "15 April 2026" in reminders[0]["message"]
+ assert reminders[0]["channel_id"] == 456
- @patch('services.reminder.date')
+ @patch("services.reminder.date")
def test_reminder_7_days(self, mock_date_class, reminder, mock_repo):
mock_date_class.today.return_value = date(2026, 4, 8)
-
+
mock_user = Mock()
mock_user.user_id = 123
mock_user.exam_date = date(2026, 4, 15)
mock_user.channel_id = 456
-
+
mock_repo.get_all_users.return_value = [mock_user]
reminders = reminder.users_to_remind()
-
+
assert len(reminders) == 1
- assert "**1 Week Until BITSAT!**" in reminders[0]['message']
- assert "Final week" in reminders[0]['message']
+ assert "**1 Week Until BITSAT!**" in reminders[0]["message"]
+ assert "Final week" in reminders[0]["message"]
- @patch('services.reminder.date')
+ @patch("services.reminder.date")
def test_reminder_tomorrow(self, mock_date_class, reminder, mock_repo):
mock_date_class.today.return_value = date(2026, 4, 14)
mock_user = Mock()
mock_user.user_id = 123
mock_user.exam_date = date(2026, 4, 15)
mock_user.channel_id = 456
-
+
mock_repo.get_all_users.return_value = [mock_user]
reminders = reminder.users_to_remind()
-
+
assert len(reminders) == 1
- assert "**Tomorrow is BITSAT!**" in reminders[0]['message']
- assert "Get good sleep for tomorrow" in reminders[0]['message']
+ assert "**Tomorrow is BITSAT!**" in reminders[0]["message"]
+ assert "Get good sleep for tomorrow" in reminders[0]["message"]
- @patch('services.reminder.date')
+ @patch("services.reminder.date")
def test_reminder_2_to_6_days(self, mock_date_class, reminder, mock_repo):
mock_date_class.today.return_value = date(2026, 4, 10)
-
+
mock_user = Mock()
mock_user.user_id = 123
mock_user.exam_date = date(2026, 4, 15)
mock_user.channel_id = 456
-
+
mock_repo.get_all_users.return_value = [mock_user]
reminders = reminder.users_to_remind()
-
+
assert len(reminders) == 1
- assert "**5 Days Until BITSAT**" in reminders[0]['message']
- assert "Please close discord & study" in reminders[0]['message']
+ assert "**5 Days Until BITSAT**" in reminders[0]["message"]
+ assert "Please close discord & study" in reminders[0]["message"]
- @patch('services.reminder.date')
+ @patch("services.reminder.date")
def test_reminder_today(self, mock_date_class, reminder, mock_repo):
today = date(2026, 4, 15)
mock_date_class.today.return_value = today
-
+
mock_user = Mock()
mock_user.user_id = 123
mock_user.exam_date = today
mock_user.channel_id = 456
-
+
mock_repo.get_all_users.return_value = [mock_user]
-
+
reminders = reminder.users_to_remind()
-
+
assert len(reminders) == 1
- assert "**TODAY IS THE DAY!**" in reminders[0]['message']
- assert "Good luck Soldier" in reminders[0]['message']
+ assert "**TODAY IS THE DAY!**" in reminders[0]["message"]
+ assert "Good luck Soldier" in reminders[0]["message"]
- @patch('services.reminder.date')
+ @patch("services.reminder.date")
def test_multiple_users_different_dates(self, mock_date_class, reminder, mock_repo):
mock_date_class.today.return_value = date(2026, 4, 14)
-
+
user1 = Mock()
user1.user_id = 123
- user1.exam_date = date(2026, 4, 15)
+ user1.exam_date = date(2026, 4, 15)
user1.channel_id = 456
-
+
user2 = Mock()
user2.user_id = 456
- user2.exam_date = date(2026, 4, 21)
+ user2.exam_date = date(2026, 4, 21)
user2.channel_id = 789
-
+
user3 = Mock()
user3.user_id = 789
- user3.exam_date = date(2026, 5, 1)
+ user3.exam_date = date(2026, 5, 1)
user3.channel_id = 111
-
+
mock_repo.get_all_users.return_value = [user1, user2, user3]
reminders = reminder.users_to_remind()
-
+
assert len(reminders) == 2
- assert reminders[0]['user_id'] == 123
- assert reminders[1]['user_id'] == 456
+ assert reminders[0]["user_id"] == 123
+ assert reminders[1]["user_id"] == 456
- @patch('services.reminder.date')
+ @patch("services.reminder.date")
def test_empty_user_list(self, mock_date_class, reminder, mock_repo):
mock_date_class.today.return_value = date(2026, 4, 15)
mock_repo.get_all_users.return_value = []
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index 0000000..72138e5
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