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title: README
emoji: 🏃
colorFrom: green
colorTo: red
sdk: static
pinned: false
license: mit
short_description: JobSelect CLI and JobAnalyze 6k model
---
# JobSelect Labs
[](https://www.python.org/)
[](https://pytorch.org/)
[](https://scikit-learn.org/)
[](https://numpy.org/)
[](https://pandas.pydata.org/)
[](https://jobselect.vercel.app/)
[](https://huggingface.co/JobSelect/JobAnalyze_6k)
[](https://pypi.org/project/JobSelect/)
[](https://www.linkedin.com/company/jobselect-labs/)
Company Details :
1) CEO and Founder - Akshay Babu
2) Company Type - Artificial Intelligence
Products :
1) JobSelect CLI
2) JobAnalyze 6k Model (Local, API and MCP Compatible)
Installation:
- `pip install jobselect`
It uses:
- **TF-IDF** features over the combined text (job description + role + type)
- A **PyTorch feed-forward neural network** trained as a **multi-label** classifier
- **Per-skill thresholding** for evaluation and **top-k ranked probabilities** for inference
---
## What it does
1. **Data preparation** (`model/prep/data_prep.py`)
- Reads cleaned job description data.
- Normalizes/repairs common skill typos (e.g., `tesnorflow/pytorch` → `tensorflow/pytorch`).
- Builds a **multi-hot** target vector of skills.
- Fits a **TF-IDF** vectorizer (with n-grams) and splits into train/test.
- Saves:
- `model/prep/prepared_data.npz` (TF-IDF arrays + labels + indexes)
- `model/prep/vectorizer.pkl` (fitted TF-IDF vectorizer)
- `model/prep/label_vocab.json` (skill label vocabulary)
2. **Model training** (`model/model.py`)
- Loads prepared TF-IDF arrays.
- Defines a simple **MLP**:
- Linear → ReLU → Dropout → Linear (one logit per skill)
- Trains with `BCEWithLogitsLoss` (multi-label setting).
- Saves:
- `model_out/skill_classifier.pt` (model weights)
- `model_out/training_history.json` (train/test loss curves)
3. **Evaluation** (`model/eval.py`)
- Loads the trained model.
- Applies a fixed sigmoid + threshold (**0.3**) to obtain binary skill predictions.
- Reports:
- Per-skill precision/recall/F1
- Micro-F1 and Macro-F1
- Compares against a simple baseline (frequency-driven / always-predict-most-frequent labels).
4. **Prediction / Inference** (`model/pred.py`)
- Loads the TF-IDF vectorizer and trained model.
- Creates TF-IDF features for the input text.
- Outputs the **top-k** skills by probability.
---
## Use cases
- **Resume/job-post matching** (first-pass filtering of relevant skills)
- **Job taxonomy building** (discover recurring skills from postings)
- **Recruiting analytics** (aggregate predicted skill demand by seniority/role/type)
- **Prototyping multi-label NLP classifiers** (TF-IDF + MLP baseline)
---
## Project structure
```text
.
├─ data/
│ ├─ raw/
│ │ └─ Job_descriptions.csv # Raw input dataset
│ ├─ sample_data/
│ │ └─ test.txt # Example JD, Role and Type for testing
│ └─ clean/
│ ├─ cleaned_job_descriptions.csv # Cleaned master CSV
│ ├─ cleaned_job_descriptions_internships.csv
│ ├─ cleaned_job_descriptions_junior.csv
│ └─ cleaned_job_descriptions_senior.csv
│
├─ model/
│ ├─ prep/
│ │ ├─ data_prep.py # TF-IDF + multi-hot label creation + train/test split
│ │ ├─ sym_map.py # Synonym/phrase normalization map used during prep
│ │ ├─ vectorizer.pkl # Saved TF-IDF vectorizer (generated by data_prep)
│ │ ├─ prepared_data.npz # Saved arrays (generated by data_prep)
│ │ └─ label_vocab.json # Skill label vocabulary (generated by data_prep)
│ │
│ ├─ model.py # PyTorch multi-label classifier training
│ ├─ eval.py # Thresholded evaluation + F1 metrics + baseline comparison
│ └─ pred.py # Predict top-k skills for new text
│
├─ model_out/
│ ├─ skill_classifier.pt # Trained model weights (generated by model.py)
│ └─ training_history.json # Training loss history (generated by model.py)
│
├─ cli/
│ ├─ jobselect.py # Rich terminal CLI (prompts + prints top skills)
│ ├─ model_select.py # Inference routing: API-first, LOCAL fallback (key resolved lazily)
│ └─ api_val.py # API key prompt / mode selection for CLI
│
├─ test/
│ └─ test_model.py # Pytest checks expected artifacts exist in model_out/ and model/prep/
│
├─ notebooks/
│ ├─ 01_EDA.ipynb # Exploratory Data Analysis
│ └─ 02_Data_Engineering.ipynb # Data engineering / cleaning notes
│
├─ api/
│ ├─ JobAnalyze_API.py # FastAPI service + Pydantic validation + API-key verification
│ ├─ pred.py # API/server-side prediction wrapper (imports model.pred)
│ └─ supabase_client.py # Optional API key persistence (Supabase)
│
├─ pipeline.py # Executes notebooks + training/eval steps in order
├─ pyproject.toml # Installs as a cli tool (jobselect)
├─ requirements.txt
└─ README.md
```
---
## Requirements
See `requirements.txt` for the exact dependencies.
---
## Getting started
### 1) Clone and Install dependencies
```bash
git clone https://github.com/Ak47xdd/Job-Description-Analysis.git
pip install -r requirements.txt
```
### 2) Run data preparation (optional)
This builds the TF-IDF features and label vocabulary from the cleaned CSV.
```bash
python model/prep/data_prep.py
```
Expected outputs:
- `model/prep/prepared_data.npz`
- `model/prep/vectorizer.pkl`
- `model/prep/label_vocab.json`
### 3) Train the model (optional)
```bash
python model/model.py
```
Expected outputs:
- `model_out/skill_classifier.pt`
- `model_out/training_history.json`
### 4) Evaluate performance (optional)
```bash
python model/eval.py
```
Outputs include:
- Per-skill metrics (precision/recall/F1)
- Micro-F1 and Macro-F1
- Baseline comparison
### 5) Predict skills for a new job description
#### Option A: Python function (LOCAL model) — advanced / offline use only
You can still run predictions locally if you have the prepared artifacts (`model/prep/vectorizer.pkl` and `model_out/skill_classifier.pt`). Local inference is useful for offline experimentation or development, but for most users we recommend using the hosted API (see Option C).
`from api.pred import JobAnalyze_6k`
`data/sample_data/test.txt` contains an example job description inside. Use:
```python
JobAnalyze_6k(job_desc, role="AI Engineer", job_type="Junior", top_k=50)
```
#### Option B: Use the interactive CLI (API-first)
The CLI (`cli/jobselect.py`) is API-first. Provide a valid API key to run the CLI against the hosted service so you always get the up-to-date model and label set. If no API key is provided the CLI can fall back to local inference, but this is not recommended for general usage.
```bash
python -m cli.jobselect
# or after install
pip install jobselect
jobselect
```
The CLI:
- prompts for **Job Description**, **Role**, and **Type**
- validates them via the API schema when running in API mode
- prints the top skills ranked by probability as returned by the hosted API
---
#### Option C: Get predictions through the hosted API (recommended)
The API is hosted and recommended for production and general use — it provides the latest model, label vocabulary, and automatic updates.
To call the hosted API, send a POST request to the hosted endpoint (replace <HOSTED_API_URL> with the actual API base URL) with the header `JOBSELECT_KEY` set to your API key, and a JSON body containing `Job_Desc`, `Role`, and `Type`.
Base URL : https://job-description-analysis.onrender.com
JOBSELECT_KEY : Vist the website (jobselect.vercel.app)
Example (curl):
```bash
curl -X POST "https://<HOSTED_API_URL>/predict" \
-H "Content-Type: application/json" \
-H "JobAnalyze_6k_Key: YOUR_API_KEY_HERE" \
-d '{"Job_Desc":"Senior ML engineer with 3+ years experience...","Role":"AI Engineer","Type":"Senior"}'
```
The response will include ranked skills and probabilities (top-k). Contact the repository maintainer or your account admin to obtain an API key and the exact hosted API base URL.
---
#### Option D: Run the FastAPI service locally (optional)
If you prefer to host your own instance of the API, you can run the FastAPI server in `api/JobAnalyze_API.py`. Local hosting is useful for private deployments or testing with custom models.
Run the service and send requests with the same header and JSON body as described in Option C (`JobAnalyze_6k_Key`, `Job_Desc`, `Role`, `Type`).
---
## How predictions work
- Text is concatenated as:
`"{job_desc} {role} {job_type}"`
- TF-IDF transforms text into a fixed-size vector
- The network outputs one logit per skill
- Sigmoid converts logits → probabilities
- Skills are ranked by probability and the top-k are returned
---
## Important implementation notes
- **Multi-label learning:** Each skill is treated independently (binary relevance via sigmoid + `BCEWithLogitsLoss`).
- **Evaluation threshold:** `model/eval.py` uses a fixed threshold of **0.3**. For production use, you may want per-label thresholds tuned on a validation set.
- **Dataset size:** The included notebooks and evaluation code suggest the dataset may be small; results can be limited by label frequency and data coverage.
---
## New features / capabilities
- **Rich terminal CLI** (`cli/jobselect.py`) using `rich` + `pyfiglet` for interactive top-skill display.
- **API validation + schema enforcement**
- Input validation via **Pydantic** model constraints in `api/JobAnalyze_API.py`.
- API key auth via header + secure verification, with optional Supabase-backed storage in `api/supabase_client.py`.
- CLI mode auto-detection (`cli/api_val.py` + `cli/model_select.py`): uses API when a key is available, otherwise falls back to local inference.
- **Synonym/phrase normalization hook** (`model/prep/sym_map.py`) applied during data preparation.
- **Pipeline runner** (`pipeline.py`) to execute notebooks and training steps in sequence.
---
## Customization ideas
- Improve text cleaning and skill normalization in `data_prep.py`
- Tune TF-IDF parameters (`max_features`, `ngram_range`, `min_df`)
- Replace the simple MLP with a stronger baseline (e.g., logistic regression on TF-IDF)
- Calibrate thresholds per label using validation data
- Add a CLI or web service endpoint for prediction
---
---
## Author
Developed with ❤️ by **Akshay Babu**
[](https://www.linkedin.com/in/akshay-babu-827b85370/)
[](https://github.com/Ak47xdd)
For questions, feedback, or collaboration opportunities, feel free to reach out!
---
## References / Inspiration
This repository follows a common pattern for multi-label NLP baselines:
TF-IDF features + a simple neural network + sigmoid-based multi-label outputs.
|