# JobMatch AI (`peaceful_mix/jobmatch-ai`) Actor

Intelligent system matching resumes to jobs using AI.
📄 Resume Analysis: Extracts data from PDFs.
🤖 AI Matching: Uses Gemini AI for accurate matching.
🎯 Smart Scoring: Provides a suitability score (0-100).
💡 Insights: Gives match reasons and prep tips.
⚡ Fast: Quick analysis of many postings.

- **URL**: https://apify.com/peaceful\_mix/jobmatch-ai.md
- **Developed by:** [Vidip Ghosh](https://apify.com/peaceful_mix) (community)
- **Categories:** AI, Jobs
- **Stats:** 14 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.01 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## JobMatch AI 🔍✨

An intelligent job matching system that analyzes resumes and matches them with the most suitable job postings using AI.

### Features

- 📄 **Resume Analysis**: Extract and analyze text from PDF resumes
- 🤖 **AI-Powered Matching**: Uses Google's Gemini AI to match resumes with job descriptions
- 🎯 **Smart Scoring**: Provides a suitability score (0-100) for each job match
- 💡 **Actionable Insights**: Get detailed reasons for matches and preparation tips
- ⚡ **Fast Processing**: Optimized for quick analysis of multiple job postings

### Prerequisites

- Python 3.8+
- Google AI API key
- Apify API key (for job scraping, if needed)

### Installation

1. Clone the repository:

   ```bash
   git clone https://github.com/yourusername/jobmatch-ai.git
   cd jobmatch-ai
   ```

2. Create and activate a virtual environment:

   ```bash
   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   ```

3. Install the required packages:

   ```bash
   pip install -r requirements.txt
   ```

4. Create a [.env](.env) file and add your API keys:
   ```
   GOOGLE_AI_API=your_google_ai_api_key
   APIFY_API_KEY=your_apify_api_key
   ```

### Usage

1. Start the Flask server:

   ```bash
   python app.py
   ```

2. Send a POST request to `/extract` with a PDF resume:
   ```bash
   curl -X POST -F "file=@/path/to/your/resume.pdf" http://localhost:3000/extract
   ```

### API Endpoints

#### POST /extract

Upload a PDF resume and get job matches.

**Request:**

- Method: POST
- Content-Type: multipart/form-data
- Body: `file` (PDF file)

**Response:**

```json
[
   {
    "job_title": "Frontend Software Engineer (React, TypeScript or JavaScript)",
    "score": 60,
    "match_reason": "The candidate has strong proficiency in React, JavaScript, and TypeScript, essential for this front-end role. Critically, their robust Python programming skills and hands-on experience in machine learning and AI (TensorFlow, PyTorch, Generative AI, Nillion AI Prize) make them an excellent match for supporting AI labs and developing coding benchmarks, which is the core focus. While the stated 3-10 years of experience is a hurdle, the depth of technical skills and relevance of AI projects are highly compelling, especially for a contract/part-time role that may value specific technical expertise.",
    "prepare": "Emphasize how their AI/ML knowledge allows them to understand and contribute to coding benchmarks for AI systems. Highlight any personal experience with code quality, testing (e.g., unit tests in projects), and debugging. Be ready to discuss the technical aspects of their AI projects and how they would approach curating issues and solutions for AI-related coding tasks."
  },
]
```

### Project Structure

```
.
├── app.py              # Main Flask application
├── data.json           # Sample job postings
├── requirements.txt    # Python dependencies
├── .env                # Environment variables
└── README.md           # This file
```

### Contributing

1. Fork the repository
2. Create a new branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add some amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request

### License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Actor input object example

```json
{}
```

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("peaceful_mix/jobmatch-ai").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("peaceful_mix/jobmatch-ai").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{}' |
apify call peaceful_mix/jobmatch-ai --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,peaceful_mix/jobmatch-ai"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/9KCIIJxCdtbVYD8gd/builds/ny8BbBa9kk3Ig1NOP/openapi.json