README.md

metadata

title: Papaya
emoji: 😻
colorFrom: indigo
colorTo: gray
sdk: docker
app_port: 7860
pinned: false
license: mit
short_description: Automated server payments via PayPal

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

Automated Payment Layer for Servers and AI Nodes

LET MACHINES DO THE WORK AND PAY THEMSELVES

NEXT-GENERATION AUTONOMOUS COMPUTE TRANSACTION INFRASTRUCTURE.

Papaya is a tool that lets backend servers and AI nodes track their own workload and complete automatic payments through PayPal without any human clicking a checkout page.

---

Why Papaya

Right now, AI models, backend servers, and IoT setups can talk to each other, but they cannot pay for resources on their own. When a script needs more computing power or wants to use a data tool, it has to wait for a human to put in credit card details or settle an invoice. Papaya gives machines their own payment setups by connecting live server workload tracking directly to the PayPal Sandbox API:

• Machine Wallets — Lets backend servers and code tasks handle money directly without a human filling out checkout forms.

• Load-Based Billing — Tracks how hard the server is working (like CPU usage and data tokens) to calculate fair prices instantly.

• No-UI Checkout — Runs entirely in the background via direct backend code requests, completely skipping pop-ups and web browser redirections.

• Exact Micro-Payments — Charges only for the small amount of server work done, making things highly efficient.

---

Product Suite

Product	What it does
Telemetry Sensor Engine	Tracks server workload details like CPU use and data processing speed in real time.
Analytical Modeler	Reads the workload numbers and uses simple formulas to decide the right cost.
No-UI Financial Core	Connects to backend API routes to talk directly with PayPal servers.
Automated Order Capture	Instantly claims the payment as soon as the server work is finished.
Dynamic Billing Dashboard	A plain Python web app that shows real-time usage graphs and payment logs.
Sovereign Node Logs	Keeps a straight text list of current script tasks, security tokens, and success alerts.

---

Architecture

flowchart LR
    subgraph CLIENT_LAYER ["Telemetry and Dashboard Layer"]
        A["Resource Sensor Node<br/>CPU / Token Streams"] --> B["Telemetry Tracker<br/>Psutil + NumPy"]
        K["Streamlit Dashboard<br/>Plotly Graphs"]
    end

    subgraph CORE_ENGINE ["FastAPI Core Engine"]
        D["Data Validator Layer<br/>Pydantic"] --> E["Analytical Modeler<br/>Pricing Code"]
    end

    subgraph PAYMENT_GATEWAY ["PayPal Sandbox Integration"]
        F["OAuth 2.0 Security Client<br/>Fetch PayPal Token"] --> G["PayPal Sandbox Gateway<br/>v2 Checkout Orders"]
        G --> H["Automated Order Capture<br/>Payment Complete"]
        H --> I["Sovereign Node Logs"]
    end

    B -->|"JSON via REST"| D
    E -->|"Final Cost Value"| F
    I -->|"Payment logs"| K

    style CLIENT_LAYER fill:#f3f4f6,stroke:#6b7280,stroke-width:2px,color:#111827
    style CORE_ENGINE fill:#e0f2f1,stroke:#009688,stroke-width:2px,color:#111827
    style PAYMENT_GATEWAY fill:#fff1e6,stroke:#f97316,stroke-width:2px,color:#111827

Infrastructure Core Stack

Priority	Component	Technology	Best For
0 (Core)	Backend Gateway	Python FastAPI + Uvicorn	Running fast, background web paths
1	Financial Operations	PayPal REST API (Sandbox Mode)	Handling automatic money transactions
2	Telemetry Tracker	Psutil + NumPy	Reading laptop or server CPU loads
3	Visual Dashboard	Streamlit + Plotly	Showing charts and logs in a clean web view

---

Security & Reliability

Control	Implementation
Token Refresh	The code automatically updates security tokens before sending money requests.
Data Checks	Pydantic blocks broken or dangerous data formats from entering the backend.
Offline Fallback	If the payment network goes down, the system shifts to safe local calculations.
Error Handling	Clean code blocks ensure network timeouts never freeze your main server scripts.

---

Quick Start

# Clone the project repository
git clone https://github.com/techindro/papaya.git
cd papaya

# Create and activate a clean virtual environment
python -m venv venv
source venv/bin/activate  # On Windows use: venv\Scripts\activate

# Install the basic Python packages
pip install -r requirements.txt

# Save your private PayPal Sandbox credentials to the environment
export PAYPAL_CLIENT_ID="your_sandbox_client_id_here"
export PAYPAL_CLIENT_SECRET="your_sandbox_client_secret_here"

# Start the main backend server (Terminal 1)
uvicorn backend.main:app --reload

# Start the dashboard UI script (Terminal 2)
streamlit run simulator/app_dashboard.py

Open http://localhost:8501 in your web browser to check the working project dashboard.

Environment Configuration

Variable	Required	Description
PAYPAL_CLIENT_ID	Yes	The app client string from your PayPal developer panel.
PAYPAL_CLIENT_SECRET	Yes	The private app secret string for logging in securely.

---

Repository Structure

papaya/
│
├── .gitignore               # Excludes caches, local environments, and token flags
├── LICENSE                  # MIT License terms open-source protection
├── README.md                # System documentation and deployment blueprints
├── requirements.txt         # Production software package dependencies
│
├── assets/
│   └── logo.jpg             # Project logo
│
├── backend/                 # High-performance processing server compartment
│   ├── __init__.py
│   ├── config.py            # API paths and credential properties configuration
│   └── main.py              # FastAPI application server core communication logic
│
└── simulator/               # Live interactive demonstration environment
    ├── __init__.py
    └── app_dashboard.py     # Streamlit application UI dashboard displaying metric logs

Contributing Team

Shubham Patel, Lead AI Systems Architect & Core Infrastructure Developer

Pratyush Kumar Nishad, Full-Stack Integration Engineer & Product Lead

License

Released under the MIT License.