openfilenet 0.1.1

OpenFilenet is a lightweight, zero-config peer-to-peer file sharing library designed to abstract away file sharing across code runtimes.

OpenFilenet lets your code access files on other machines as if they were local, using a tiny, dependency-free P2P protocol (optionally secured with AES-256-GCM) using a lightweight, zero-config peer-to-peer file sharing library designed for code runtimes to be as developer friendly as possible. Openfilenet abstracts away the file sharing protocol and makes it super easy to implement in-line on multiple instances of Python.

Features:

• Works over local networks / Wi-Fi / Ethernet / VPN.
• Cross-platform (Windows, Linux, macOS, Raspberry Pi).
• No servers, no Daemons, no config files.
• Shared files/directories accessible in-line.
• Async discovery via UDP broadcast.
• Encrypted transfer option.
• Ideal for ML pipelines, sensors, robotics, distributed data collection, edge devices.

• OpenFilenet abstracts away networking so your code can access remote files with zero overhead.

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Usage example

Usecase: Run openfilenet to share, and receive and process a csv file across two peers connected over LAN.

• For more detail, see function reference doc.

Peer 1

1. Install openfilenet

pip install openfilenet
pip install cryptography

2. Example peer_share.py

"""
Peer A: Share a local CSV file via OpenFilenet to all the peers.
"""
import time
import openfilenet as ofn

# -------------------------------------------------------------------
# openfilenet configuration
# -------------------------------------------------------------------
# Token / namespace. All peers using this token / namespace can see each other on the network.
ofn.token = "demo"

# Optional: Enable debug
# ofn.debug = True

# Optional: override UDP discovery port (default: 51230)
# ofn.port.udp_discovery = 51230

# Optional: force a specific TCP server port (default: 0 = OS chooses)
# ofn.port.tcp_server = 50000

# Optional: enable AES256 encryption (AES-256-GCM) for get_file() transfers
# If you enable this, all peers must set the SAME key and have the `cryptography` package installed.
ofn.encrypt = True
ofn.key = "secret"

# -------------------------------------------------------------------
# Share a file or directory on the P2P network
# -------------------------------------------------------------------
# Change this to the absolute or relative path of file or directory.
CSV_PATH = "path/to/your/dir/or/file"  # e.g. on windows "C:\\Users\\username\\data\\my_data.csv"

ofn.share_files(CSV_PATH)

print(f"Sharing CSV file: {CSV_PATH}")
print("Peers using the same token: ", ofn.token," can now discover and fetch the shared file(s).")
print("Press Ctrl+C to exit.")

# Keep the process alive so the TCP server and discovery keep running
while True:
    time.sleep(10)

Peer 2

1. Install openfilenet

pip install openfilenet
pip install cryptography

2. Example peer_receive.py

"""
Peer B: Discover a remote CSV file via OpenFilenet and process it in memory.
"""

import time
import io
import csv
from pathlib import Path

import openfilenet as ofn

# ---------------------------------------------------------------------------
# openfilenet configuration
# ---------------------------------------------------------------------------

# Must match the token used on the sharing peer.
ofn.token = "demo"

# Enable debug logs to see discovery and connections.
# ofn.debug = True

# Optional: enable encryption (must match sharer if used).
ofn.encrypt = True
ofn.key = "secret"


# ---------------------------------------------------------------------------
# Wait for files to appear on the P2P network
# ---------------------------------------------------------------------------

def wait_for_remote_files(timeout_seconds: int = 10, poll_interval: float = 1.0):
    """
    Poll openfilenet.list_files() until we see at least one file
    or until timeout is reached.

    Discovery is asynchronous, so this ensures we see peers that come online.
    """
    start = time.time()
    while time.time() - start < timeout_seconds:
        files = ofn.list_files()
        if files:
            return files
        print("No remote files yet, waiting...")
        time.sleep(poll_interval)
    return []


print("Looking for remote files...")

files = wait_for_remote_files(timeout_seconds=10)

if not files:
    print(
        "No remote files found."
        f"Is the sharing peer running and using token {ofn.token!r}?"
    )
    raise SystemExit

print("\nDiscovered remote files:")
for f in files:
    print(f'  peer={f["peer_id"]}  path={f["path"]}  size={f["size"]}')
print()


# ---------------------------------------------------------------------------
# Fetch and process the file (CSV) in memory
# ---------------------------------------------------------------------------

# fetch the file the sharer is exposing. (use a loop for multiple files)
fmeta = files[0]
peer_id = fmeta["peer_id"]
remote_path = fmeta["path"]

print(f"Fetching remote CSV from peer={peer_id} path={remote_path}")

# Get file bytes IN MEMORY (no temp files).
data = ofn.get_file(peer_id, remote_path)  # dest=None => returns bytes

# Decode bytes into text (UTF-8 is common for CSV).
text = data.decode("utf-8", errors="replace")

# Parse CSV using Python's built-in CSV reader.
buf = io.StringIO(text)
reader = csv.reader(buf)

try:
    header = next(reader)   # first row = column names
except StopIteration:
    print("The CSV file appears to be empty.")
    raise SystemExit

# Count remaining rows
row_count = sum(1 for _ in reader)

print("\nCSV metadata:")
print(f"  Columns ({len(header)}): {header}")
print(f"  Rows: {row_count}")
print(f"  Dimensions: {row_count} rows × {len(header)} columns\n")


# ---------------------------------------------------------------------------
# Or plug in your own processing logic
# ---------------------------------------------------------------------------
# For example:
#   - load into pandas
#   - feed into a model
#   - compute statistics
#   - filter rows, etc.
#
# Example:
#
# import pandas as pd
# buf.seek(0)    # rewind to beginning
# df = pd.read_csv(buf)
# print(df.head())

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"Data should empower, not overwhelm"