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GNU Affero General Public License
=================================
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Notwithstanding any other provision of this License, if you modify the
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### 17. Interpretation of Sections 15 and 16
If the disclaimer of warranty and limitation of liability provided
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_END OF TERMS AND CONDITIONS_
## How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the “copyright” line and a pointer to where the full notice is found.
OpenRecall
Copyright (C) 2024 Koen van Eijk, Thomas van Turnhout
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see http://www.gnu.org/licenses/.
Also add information on how to contact you by electronic and paper mail.
If your software can interact with users remotely through a computer
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solutions will be better for different programs; see section 13 for the
specific requirements.
You should also get your employer (if you work as a programmer) or school,
if any, to sign a “copyright disclaimer” for the program, if necessary.
For more information on this, and how to apply and follow the GNU AGPL, see http://www.gnu.org/licenses/

19
readme.md → READM.md
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@ -27,7 +27,7 @@ OpenRecall offers several key advantages over closed-source alternatives:
- **Transparency**: OpenRecall is 100% open-source, allowing you to audit the source code for potential backdoors or privacy-invading features. - **Transparency**: OpenRecall is 100% open-source, allowing you to audit the source code for potential backdoors or privacy-invading features.
- **Cross-platform Support**: OpenRecall works seamlessly on Windows, macOS, and Linux, giving you the freedom to use it on your preferred operating system. - **Cross-platform Support**: OpenRecall works seamlessly on Windows, macOS, and Linux, giving you the freedom to use it on your preferred operating system.
- **Privacy-focused**: Your data is stored locally on your device, and you have the option to encrypt it with a password for added security. No cloud integration is required. - **Privacy-focused**: (soon to be implemented) Your data is stored locally on your device, and you have the option to encrypt it with a password for added security. No cloud integration is required.
- **Hardware Compatibility**: OpenRecall is designed to work with a wide range of hardware, unlike proprietary solutions that may require specific certified devices. - **Hardware Compatibility**: OpenRecall is designed to work with a wide range of hardware, unlike proprietary solutions that may require specific certified devices.
- -
<p align="center"> <p align="center">
@ -40,7 +40,7 @@ OpenRecall offers several key advantages over closed-source alternatives:
- **Time Travel**: Go back in time and revisit your digital activities on Windows, macOS, or Linux. - **Time Travel**: Go back in time and revisit your digital activities on Windows, macOS, or Linux.
- **Local-first AI**: OpenRecall leverages local AI processing to ensure your data remains private and secure. - **Local-first AI**: OpenRecall leverages local AI processing to ensure your data remains private and secure.
- **Encrypted Storage**: Optionally encrypt your local data with a password to protect against unauthorized access, even if your device is lost or stolen. - **Encrypted Storage**: (soon to be implemented) Optionally encrypt your local data with a password to protect against unauthorized access, even if your device is lost or stolen.
- **Pause Functionality**: Pause OpenRecall indefinitely or for a specified duration (e.g., 30 minutes) to maintain privacy when needed. - **Pause Functionality**: Pause OpenRecall indefinitely or for a specified duration (e.g., 30 minutes) to maintain privacy when needed.
- **Exclude apps**: Exclude apps that you do not want to record - **Exclude apps**: Exclude apps that you do not want to record
@ -62,7 +62,20 @@ We have exciting plans for OpenRecall's future, including:
## Get Started ## Get Started
To start using OpenRecall, simply visit our GitHub repository, follow the installation instructions, and take control of your digital memory today! ### Prerequisites
- Python 3.11
- MacOSX/Windows/Linux
To install:
```
pip install git+https://github.com/openrecall/openrecall.git
```
To run:
```
python3 -m openrecall.app
```
Open your browser to:
[http://localhost:8082](http://localhost:8082) to access OpenRecall.
## Contribute ## Contribute

0
openrecall/__init__.py Normal file
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506
openrecall/app.py Normal file
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@ -0,0 +1,506 @@
import os
import sqlite3
import sys
import threading
import time
import mss
import numpy as np
from doctr.models import ocr_predictor
from flask import Flask, render_template_string, request, send_from_directory
from PIL import Image
from sentence_transformers import SentenceTransformer
def get_appdata_folder(app_name="openrecall"):
"""
Get the path to the application data folder.
Args:
app_name (str): The name of the application.
Returns:
str: The path to the application data folder.
"""
if sys.platform == "win32":
appdata = os.getenv("APPDATA")
if not appdata:
raise EnvironmentError("APPDATA environment variable is not set.")
path = os.path.join(appdata, app_name)
elif sys.platform == "darwin":
home = os.path.expanduser("~")
path = os.path.join(home, "Library", "Application Support", app_name)
else: # Linux and other Unix-like systems
home = os.path.expanduser("~")
path = os.path.join(home, ".local", "share", app_name)
if not os.path.exists(path):
os.makedirs(path)
return path
appdata_folder = get_appdata_folder()
print(f"All data is stored in: {appdata_folder}")
db_path = os.path.join(appdata_folder, "recall.db")
screenshots_path = os.path.join(appdata_folder, "screenshots")
# ensure the screenshots folder exists
if not os.path.exists(screenshots_path):
try:
os.makedirs(screenshots_path)
except:
pass
def get_active_app_name_osx():
"""Returns the name of the active application."""
from AppKit import NSWorkspace
active_app = NSWorkspace.sharedWorkspace().activeApplication()
return active_app["NSApplicationName"]
def get_active_window_title_osx():
"""Returns the title of the active window."""
from Quartz import (
CGWindowListCopyWindowInfo,
kCGNullWindowID,
kCGWindowListOptionOnScreenOnly,
)
app_name = get_active_app_name_osx()
windows = CGWindowListCopyWindowInfo(
kCGWindowListOptionOnScreenOnly, kCGNullWindowID
)
for window in windows:
if window["kCGWindowOwnerName"] == app_name:
return window.get("kCGWindowName", "Unknown")
return None
def get_active_app_name_windows():
"""returns the app's name .exe"""
import psutil
import win32gui
pid = win32gui.GetForegroundWindow()
exe = psutil.Process(win32gui.GetWindowThreadProcessId(pid)[-1]).name()
return exe
def get_active_window_title_windows():
"""Returns the title of the active window."""
import win32gui
hwnd = win32gui.GetForegroundWindow()
window_title = win32gui.GetWindowText(hwnd)
return window_title
def get_active_app_name():
if sys.platform == "win32":
return get_active_app_name_windows()
elif sys.platform == "darwin":
return get_active_app_name_osx()
else:
raise NotImplementedError("This platform is not supported")
def get_active_window_title():
if sys.platform == "win32":
return get_active_window_title_windows()
elif sys.platform == "darwin":
return get_active_window_title_osx()
else:
raise NotImplementedError("This platform is not supported")
def create_db():
# create table if not exists for entries, with columns id, text, datetime, and embedding (blob)
conn = sqlite3.connect(db_path)
c = conn.cursor()
c.execute(
"""CREATE TABLE IF NOT EXISTS entries
(id INTEGER PRIMARY KEY AUTOINCREMENT, app TEXT, title TEXT, text TEXT, timestamp INTEGER, embedding BLOB)"""
)
conn.commit()
conn.close()
def get_embedding(text):
# Initialize the model
model = SentenceTransformer("all-MiniLM-L6-v2")
# Split text into sentences
sentences = text.split("\n")
# Get sentence embeddings
sentence_embeddings = model.encode(sentences)
# Aggregate embeddings (mean pooling in this example)
mean = np.mean(sentence_embeddings, axis=0)
# convert to float64
mean = mean.astype(np.float64)
return mean
ocr = ocr_predictor(
pretrained=True,
det_arch="db_mobilenet_v3_large",
reco_arch="crnn_mobilenet_v3_large",
)
def take_screenshot(monitor=1):
"""
Take a screenshot of the specified monitor.
Args:
monitor (int): The index of the monitor to capture the screenshot from.
Returns:
numpy.ndarray: The screenshot image as a numpy array.
"""
with mss.mss() as sct:
monitor_ = sct.monitors[monitor]
screenshot = np.array(sct.grab(monitor_))
screenshot = screenshot[:, :, [2, 1, 0]]
return screenshot
def record_screenshot_thread():
"""
Thread function to continuously record screenshots and process them.
This function takes screenshots at regular intervals and compares them with the previous screenshot.
If the new screenshot is different enough from the previous one, it saves the screenshot, performs OCR on it,
extracts the text, computes the embedding, and stores the entry in the database.
Returns:
None
"""
last_screenshot = take_screenshot()
while True:
screenshot = take_screenshot()
if not is_similar(screenshot, last_screenshot):
last_screenshot = screenshot
image = Image.fromarray(screenshot)
timestamp = int(time.time())
image.save(
os.path.join(screenshots_path, f"{timestamp}.webp"),
format="webp",
lossless=True,
)
result = ocr([screenshot])
text = ""
for page in result.pages:
for block in page.blocks:
for line in block.lines:
for word in line.words:
text += word.value + " "
text += "\n"
text += "\n"
embedding = get_embedding(text)
active_app_name = get_active_app_name()
active_window_title = get_active_window_title()
# connect to db
conn = sqlite3.connect(db_path)
c = conn.cursor()
# Insert the entry into the database
embedding_bytes = embedding.tobytes()
c.execute(
"INSERT INTO entries (text, timestamp, embedding, app, title) VALUES (?, ?, ?, ?, ?)",
(
text,
timestamp,
embedding_bytes,
active_app_name,
active_window_title,
),
)
# Commit the transaction
conn.commit()
conn.close()
time.sleep(3)
def mean_structured_similarity_index(img1, img2, L=255):
"""Compute the mean Structural Similarity Index between two images."""
K1, K2 = 0.01, 0.03
C1, C2 = (K1 * L) ** 2, (K2 * L) ** 2
# Convert images to grayscale
def rgb2gray(img):
return 0.2989 * img[..., 0] + 0.5870 * img[..., 1] + 0.1140 * img[..., 2]
img1_gray = rgb2gray(img1)
img2_gray = rgb2gray(img2)
# Means
mu1 = np.mean(img1_gray)
mu2 = np.mean(img2_gray)
# Variances and covariances
sigma1_sq = np.var(img1_gray)
sigma2_sq = np.var(img2_gray)
sigma12 = np.mean((img1_gray - mu1) * (img2_gray - mu2))
# SSIM computation
ssim_index = ((2 * mu1 * mu2 + C1) * (2 * sigma12 + C2)) / (
(mu1**2 + mu2**2 + C1) * (sigma1_sq + sigma2_sq + C2)
)
return ssim_index
def is_similar(img1, img2, similarity_threshold=0.9):
"""Check if two images are similar based on a given similarity threshold."""
similarity = mean_structured_similarity_index(img1, img2)
return similarity >= similarity_threshold
def cosine_similarity(a, b):
return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
app = Flask(__name__)
def human_readable_time(timestamp):
import datetime
now = datetime.datetime.now()
dt_object = datetime.datetime.fromtimestamp(timestamp)
diff = now - dt_object
if diff.days > 0:
return f"{diff.days} days ago"
elif diff.seconds < 60:
return f"{diff.seconds} seconds ago"
elif diff.seconds < 3600:
return f"{diff.seconds // 60} minutes ago"
else:
return f"{diff.seconds // 3600} hours ago"
def timestamp_to_human_readable(timestamp):
import datetime
dt_object = datetime.datetime.fromtimestamp(timestamp)
return dt_object.strftime("%Y-%m-%d %H:%M:%S")
app.jinja_env.filters["human_readable_time"] = human_readable_time
app.jinja_env.filters["timestamp_to_human_readable"] = timestamp_to_human_readable
@app.route("/")
def timeline():
# connect to db
conn = sqlite3.connect(db_path)
c = conn.cursor()
results = c.execute(
"SELECT timestamp FROM entries ORDER BY timestamp DESC LIMIT 1000"
).fetchall()
timestamps = [result[0] for result in results]
conn.close()
return render_template_string(
"""
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>OpenRecall - Timeline</title>
<!-- Bootstrap CSS -->
<link href="https://stackpath.bootstrapcdn.com/bootstrap/4.5.2/css/bootstrap.min.css" rel="stylesheet">
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bootstrap-icons@1.3.0/font/bootstrap-icons.css">
<style>
.slider-container {
display: flex;
flex-direction: column;
align-items: center;
padding: 20px;
}
.slider {
width: 80%;
}
.slider-value {
margin-top: 10px;
font-size: 1.2em;
}
.image-container {
margin-top: 20px;
text-align: center;
}
.image-container img {
max-width: 100%;
height: auto;
}
</style>
</head>
<body>
<nav class="navbar navbar-light bg-light">
<div class="container">
<form class="form-inline my-2 my-lg-0 w-100 d-flex" action="/search" method="get">
<input class="form-control flex-grow-1 mr-sm-2" type="search" name="q" placeholder="Search" aria-label="Search">
<button class="btn btn-outline-secondary my-2 my-sm-0" type="submit">
<i class="bi bi-search"></i>
</button>
</form>
</div>
</nav>
<div class="container">
<div class="slider-container">
<input type="range" class="slider custom-range" id="discreteSlider" min="0" max="{{timestamps|length - 1}}" step="1" value="{{timestamps|length - 1}}">
<div class="slider-value" id="sliderValue">{{timestamps[0] | timestamp_to_human_readable }}</div>
</div>
<div class="image-container">
<img id="timestampImage" src="/static/{{timestamps[0]}}.webp" alt="Image for timestamp">
</div>
</div>
<!-- Bootstrap and jQuery JS -->
<script src="https://code.jquery.com/jquery-3.5.1.slim.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/@popperjs/core@2.5.3/dist/umd/popper.min.js"></script>
<script src="https://stackpath.bootstrapcdn.com/bootstrap/4.5.2/js/bootstrap.min.js"></script>
<script>
const timestamps = {{ timestamps|tojson }};
const slider = document.getElementById('discreteSlider');
const sliderValue = document.getElementById('sliderValue');
const timestampImage = document.getElementById('timestampImage');
slider.addEventListener('input', function() {
const reversedIndex = timestamps.length - 1 - slider.value;
const timestamp = timestamps[reversedIndex];
sliderValue.textContent = new Date(timestamp * 1000).toLocaleString(); // Convert to human-readable format
timestampImage.src = `/static/${timestamp}.webp`;
});
// Initialize the slider with a default value
slider.value = timestamps.length - 1;
sliderValue.textContent = new Date(timestamps[0] * 1000).toLocaleString(); // Convert to human-readable format
timestampImage.src = `/static/${timestamps[0]}.webp`;
</script>
</body>
</html>
""",
timestamps=timestamps,
)
@app.route("/search")
def search():
q = request.args.get("q")
# load embeddings from db to numpy array
conn = sqlite3.connect(db_path)
c = conn.cursor()
# Get all entries
results = c.execute("SELECT * FROM entries").fetchall()
embeddings = []
for result in results:
embeddings.append(np.frombuffer(result[5], dtype=np.float64))
embeddings = np.array(embeddings)
# Get the embedding of the query
query_embedding = get_embedding(q)
# Compute the cosine similarity between the query and all entries
similarities = []
for embedding in embeddings:
similarities.append(cosine_similarity(query_embedding, embedding))
# Sort the entries by similarity
indices = np.argsort(similarities)[::-1]
entries = []
for i in indices:
result = results[i]
entries.append(
{
"text": result[3],
"timestamp": result[4],
"image_path": f"/static/{result[4]}.webp",
}
)
return render_template_string(
"""
<html>
<head>
<title>Search Results</title>
<link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.5.2/css/bootstrap.min.css">
</head>
<body>
<nav class="navbar navbar-light bg-light">
<a class="navbar-brand" href="/">Back to Home</a>
</nav>
<div class="container">
<h1 class="display-4">Search Results</h1>
<div class="row">
{% for entry in entries %}
<div class="col-md-3 mb-4">
<div class="card">
<a href="#" data-toggle="modal" data-target="#modal-{{ loop.index0 }}">
<img src="{{ entry.image_path }}" alt="Image" class="card-img-top">
</a>
</div>
</div>
<!-- Modal -->
<div class="modal fade" id="modal-{{ loop.index0 }}" tabindex="-1" role="dialog" aria-labelledby="exampleModalLabel" aria-hidden="true">
<div class="modal-dialog modal-xl" role="document" style="max-width: none; width: 100vw; height: 100vh; padding: 20px;">
<div class="modal-content" style="height: calc(100vh - 40px); width: calc(100vw - 40px); padding: 0;">
<div class="modal-body" style="padding: 0;">
<img src="{{ entry.image_path }}" alt="Image" style="width: 100%; height: 100%; object-fit: contain; margin: 0 auto;">
</div>
</div>
</div>
</div>
{% endfor %}
</div>
</div>
<!-- Bootstrap and jQuery JS -->
<script src="https://code.jquery.com/jquery-3.5.1.slim.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/@popperjs/core@2.5.3/dist/umd/popper.min.js"></script>
<script src="https://stackpath.bootstrapcdn.com/bootstrap/4.5.2/js/bootstrap.min.js"></script>
</body>
</html>
""",
entries=entries,
)
@app.route("/static/<filename>")
def serve_image(filename):
return send_from_directory(screenshots_path, filename)
if __name__ == "__main__":
create_db()
# Start the thread to record screenshots
t = threading.Thread(target=record_screenshot_thread)
t.start()
app.run(port=8082)

11
requirements.txt Normal file
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@ -0,0 +1,11 @@
Flask==3.0.3
numpy==1.26.4
mss==9.0.1
pillow==10.3.0
sentence-transformers==3.0.0
torch==2.3.0
torchvision==0.18.0
git+https://github.com/koenvaneijk/doctr.git
shapely
h5py
rapidfuzz

40
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@ -0,0 +1,40 @@
import io
import platform
from setuptools import find_packages, setup
# Read the README.md file
with io.open("README.md", "r", encoding="utf-8") as f:
long_description = f.read()
# Read the requirements from requirements.txt
with open("requirements.txt", "r") as f:
install_requires = [line.strip() for line in f.readlines()]
# Define OS-specific dependencies
extras_require = {"windows": ["pywin32"], "macos": ["pyobjc"], "linux": []}
# Determine the current OS
current_os = platform.system().lower()
if current_os.startswith("win"):
current_os = "windows"
elif current_os == "darwin":
current_os = "macos"
elif current_os == "linux":
current_os = "linux"
else:
current_os = None
# Include the OS-specific dependencies if the current OS is recognized
if current_os and current_os in extras_require:
install_requires.extend(extras_require[current_os])
setup(
name="OpenRecall",
version="0.1",
packages=find_packages(),
install_requires=install_requires,
long_description=long_description,
long_description_content_type="text/markdown",
extras_require=extras_require,
)