DEV Community: ProspexAI

ScrapeGraphAI:Revolutionizing Web Scraping with AI

ProspexAI — Wed, 05 Jun 2024 19:13:33 +0000

ScrapeGraphAI is an innovative web scraping Python library that leverages Large Language Models (LLMs) and direct graph logic to create sophisticated scraping pipelines for websites and local documents. This guide will be divided into three parts to provide a detailed overview, setup instructions, use cases, and real-world examples.

Introduction to ScrapeGraphAI and Setup

Overview:
ScrapeGraphAI stands out in the data-intensive digital landscape by integrating LLMs and modular graph-based pipelines. This library automates the scraping of data from various sources like websites and local files (XML, HTML, JSON, etc.).

Why Choose ScrapeGraphAI?
Traditional scraping tools often require manual configuration and struggle with changing website structures. ScrapeGraphAI, powered by LLMs, adapts to these changes, reducing the need for constant developer intervention and ensuring continuous functionality.

Features:

Supports multiple LLMs, including GPT, Gemini, Groq, Azure, Hugging Face, and local models via Ollama.
Flexible and low-maintenance, adapting to website structure changes automatically.

Library Diagram:
The diagram illustrates the high-level architecture of ScrapeGraphAI, showcasing its nodes, graphs, and models.

Installation:

Prerequisites:
- Python >= 3.9
- Pip
- Ollama (for local models)
Install the Library:

   pip install scrapegraphai

Optional: Using Rye for Dependency Management:

   rye pin 3.10
   rye sync
   rye build

Additional Requirements for WSL on Windows:

   sudo apt-get -y install libnss3 libnspr4 libgbm1 libasound2

Using ScrapeGraphAI for Different Scenarios

1. SmartScraperGraph with Local Models:
Configure the graph for local models and run it to scrape a list of projects from a website.

Example:

from scrapegraphai.graphs import SmartScraperGraph

graph_config = {
    "llm": {
        "model": "ollama/mistral",
        "temperature": 0,
        "format": "json",
        "base_url": "http://localhost:11434",
    },
    "embeddings": {
        "model": "ollama/nomic-embed-text",
        "base_url": "http://localhost:11434",
    },
    "verbose": True,
}

smart_scraper_graph = SmartScraperGraph(
    prompt="List me all the projects with their descriptions",
    source="https://perinim.github.io/projects",
    config=graph_config
)

result = smart_scraper_graph.run()
print(result)

2. SearchGraph with Mixed Models:
Combine Groq for LLM and Ollama for embeddings to scrape multiple pages based on search results.

Example:

from scrapegraphai.graphs import SearchGraph

graph_config = {
    "llm": {
        "model": "groq/gemma-7b-it",
        "api_key": "GROQ_API_KEY",
        "temperature": 0
    },
    "embeddings": {
        "model": "ollama/nomic-embed-text",
        "base_url": "http://localhost:11434",
    },
    "max_results": 5,
}

search_graph = SearchGraph(
    prompt="List me all the traditional recipes from Chioggia",
    config=graph_config
)

result = search_graph.run()
print(result)

3. SpeechGraph with OpenAI:
Generate an audio summary from a website using OpenAI’s LLM and TTS models.

Example:

from scrapegraphai.graphs import SpeechGraph

graph_config = {
    "llm": {
        "api_key": "OPENAI_API_KEY",
        "model": "gpt-3.5-turbo",
    },
    "tts_model": {
        "api_key": "OPENAI_API_KEY",
        "model": "tts-1",
        "voice": "alloy"
    },
    "output_path": "audio_summary.mp3",
}

speech_graph = SpeechGraph(
    prompt="Make a detailed audio summary of the projects.",
    source="https://perinim.github.io/projects/",
    config=graph_config,
)

result = speech_graph.run()
print(result)

Advanced Use Cases and Custom Configurations

Advanced Configurations:
Explore the various configurations and customizations possible with ScrapeGraphAI, such as proxy settings, detailed logging, and handling authentication.

Node Types:
Understand the different nodes available in ScrapeGraphAI, like ConditionalNode, FetchNode, ParseNode, and more, which allow for fine-grained control over the scraping process.

Use Cases:

E-commerce: Scrape product details, reviews, and pricing from multiple websites.
Real Estate: Gather property listings, descriptions, and prices for market analysis.
News Aggregation: Collect news articles, summaries, and metadata for a news aggregator.

Future Roadmap:
ScrapeGraphAI’s development roadmap includes integrating more LLM APIs, improving documentation, and adding features like proxy rotation and enhanced search engine support.

Conclusion

ScrapeGraphAI offers a flexible and powerful solution for web scraping, harnessing the capabilities of LLMs to adapt to changing web structures and simplify data extraction. By following this guide, you can set up and use ScrapeGraphAI effectively for a variety of use cases, ensuring efficient and reliable data scraping.

Sources:

- ScrapeGraphAI GitHub Repository

Mastering YOLOv10: A Complete Guide with Hands-On Projects

ProspexAI — Thu, 30 May 2024 16:16:05 +0000

Introduction

In the rapidly evolving field of computer vision, YOLO (You Only Look Once) models have consistently stood out for their remarkable balance between computational cost and detection performance. YOLOv10, the latest iteration, addresses key inefficiencies and introduces a slew of innovations, making it a game-changer for real-time object detection. This guide will walk you through the significant improvements in YOLOv10 and provide step-by-step instructions to implement object detection and region counting projects using YOLOv10.

👉 Check out my earlier articles for more information:

Django Articles

💡I write about Machine Learning web application with python NodeJs Javascripts on Dev.to || Github || Kaggle || Linkedin || Medium. 🔔 Follow “ProspexAI” for future updates!

Setup
Example 1: Object Detection with YOLOv10
Example 2: Region Counting Using YOLOv10
Comparing YOLOv10 to Previous Versions and Other Models

Academic Perspective

YOLO models are popular in real-time object detection for their balance between computational cost and detection performance. Over the years, researchers have improved their designs, objectives, and data strategies, but reliance on non-maximum suppression increases latency and hinders end-to-end deployment. Various YOLO components have inefficiencies that limit their capability.

YOLOv10 Improvements

YOLOv10 addresses these issues with NMS-free training for lower latency and an efficiency-accuracy driven design strategy. The authors introduced consistent dual assignments for NMS-free training, which simultaneously achieves competitive performance and low inference latency. They also proposed a holistic efficiency-accuracy driven model design strategy, optimizing various YOLO components from both efficiency and accuracy perspectives. This reduces computational overhead and enhances performance.

Performance Comparison

Experiments show YOLOv10 achieves state-of-the-art performance and efficiency. For example, YOLOv10-S is 1.8 times faster than RT-DETR-R18 with similar accuracy and has fewer parameters and FLOPs. Compared to YOLOv9-C, YOLOv10-B has 46% less latency and 25% fewer parameters for the same performance.

Visualization

Here are visual comparisons of YOLOv10 with previous YOLO versions and other models in terms of latency and number of parameters:

Figure 1: Comparisons with others in terms of latency-accuracy (left) and size-accuracy (right) trade-offs. We measure the end-to-end latency using the official pre-trained models.

Setup

Before diving into the examples, let’s ensure we have the necessary setup. We’ll start by installing the required libraries.

Step 1: Install the Required Libraries

# Clone ultralytics repo
git clone https://github.com/ultralytics/ultralytics

# cd to local directory
cd ultralytics

# Install dependencies
pip install -r requirements.txt

Example 1: Object Detection with YOLOv10

Object detection is a fundamental task in computer vision. YOLOv10 enhances this by eliminating the need for non-maximum suppression (NMS) during inference, leading to lower latency and improved performance.

Step-by-Step Implementation

Download the model of your choice and ensure it fits your GPU memory. Here's a Python script to download videos from YouTube for testing:

from pytube import YouTube

# Replace 'YOUR_VIDEO_URL' with the URL of the YouTube video you want to download
video_url = 'your link here' 

# Create a YouTube object
yt = YouTube(video_url)

# Get the highest resolution stream available
video_stream = yt.streams.filter(progressive=True, file_extension='mp4').order_by('resolution').desc().first()

# Download the video
video_stream.download()

print("Download complete!")

Setup:

import cv2
import numpy as np
from ultralytics import YOLO

# Load YOLOv10 model
model = YOLO('yolov10.pt')

# Path to the video file
video_path = 'path/to/your/video.mp4'
cap = cv2.VideoCapture(video_path)

# Process Video Frames
while cap.isOpened():
    ret, frame = cap.read()
    if not ret:
        break

    # Perform object detection
    results = model(frame)

    # Draw bounding boxes
    for result in results:
        boxes = result['boxes']
        for box in boxes:
            x1, y1, x2, y2 = box['coords']
            label = box['label']
            confidence = box['confidence']
            cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
            cv2.putText(frame, f'{label} {confidence:.2f}', (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

    # Display the frame
    cv2.imshow('YOLOv10 Object Detection', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

Example 2: Region Counting Using YOLOv10

Region counting allows for tallying objects within specified areas, providing valuable insights in various applications such as surveillance and traffic monitoring. This example demonstrates how to count objects in defined regions using YOLOv10.

Step-by-Step Implementation

Define Regions and Setup Model:

from shapely.geometry import Polygon, Point

# Define counting regions
counting_regions = [
    {
        "name": "Region 1",
        "polygon": Polygon([(50, 80), (250, 20), (450, 80), (400, 350), (100, 350)]),
        "counts": 0,
        "color": (255, 0, 0)
    },
    {
        "name": "Region 2",
        "polygon": Polygon([(200, 250), (440, 250), (440, 550), (200, 550)]),
        "counts": 0,
        "color": (0, 255, 0)
    },
]

model = YOLO('yolov10.pt')

Process Video and Count Objects in Regions:

cap = cv2.VideoCapture('path/to/your/video.mp4')
while cap.isOpened():
    ret, frame = cap.read()
    if not ret:
        break

    # Perform object detection
    results = model(frame)

    # Draw regions
    for region in counting_regions:
        points = np.array(region["polygon"].exterior.coords, dtype=np.int32)
        cv2.polylines(frame, [points], isClosed=True, color=region["color"], thickness=2)
        region["counts"] = 0  # Reset counts for each frame

    # Count objects in regions
    for result in results:
        boxes = result['boxes']
        for box in boxes:
            x1, y1, x2, y2 = box['coords']
            center = Point((x1 + x2) / 2, (y1 + y2) / 2)
            for region in counting_regions:
                if region["polygon"].contains(center):
                    region["counts"] += 1

    # Display counts
    for region in counting_regions:
        text = f'{region["name"]}: {region["counts"]}'
        cv2.putText(frame, text, (int(region["polygon"].centroid.x), int(region["polygon"].centroid.y)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, region["color"], 2)

    # Display the frame
    cv2.imshow('YOLOv10 Region Counting', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

Community Support

For more information, you can explore Ultralytics YOLOv10 Docs.

YOLOv10 Resources:

GitHub
Docs

If you have any questions running the code in your environments, contact me directly.

Unlocking the Power of YOLOv10: Step-by-Step Guide with Real-World Examples

ProspexAI — Mon, 27 May 2024 20:38:08 +0000

Introduction

In the rapidly evolving field of computer vision, YOLO (You Only Look Once) models have consistently stood out for their remarkable balance between computational cost and detection performance. YOLOv10, the latest iteration, addresses key inefficiencies and introduces a slew of innovations, making it a game-changer for real-time object detection. This guide will walk you through the significant improvements in YOLOv10, provide a comparison with older YOLO versions and other models, and offer step-by-step instructions to implement object detection and region counting projects using YOLOv10.

Comparing YOLOv10 to Previous Versions and Other Models
Setup
Example 1: Object Detection with YOLOv10
Example 2: Region Counting Using YOLOv10

Comparing YOLOv10 to Previous Versions and Other Models

Academic Perspective

YOLOv10 Improvements

Performance Comparison

Visualization

Here are visual comparisons of YOLOv10 with previous YOLO versions and other models in terms of latency and number of parameters:

Setup

Before diving into the examples, let's ensure we have the necessary setup. We'll start by installing the required libraries.

Step 1: Install the Required Libraries



# Clone ultralytics repo
git clone https://github.com/ultralytics/ultralytics

# cd to local directory
cd ultralytics

# Install dependencies
pip install -r requirements.txt

Example 1: Object Detection with YOLOv10

Step-by-Step Implementation

Setup



   import cv2
   import numpy as np
   from ultralytics import YOLO

   # Load YOLOv10 model
   model = YOLO('yolov10.pt')

   # Path to the video file
   video_path = 'path/to/your/video.mp4'
   cap = cv2.VideoCapture(video_path)

Process Video Frames



   while cap.isOpened():
       ret, frame = cap.read()
       if not ret:
           break

       # Perform object detection
       results = model(frame)

       # Draw bounding boxes
       for result in results:
           boxes = result['boxes']
           for box in boxes:
               x1, y1, x2, y2 = box['coords']
               label = box['label']
               confidence = box['confidence']
               cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
               cv2.putText(frame, f'{label} {confidence:.2f}', (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

       # Display the frame
       cv2.imshow('YOLOv10 Object Detection', frame)
       if cv2.waitKey(1) & 0xFF == ord('q'):
           break

   cap.release()
   cv2.destroyAllWindows()

Example 2: Region Counting Using YOLOv10

Step-by-Step Implementation

Define Regions and Setup Model



   from shapely.geometry import Polygon, Point

   # Define counting regions
   counting_regions = [
       {
           "name": "Region 1",
           "polygon": Polygon([(50, 80), (250, 20), (450, 80), (400, 350), (100, 350)]),
           "counts": 0,
           "color": (255, 0, 0)
       },
       {
           "name": "Region 2",
           "polygon": Polygon([(200, 250), (440, 250), (440, 550), (200, 550)]),
           "counts": 0,
           "color": (0, 255, 0)
       },
   ]

   model = YOLO('yolov10.pt')

Process Video and Count Objects in Regions



   cap = cv2.VideoCapture('path/to/your/video.mp4')
   while cap.isOpened():
       ret, frame = cap.read()
       if not ret:
           break

       # Perform object detection
       results = model(frame)

       # Draw regions
       for region in counting_regions:
           points = np.array(region["polygon"].exterior.coords, dtype=np.int32)
           cv2.polylines(frame, [points], isClosed=True, color=region["color"], thickness=2)
           region["counts"] = 0  # Reset counts for each frame

       # Count objects in regions
       for result in results:
           boxes = result['boxes']
           for box in boxes:
               x1, y1, x2, y2 = box['coords']
               center = Point((x1 + x2) / 2, (y1 + y2) / 2)
               for region in counting_regions:
                   if region["polygon"].contains(center):
                       region["counts"] += 1

       # Display counts
       for region in counting_regions:
           text = f'{region["name"]}: {region["counts"]}'
           cv2.putText(frame, text, (int(region["polygon"].centroid.x), int(region["polygon"].centroid.y)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, region["color"], 2)

       # Display the frame
       cv2.imshow('YOLOv10 Region Counting', frame)
       if cv2.waitKey(1) & 0xFF == ord('q'):
           break

   cap.release()
   cv2.destroyAllWindows()

Community Support

For more information, you can explore Ultralytics YOLOv10 Docs.

Ultralytics Resources:

Discord Community

YOLOv10 Resources:

GitHub
Docs

Unlocking the Power of NodeJS with Human Library

ProspexAI — Sat, 25 May 2024 12:33:58 +0000

The Power of NodeJS and Its Usage

NodeJS is a powerful runtime environment that allows developers to build scalable and high-performance applications using JavaScript. Its event-driven architecture and non-blocking I/O operations make it an ideal choice for building real-time applications such as chat applications, online gaming, collaboration tools, and much more.

In this post, we'll explore the "Human Library," a comprehensive AI-powered toolkit for 3D face detection, rotation tracking, face description, recognition, and more. We'll delve into its features, installation process, and various use cases, demonstrating how NodeJS enhances its capabilities.

Features of the Human Library

The Human Library boasts an impressive array of features, making it a versatile tool for developers working on a wide range of applications:

3D Face Detection & Rotation Tracking: Accurate detection and tracking of faces in 3D space.
Face Description & Recognition: Advanced algorithms for describing and recognizing faces.
Body Pose Tracking: Detailed tracking of body poses for various applications.
3D Hand & Finger Tracking: Precision tracking of hand and finger movements.
Iris Analysis: In-depth analysis of the iris for biometric applications.
Age, Gender & Emotion Prediction: Predict demographic and emotional states from facial data.
Gaze Tracking: Track where a person is looking in real-time.
Gesture Recognition: Recognize and interpret human gestures.
Body Segmentation: Separate the body from the background in images and videos.

Installation

Getting started with the Human Library is straightforward. Here's a step-by-step guide to install and set up the library:

Install NodeJS: Ensure you have NodeJS installed on your machine. You can download it from the official website.

Initialize a New NodeJS Project:

mkdir human-library
cd human-library
npm init -y

Install the Human Library:
```
npm install @vladmandic/human
```

Set Up a Basic Project: Create an index.js file with the following content to test the installation.

const Human = require('@vladmandic/human');

const human = new Human();

human.init().then(() => {
    console.log('Human library initialized');
});

Multi-use Cases

The Human Library is versatile and can be integrated into various applications. Here are a few examples:

Real-time Video Processing

Use the Human Library to process video streams from webcams or other sources, providing real-time feedback on facial expressions, gaze direction, and more.

Security and Surveillance

Implement face recognition and tracking to enhance security systems, allowing for automated detection of individuals and monitoring of their activities.

Healthcare

Utilize body pose tracking and gesture recognition to develop applications for physical therapy, enabling remote monitoring of exercises and movements.

Virtual and Augmented Reality

Incorporate 3D face and body tracking into VR and AR applications to create more immersive and interactive experiences.

Scalability and Possible Applications

NodeJS, with its non-blocking I/O and event-driven architecture, provides the perfect backbone for scalable applications using the Human Library. Whether you're developing a small prototype or a large-scale deployment, NodeJS can handle the load efficiently.

Application Examples

Facial Recognition in Retail: Enhance customer experiences by recognizing returning customers and providing personalized services.
Fitness Applications: Track users' exercises and provide real-time feedback on their form and movements.
Interactive Advertising: Create interactive billboards that respond to viewers' gestures and facial expressions.

Conclusion

The Human Library, combined with the power of NodeJS, opens up endless possibilities for developers. Its comprehensive set of features allows for the creation of innovative applications across various industries. By leveraging NodeJS's scalability and performance, you can build robust and high-performing solutions that take full advantage of AI-powered human tracking and recognition.

Resources

GitHub Repository: Human Library
NPM Package: @vladmandic/human
Live Demos: Human Library Demos

YOLOv9 vs. YOLOv8: Segmentation & Fine-Tuning Guide

ProspexAI — Fri, 24 May 2024 06:16:33 +0000

Introduction

The YOLOv9 model for object segmentation was released recently, offering superior performance to the previous YOLOv8 model. This article will compare YOLOv8 and YOLOv9, showcase YOLOv9 segmentation, and include a guide for fine-tuning YOLOv9 on your own datasets.

Comparison and Showcase

In this section, we will compare YOLOv8 and YOLOv9 performance and quickly showcase YOLOv9 segmentation.

YOLOv8 vs. YOLOv9

The new YOLO model uses techniques such as Programmable Gradient Information (PGI) and Generalized Efficient Layer Aggregation Network (GELAN) to improve performance.

YOLOv9 demonstrates improved accuracy and efficiency for object segmentation, achieving higher precision and recall rates compared to YOLOv8【12†source】【13†source】.

Demo

YOLOv9e segmentation demo:

Training

It’s possible to train YOLOv9 on your own segmentation data. The process can be divided into three steps: (1) Installation, (2) Dataset Creation, and (3) Fine-tuning/Training.

Installation

Begin by installing the Ultralytics framework:

pip install ultralytics

Dataset

Choose or create the dataset you need. The dataset needs to be in YOLO segmentation format, meaning each image shall have a corresponding text file (.txt) with the following content:

<class-index> <x1> <y1> <x2> <y2> ... <xn> <yn>
...
<class-index> <x1> <y1> <x2> <y2> ... <xn> <yn>

After finding a dataset and completing the image annotations, organize the dataset in the following way:

path/to/dataset/
├─ train/
│  ├─ img_0000.jpg
│  ├─ img_0000.txt
│  ├─ ...
│  ├─ img_0999.jpg
│  ├─ img_0999.txt
├─ val/
│  ├─ img_1000.jpg
│  ├─ img_1000.txt
│  ├─ ...
│  ├─ img_1099.jpg
│  ├─ img_1099.txt

Fine-Tuning

This section is for you if you want to train YOLOv9 on your custom data. If you’re just looking to use the model, skip ahead to the section Inference and Segmentation.

First, begin by creating a training configuration file:

# train.yaml
path: path/to/dataset
train: train
val: val

names:
  0: person
  1: bicycle
  2: car
  # ...
  77: teddy bear
  78: hair drier
  79: toothbrush

The configuration file shall contain the paths to the training and validation sets, class names, and class mapping.

Finally, train the model using the Ultralytics framework:

from ultralytics import YOLO

model = YOLO("yolov9c-seg.yaml")
model.train(data="path/to/train.yaml", epochs=100)

Make sure to use the correct segmentation model depending on your time constraints and hardware:

yolov9c-seg.yaml
yolov9e-seg.yaml

Best Practices

If you want to optimize the training performance, read this guide:

YOLOv8: Best Practices for Training

Inference and Segmentation

Run inference:

results = model("images/sofa.jpg")

Plot segmented masks:

import numpy as np
import matplotlib.pyplot as plt
import cv2

for result in results:
    height, width = result.orig_img.shape[:2]
    background = np.ones((height, width, 3), dtype=np.uint8) * 255

    masks = result.masks.xy
    for mask in masks:
        mask = mask.astype(int)
        cv2.drawContours(background, [mask], -1, (0, 255, 0), thickness=cv2.FILLED)

    plt.imshow(background)
    plt.title('Segmented objects')
    plt.axis('off')
    plt.show()

    plt.imsave('segmented_objects.jpg', background)

Plot segmentation mask with original colors:

import cv2
import numpy as np
import matplotlib.pyplot as plt

for result in results:
    height, width = result.orig_img.shape[:2]
    background = np.ones((height, width, 3), dtype=np.uint8) * 255
    masks = result.masks.xy
    orig_img = result.orig_img

    for mask in masks:
        mask = mask.astype(int)
        mask_img = np.zeros_like(orig_img)

        cv2.fillPoly(mask_img, [mask], (255, 255, 255))
        masked_object = cv2.bitwise_and(orig_img, mask_img)
        background[mask_img == 255] = masked_object[mask_img == 255]

    background_rgb = cv2.cvtColor(background, cv2.COLOR_BGR2RGB)

    plt.imshow(background_rgb)
    plt.title('Segmented objects')
    plt.axis('off')
    plt.show()

    cv2.imwrite('segmented_objects.jpg', background)

Bonus: Comparison with Other Technologies

Let's briefly compare YOLO with another popular computer vision technology, such as Mask R-CNN, in terms of performance and ease of use.

Performance: YOLO models are generally faster but might be less accurate for complex segmentation tasks compared to Mask R-CNN.
Ease of Use: YOLO is simpler to set up and use for real-time applications, while Mask R-CNN can be more complex but offers finer segmentation.

Word of the Day

Generalized Efficient Layer Aggregation Network (GELAN) - A technique used in YOLOv9 to enhance model performance by efficiently aggregating information across layers.

Conclusion

YOLOv9 offers significant improvements over YOLOv8, particularly in accuracy and efficiency for object segmentation tasks. Training and fine-tuning your own YOLOv9 model can be straightforward with the right dataset and tools. Don't hesitate to experiment and share your results!

Limitations:

The limitation of YOLO algorithm is that it struggles with small objects with in the image. The algorithm might not be able to detect very small object in the image, due to spatial constraints of the algorithm.

Courses & Projects

YOLOR Pro: course link
YOLOR Streamlit Dashboard: [project link](https://store.augmentedstartups.com/9ace9ef5-bf2d-4a95-b081-d4346d7a75bd
Mask Detection using YOLOR: project link
Weeds Detection using YOLOR: project link
Car Counting on Lane using YOLOR: project link

Feel free to ask any questions in the comments or reach out to me on Medium. Your feedback is valuable and helps me create better content for you.

Happy learning and coding!

Comparing Odoo 17 and Django Ledger for Accounting and Financial Management 2024

ProspexAI — Thu, 23 May 2024 08:52:06 +0000

As a developer or business owner, choosing the right accounting and financial management tool is crucial for the smooth operation of your business. In this post, we'll compare two powerful options: Odoo 17 and Django Ledger. We'll explore their features, auditing and financial management capabilities, costs, and suitability for different business sizes and needs.

Odoo 17

Odoo 17 is a comprehensive suite of business applications, with its accounting and invoicing modules being some of the most robust features. Let's dive into what it offers:

Accounting and Invoicing

Invoicing Automation: Odoo Invoicing allows you to create, send, and manage payments for invoices with ease. AI-powered invoice recognition and bank account synchronization streamline the process.
Accounts Payable and Receivable: Manage vendor bills, customer invoices, and credit notes efficiently.
Bank Synchronization: Automatically import transactions and reconcile accounts for a real-time overview of your cash flow.
Tax Management: Automate tax reports for multiple jurisdictions, ensuring compliance.
Financial Reports: Generate balance sheets, profit and loss statements, cash flow statements, and more in real-time.
Double-entry Bookkeeping: Ensures accurate and consistent transaction records.
Multi-company Management: Manage multiple companies within a single database.
Multi-currency Environment: Handle international transactions with automated exchange rates.

Auditing and Financial Management

Audit Trails: Detailed logs for all transactions and activities.
Budget Management: Create and track budgets against actual expenses.
Asset Management: Track assets, depreciation schedules, and movements.
Expense Management: Streamline employee expense reports and reimbursements.
International Standards: Supports over 70 countries with local fiscal requirements. ### Dashboard View ### Cost
Subscription-based Pricing: Costs vary based on the number of users and modules needed.
Implementation and Customization Costs: Can be significant depending on the extent of customization and integration.

Advanced Features

Bank Synchronization: Connect directly with banks to import transactions.
Online Payments: Supports various payment providers like Stripe, PayPal, and Adyen.
Inventory Valuation: Supports standard price, average price, LIFO, and FIFO methods.
Retained Earnings: Calculates in real-time with no year-end journal required.

Expense Management

Expense Categories: Configure specific expense types for accurate tracking.
Automated Expense Recording: Attach receipts, scan PDFs, and email expenses directly into the system.
Expense Reports: Submit and approve expense reports for reimbursement and accounting integration.

Django Ledger

Django Ledger is an open-source accounting system designed for financially driven applications using the Django Web Framework. It offers a high-level API that simplifies the implementation of accounting functionalities.

Accounting and Invoicing

High-Level API: Simplifies the process of implementing accounting functionalities.
Double-entry Accounting System: Ensures accurate and consistent financial records.
Multiple Hierarchical Chart of Accounts: Organize accounts in a hierarchical structure.
Financial Statements: Generates income statements, balance sheets, and cash flow statements.
Purchase Orders and Sales Orders: Manage estimates, bills, and invoices.
Automatic Financial Ratio and Insight Calculations: Provides insights into financial health.
Multi-tenancy: Supports multiple companies, users, and clients within a single instance.
Self-contained Ledgers, Journal Entries, and Transactions: Manages all financial transactions internally.

Auditing and Financial Management

Closing Entries: Automatically handles the closing of accounts at the end of fiscal periods.
Bank Accounts Information: Maintains detailed information on bank accounts.
OFX & QFX File Import: Allows importing financial data from external sources.
Financial Analysis Engine: Provides detailed financial analysis and insights.

Cost

Open-source: Free to use, making it cost-effective for small businesses and startups.
Customization Costs: Costs associated with development and integration based on complexity and expertise required.

Advanced Features

Entity Management UI: Built-in UI for managing entities.
Django Admin Classes: Leverages Django's admin interface for managing accounting data.
Inventory Management: Tracks items, lists, and inventory with units of measure.

Dashboard View

Summary

Features

Odoo 17: Comprehensive features for invoicing, accounting, bank synchronization, and financial reporting. Suitable for larger businesses with complex needs.
Django Ledger: Basic but effective features for small to medium-sized businesses. Highly customizable with in-house development capabilities.

Auditing and Financial Management

Odoo 17: Extensive audit trails, multi-company management, and detailed budgeting tools.
Django Ledger: Basic auditing and budgeting features with the flexibility for custom solutions.

Cost

Odoo 17: Higher total cost of ownership with subscription fees and potential implementation costs.
Django Ledger: Cost-effective with no initial software costs but potential customization and development expenses.

Suitability

Odoo 17: Ideal for larger enterprises or those needing comprehensive, integrated solutions with strong support.
Django Ledger: Suitable for smaller businesses or those with in-house technical expertise.

In conclusion, Odoo 17 provides a robust, feature-rich platform suitable for large enterprises with complex requirements, while Django Ledger offers a flexible, cost-effective solution for smaller businesses or those capable of handling custom development.

Feel free to share your thoughts or ask questions in the comments below. Happy accounting!

Unlocking Rapid Data Extraction: Groq + OCR and Claude Vision

ProspexAI — Wed, 22 May 2024 05:36:23 +0000

Introduction

In this article, we explore various methods for extracting data from documents, comparing OCR+LLM with Claude 3 Vision, and delving into fast OCR transformers and cloud-native OCRs. We also provide a code example for implementing OCR as a simple API using docTR and discuss how Groq can be leveraged to achieve the best inference speed for LLMs.

The Use Case: Document Scanning to Save Time

Imagine a SaaS platform that helps register invoices for a company. Speed and convenience are paramount, and while some errors are tolerable, the goal is to minimize them. This scenario highlights the need for rapid and reliable data extraction.

Claude 3 Vision vs OCR+LLM

Claude 3 Vision

Claude 3 Vision is known for its speed and cost-efficiency. However, it has limitations, including a tendency to hallucinate (produce errors). It's suitable for simple tasks but may fall short in more complex scenarios.

OCR+LLM

OCR+LLM combines Optical Character Recognition (OCR) with Large Language Models (LLMs) to extract and analyze text. This approach offers a balance between accuracy and speed, making it ideal for more detailed data extraction tasks.

Testing Limits with Claude 3 Vision

Using an example invoice, we can define a protocol for our application:

invoice_number: "string"
invoice_date: "string"  # YYYY-MM-DD
due_date: "string"  # YYYY-MM-DD
seller_details:
  seller_name: "string"
  seller_address:
    street_number_and_name: "string"
    city_or_town: "string"
    country: "string"
buyer_details:
  buyer_name: "string"
  buyer_address:
    street_number_and_name: "string"
    city_or_town: "string"
    country: "string"
  buyer_email: "string"
  buyer_phone_number: "string"
products_services:
  - item_number: number
    description: "string"
    quantity: number
    unit_price: number
    total_price: number
sub_total: number
total: number

This pseudo-YAML format outlines the fields we want to extract from an invoice. Testing with Claude 3 Vision yielded response times of about 1 second, which is slower than desired.

OCR Transformers Designed for Speed

Notable OCR Tools

DocTR: Optimized for high-speed performance on both CPU and GPU, requiring only three lines of code to implement.
TrOCR: Pre-trained transformers supported by Microsoft, offering various models.
PaddleOCR: Known for its speed, capable of processing large volumes of images in real-time.
MMOCR: Another fast OCR tool.
Surya: Highly efficient and fast.

Performance testing showed that these OCR tools could achieve processing times as low as 20ms on a GPU.

Cloud-Native OCRs

Azure Form Recognizer: Best performance time around 3 seconds.
Amazon Textract: Processes documents in 3-4 seconds per page.
Google Cloud Vision API and Document AI: Highly efficient and similar to Azure and Amazon.
Abby Cloud OCR: Faster than the other alternatives and offers detailed page representations.

These cloud AI services used to be the go-to solutions but are now often replaced by LLMs due to cost and flexibility advantages.

(https://miro.medium.com/v2/resize:fit:720/format:webp/1*xlWNvAtaM0ObnSKYz6MWwA.png)

Implementing OCR with docTR

Setting Up the OCR API

Here’s a simple example using docTR:

from fastapi import FastAPI, File, UploadFile
from fastapi.responses import JSONResponse
from doctr.io import DocumentFile
from doctr.models import ocr_predictor
from PIL import Image
import io

app = FastAPI(title="OCR Service using docTR")

@app.post("/ocr/")
async def perform_ocr(file: UploadFile = File(...)):
    image_data = await file.read()
    doc = DocumentFile.from_images(image_data)
    model = ocr_predictor(pretrained=True)
    result = model(doc)

    extracted_texts = []
    for page in result.pages:
        for block in page.blocks:
            for line in block.lines:
                line_text = ' '.join([word.value for word in line.words])
                extracted_texts.append(line_text)

    return JSONResponse(content={"ExtractedText": extracted_texts})

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8001)

Docker Setup for the OCR API

# Use an official Python runtime as a parent image, suitable for TensorFlow
FROM tensorflow/tensorflow:latest

# Set the working directory in the container
WORKDIR /app

# Install system dependencies required for OpenCV and WeasyPrint
RUN apt-get update && apt-get install -y \
    libgl1-mesa-glx \
    libpango-1.0-0 \
    libpangocairo-1.0-0 \
    libgdk-pixbuf2.0-0 \
    libffi-dev \
    shared-mime-info

# Install FastAPI and Uvicorn
RUN pip install fastapi uvicorn python-multipart aiofiles Pillow

# Copy the local directory contents into the container
COPY . /app

# Install `doctr` with TensorFlow support
RUN pip install python-doctr[tf]

# Expose the port FastAPI will run on
EXPOSE 8001

# Command to run the FastAPI server on container start
CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8001", "--workers", "4"]

(https://miro.medium.com/v2/resize:fit:720/format:webp/0*OeNLrlTN_iNgWcvX)

Groq: The King of Speed

Groq's architecture provides exceptional performance and cost efficiency, boasting speeds three times faster at half the cost compared to traditional methods.

Using Groq with OCR

from groq import Groq

def send_request_to_groq(content: str) -> str:
    client = Groq(api_key=API_KEY_GROQ)
    completion = client.chat.completions.create(
        model="gemma-7b-it",
        messages=[
            {
                "role": "system",
                "content": "You are an API server that receives content from a document and returns a JSON with the defined protocol"
            },
            {
                "role": "user",
                "content": content
            }
        ],
        temperature=1,
        max_tokens=1024,
        top_p=1,
        stream=False,
        response_format={"type": "json_object"},
        stop=None,
    )

    return completion.choices[0].message.content

The response_format feature of Groq is particularly noteworthy, offering unique capabilities not found in other providers.

Final Implementation

Controller Code

@app.post("/extract_fast")
async def extract_text(file: UploadFile = File(...), extraction_contract: str = Form(...)):
    temp_file = tempfile.NamedTemporaryFile(delete=False)
    shutil.copyfileobj(file.file, temp_file)
    file_path = temp_file.name

    images = convert_pdf_to_images(file_path)

    extracted_text = extract_text_with_pytesseract(images)
    extracted_text = "\n new page --- \n".join(extracted_text)
    extracted_text = systemMessage + "\n####Content\n\n" + extracted_text
    extracted_text = extracted_text + "\n####Structure of the JSON output file\n\n" + extraction_contract
    extracted_text = extracted_text + "\n#### JSON Response\n\n" + jsonContentStarter

    start_time = time.time()
    content = send_request_to_groq(extracted_text)
    elapsed_time = time.time() - start_time
    print(f"send_request_to_groq took {elapsed_time} seconds")

    temp_file.close()
    content = remove_json_format(content)

    return json.loads(content)

Conclusion

For document scanning and data extraction, combining OCR and LLMs on GPUs with Groq provides superior speed and efficiency. This approach is especially beneficial for processing invoices and other documents captured via mobile devices.

NLP vs LLM: A Comprehensive Guide to Understanding Key Differences

ProspexAI — Tue, 21 May 2024 15:29:53 +0000

The NLP and LLM Technologies: A Comparative Guide

AI, Deep Learning, Machine Learning, Data Science

The NLP and LLM technologies are central to the analysis and generation of human language on a large scale. With their growing prevalence, distinguishing between LLM vs NLP becomes increasingly important.

NLP encompasses a suite of algorithms to understand, manipulate, and generate human language. Since its inception in the 1950s, NLP has evolved to analyze textual relationships. It uses part-of-speech tagging, named entity recognition, and sentiment analysis methods.

As exemplified by OpenAI’s ChatGPT, LLMs leverage deep learning to train on extensive text sets. Although they can mimic human-like text, their comprehension of language’s nuances is limited. Unlike NLP, which focuses on language analysis, LLMs primarily generate text.

I am pleased to present this guide, offering a concise yet comprehensive comparison of NLP and LLMs. We will explore the intricacies of these technologies, delve into their diverse applications, and examine their challenges.

Exploring the Distinctive Features of NLP

NLP facilitates machines’ understanding and engagement with human language in meaningful ways. It can be used for applications from spell-checking and auto-correction to chatbots and voice assistants.

NLP is about creating algorithms that enable the generation of human language. It bridges the gap between digital systems and human communication. This technology paves the way for enhanced data analysis and insight across industries.

Essential Technologies in NLP: From Parsing to Natural Language Generation

Natural Language Processing relies on various processes to enable computers to produce human language:

Parsing: This technique dissects a sentence into its grammatical elements. It simplifies the language structure for machines. It aids in recognizing parts of speech, sentence limits, and syntactic connections.
Semantic Analysis: Advances beyond simple word identification to grasp the meanings and relationships among words. It’s vital for interpreting the context of texts, idioms, and humor.
Speech Recognition: Transforms spoken words into written text, enabling audio transcription into a readable format.
Natural Language Generation: Contrary to speech recognition, NLG provides text that mimics human writing based on computer data. Applications include report writing, summarizing, and drafting messages.

Sentiment Analysis: Often used in monitoring social media and managing brand reputation. It evaluates the emotional tone of texts and analyzes customer feedback and market trends.
Machine Translation: Enables the conversion of text or speech from one language to another.
Named Entity Recognition: Detects and classifies essential information in texts, such as names of individuals, places, and organizations.
Text Classification and Categorization: Assigns labels to texts to facilitate sorting and management of vast data volumes. This is useful for organizing documents, emails, and online content.

NLP Applications: Enhancing Communication and Analysis

NLP’s applications are extensive, influencing various sectors by:

Text Analytics: Analyzes large text datasets to derive significant insights. It can be beneficial in market research and social media scrutiny.
Speech Recognition: Powers voice-activated devices and applications to comprehend and act on spoken instructions. This technology underpins virtual assistants and transcription tools.
Sentiment Analysis: Examines text for emotional undertones. It is crucial for monitoring public opinion and conducting market research.
Machine Translation: Breaks language barriers by translating texts or speech, facilitating international communication.
Content Recommendation: Utilizes NLP to tailor content suggestions based on user preferences and content characteristics. It also enhances experiences on streaming platforms and online shopping.

Challenges in NLP: Navigating Through Limitations

Despite progress, NLP encounters several hurdles that, if addressed, could refine its accuracy and integration into technology:

Contextual Understanding: Grasping language nuances (sarcasm or idiomatic expressions) remains challenging, leading to misunderstandings.
Language Diversity: The sheer number of languages and dialects, each with its grammar and syntax, poses a significant challenge.
Ambiguity in Language: The inherent ambiguity of human language can complicate the interpretation of NLP systems.
Data Quality and Availability: The performance of NLP systems is contingent on the quality and volume of training data. Biases in this data can result in skewed outcomes.
Computational Resources: The demand for substantial computational power for advanced apps limits their development and deployment.
Real-Time Processing: Achieving it for applications like simultaneous translation and customer service presents technological challenges.

Exploring the Capabilities of Large Language Models

Large Language Models offer a comprehensive approach to language tasks. They exhibit fluency and adaptability far beyond traditional Natural Language Processing systems. LLMs utilize a sophisticated tech stack for generative AI, enabling them to:

Generate coherent and contextually appropriate text.
Engage in meaningful conversations.
Provide answers to questions.
Create content that closely resembles human writing. ### Distinguishing Features of LLMs

LLMs are characterized by several key attributes that set them apart:

Extensive Training Data: LLMs are trained on vast datasets from diverse text sources. This approach allows them to produce various language styles and formats.
Adaptability: Language models can tackle various language tasks without needing specific training for each task. LLMs are highly versatile for automated content creation and advanced chatbot functionalities.
Contextual Understanding: LLMs generate text relevant to the context, maintaining coherence across text passages.
Continual Learning: LLMs can refine and expand their language capabilities with exposure to new data. They continue adapting to emerging slang and terminologies.

Core Technologies Behind LLMs

The effectiveness of Large Language Models is rooted in their foundational technologies:

Deep Learning: LLMs can autonomously learn and make decisions using neural networks with multiple layers.
Transformers Architecture: These models are designed for handling sequential data, enabling accurate predictions of the next word in a sentence.
Self-Attention Mechanisms: LLMs can produce more relevant responses by assessing the significance of each word.
Scalability: LLMs’ capabilities can be enhanced by training them with progressively larger datasets.

Practical Applications of LLMs

LLMs find application in a myriad of sectors, including:

Content Creation: From generating articles and reports to crafting poetry and fiction.
Customer Service: Providing efficient and accurate automated responses through chatbots. For instance, developing a ChatGPT plugin can enhance user experience in service-oriented sectors.
Language Translation: With a deep understanding of linguistic nuances, LLMs can facilitate global communication.
Educational Tools: Assisting in tutoring, creating personalized learning materials, grading assignments, and summarizing extensive texts.
Healthcare: Supporting patient interaction, information management, and the analysis of medical documents.

Challenges and Ethical Considerations of LLMs

Despite their advanced capabilities, LLMs face limitations and ethical dilemmas that need careful consideration:

Bias and Fairness: LLMs learn from pre-existing data, which raises concerns over fairness and representation.
Accuracy and Reliability: Outputs may sometimes reflect data patterns rather than factual correctness. It often leads to inaccuracies or nonsensical responses.
Lack of True Understanding: LLMs simulate comprehension but lack genuine understanding. This can result in errors or inappropriate outputs in complex scenarios.
Data Privacy: The handling of potentially sensitive data underscores the importance of stringent data governance.
Energy Consumption: Just like for NLP, the significant computational resources required pose environmental and resource allocation concerns.

Comparative Analysis: NLP vs LLM

NLP and LLM play pivotal roles in enhancing human-computer interaction through language. Although they share common objectives, there are several differences in their methodologies, capabilities, and application areas. Let’s focus on NLP vs LLM performance, scalability, accuracy, and their utility across various sectors.

Performance Metrics

NLP: Demonstrates high accuracy in specialized tasks such as syntax parsing and entity recognition.
LLM: Excels at generating human-like text and managing a wide spectrum of language tasks.

Scalability and Efficiency

NLP: More efficient at executing specific tasks with lower computational demands.
LLM: Highly scalable and adept at undertaking diverse tasks, albeit requiring greater computational resources.

Accuracy and Reliability

NLP: Exhibits high accuracy and reliability within specialized domains. May face challenges in tasks that require a rich understanding of context.
LLM: Achieves reliability in producing coherent language output. It may also generate inaccurate or biased content influenced by its training data.

Usability in Healthcare

(https://miro.medium.com/v2/resize:fit:720/format:webp/1*3rRxBHyuKy_M5Nqh38l2-Q.jpeg)

NLP: Utilized for processing medical records, extracting pertinent patient information, and enabling predictive diagnostics.
LLM: Facilitates patient interaction, disseminates information, and provides general medical advice.

Usability in Finance

NLP: Applied in sentiment analysis, risk assessment, and enhancing customer service. It is particularly adept at processing financial language through generative AI in banking.
LLM: Useful for creating financial reports, conducting market analyses, and automating customer service interactions.

Usability in E-commerce

NLP: Improves customer experience through chatbots, personalized recommendations, and analysis of customer feedback.
LLM: Aids in generating content, managing large-scale customer interactions, and automating aspects of digital marketing.

Enhancing AI through NLP and LLM Integration

Fusing NLP and LLMs is a significant leap forward in developing advanced language processing systems. This collaboration combines NLP’s precise capabilities with LLM’s expansive contextual knowledge. It can also significantly improve AI applications’ efficiency and effectiveness across industries.

Synergistic Benefits of NLP and Large Language Model Integration

Integrating NLP with LLM technologies offers several key advantages:

Enhanced Accuracy and Contextual Understanding: Merging NLP’s targeted processing strengths with LLM’s wide-ranging contextual comprehension improves accuracy and relevance in executing language tasks.
Resource Optimization: The efficiency of NLP in specific task

Large Language Models (LLMs) in Scoring Tasks and Decision Making

ProspexAI — Tue, 21 May 2024 05:17:53 +0000

Large language models (LLMs) open new possibilities for automating complex scoring tasks and enhancing decision-making processes.

From essay evaluation to credit risk assessment, LLMs are demonstrating remarkable capabilities in understanding and analyzing textual data.

However, to fully harness the potential of LLMs, it is crucial to explore how they can be combined with other forms of AI, such as rule-based systems and predictive models, and how they can contribute to explainable scoring and decision making.

In this article, we explore the methods employed by three recent studies that showcase the power of LLMs in scoring tasks.

We examine how these methods can be integrated with other AI approaches and discuss the implications for improving the interpretability and transparency of scoring systems.

Current Pain Points of Human Scoring

Historically, scoring tasks, such as essay grading or credit risk assessment, have relied heavily on human judgment. While human expertise is invaluable, it also comes with several inherent limitations and challenges:

Subjectivity and Inconsistency: Human scorers may have different interpretations, biases, and personal standards, leading to inconsistencies in scoring across individuals and over time. This subjectivity can result in unfair or unreliable evaluations.
Time and Resource Intensive: Manual scoring processes are often time-consuming and labor-intensive, requiring significant human resources. This can limit the scalability and efficiency of scoring tasks, particularly in large-scale assessments or high-volume decision-making scenarios.
Fatigue and Cognitive Limitations: Human scorers are prone to fatigue and cognitive limitations, especially when dealing with a high volume of evaluations. As mental exhaustion sets in, the quality and consistency of scoring may deteriorate, introducing errors and variability.
Lack of Granular Feedback: Human scorers may struggle to provide detailed, actionable feedback on specific aspects of performance. Generating granular, trait-specific insights can be mentally taxing and time-consuming, limiting the depth and breadth of feedback provided to individuals being evaluated.

Advantages of AI in Scoring

The integration of AI, particularly LLMs, in scoring tasks offers several compelling advantages that can address the limitations of human scoring:

Consistency and Standardization: LLMs can be trained on large datasets and fine-tuned to adhere to predefined scoring criteria and standards. This ensures a high level of consistency in scoring across different inputs and over time, reducing the variability and subjectivity inherent in human evaluations.
Efficiency and Scalability: AI-powered scoring systems can process vast amounts of data quickly and efficiently, enabling the evaluation of a large number of inputs in a fraction of the time required by human scorers. This scalability is particularly valuable in scenarios where high-volume scoring is necessary, such as in massive open online courses (MOOCs) or large-scale financial assessments.
Objectivity and Fairness: LLMs can be designed to minimize biases and ensure a more objective evaluation process. By relying on data-driven models and well-defined scoring criteria, AI systems can reduce the influence of human subjectivity and personal biases, promoting fairness and equal treatment across all individuals being evaluated.
Granular and Actionable Feedback: LLMs have the capacity to provide detailed, trait-specific feedback on various aspects of performance. By leveraging their ability to analyze and interpret textual data at a granular level, AI systems can generate targeted insights and recommendations, enabling individuals to understand their strengths and areas for improvement more effectively.

Grounding and Calibrating Human Decisions

While AI-powered scoring offers numerous benefits, it is essential to recognize the importance of human oversight and collaboration. LLMs can serve as a powerful tool to ground and calibrate human decision-making processes, reducing noise and inconsistencies:

Augmenting Human Judgment: LLMs can provide initial scores, recommendations, and insights to human experts, serving as a starting point for their evaluation. Human scorers can then review and validate the AI-generated scores, making adjustments based on their domain knowledge and contextual understanding. This human-AI collaboration can lead to more accurate and well-rounded evaluations.
Establishing Benchmarks and Norms: AI scoring systems can help establish benchmarks and norms based on large-scale data analysis. By processing vast amounts of historical data, LLMs can identify patterns, trends, and standards that can serve as reference points for human decision-makers. This can help calibrate human judgments and ensure greater consistency across evaluations.
Identifying Outliers and Anomalies: LLMs can flag unusual or anomalous inputs that deviate from established patterns or norms. By drawing attention to these outliers, AI systems can prompt human experts to carefully review and investigate these cases, reducing the risk of overlooking important exceptions or making erroneous decisions.
Continuous Learning and Adaptation: AI scoring systems can learn and adapt over time based on feedback from human experts. As human scorers provide their insights and make adjustments to AI-generated scores, the models can be fine-tuned and improved iteratively. This continuous learning process allows the AI system to better align with human expertise and evolving domain knowledge.

Integrating Different Forms of AI

To fully harness the potential of LLMs in scoring and decision-making, it is crucial to explore how they can be combined with other forms of AI, such as rule-based systems and predictive models:

Rule-Based Systems: LLMs can be integrated with rule-based systems that encapsulate domain-specific knowledge and guidelines. These rules can be used to constrain the scoring process, ensuring compliance with established standards and regulations. For example, in credit risk assessment, rule-based systems can enforce certain eligibility criteria or risk thresholds, while LLMs can handle the qualitative analysis of unstructured data.
Predictive Models: LLMs can be combined with predictive models that leverage structured data and statistical techniques. While LLMs excel at processing and interpreting textual information, predictive models can handle numerical and categorical data effectively. By combining the outputs of LLMs with predictive models, a more comprehensive and accurate assessment can be achieved. For instance, in essay scoring, LLMs can evaluate the content and coherence of the essay, while predictive models can analyze grammatical features and writing style.
Explainable AI: To enhance the interpretability and transparency of scoring systems, LLMs can be designed to generate human-readable explanations and justifications for their scores and recommendations. By providing clear and understandable rationales, LLMs can help build trust and accountability in AI-assisted decision-making processes. This explainability is crucial for domains where decisions have significant consequences, such as in financial lending or educational assessments.

Multi-Trait Specialization for Zero-shot Essay Scoring

Luo et al. [1] propose the Multi-Trait Specialization (MTS) framework for zero-shot essay scoring using LLMs. The MTS approach leverages ChatGPT to decompose writing proficiency into distinct traits, such as organization, coherence, and language use. By generating scoring criteria for each trait and extracting scores through conversational prompts, MTS enables a nuanced evaluation of essays without the need for extensive labeled training data.

The MTS framework highlights the potential for LLMs to provide fine-grained, trait-specific feedback on writing quality. This level of granularity can enhance the explainability of essay scores, as it allows for a clear mapping between the scores and the specific aspects of writing being evaluated. Moreover, the conversational nature of the scoring process in MTS opens up possibilities for integrating LLMs with rule-based systems or expert knowledge.

For example, domain experts could define scoring rubrics or provide examples of high-quality writing for each trait. These rules and examples could be incorporated into the prompts used to guide the LLM’s scoring process. By combining the linguistic understanding of LLMs with expert-defined criteria, the scoring system can produce more accurate and interpretable results.

Furthermore, the trait scores generated by MTS could serve as inputs to predictive models or decision support systems. For instance, trait scores could be weighted and combined based on their importance for a specific writing task or domain. This integration of LLM-based scoring with predictive modeling can enable more nuanced and context-aware assessment of writing quality.

Augmenting Essay Scoring with Large Language Models

Gneiting et al. [2] explore the potential of LLMs to augment human essay graders and elevate the scoring landscape. Their study demonstrates how LLM-generated scores and feedback can improve the accuracy and consistency of both novice and expert graders.

The augmentation approach highlights the potential for LLMs to serve as intelligent assistants in the scoring process. By providing graders with LLM-generated insights and recommendations, the system can help them make more informed and consistent evaluations. This human-AI collaboration can lead to improved scoring quality while still maintaining the interpretability and accountability of human judgment.

Moreover, the LLM-based augmentation can be extended to incorporate rule-based constraints or domain-specific guidelines. For example, the LLM could be fine-tuned on a dataset of essays annotated with expert-defined scores and feedback. This fine-tuning process can align the LLM’s scoring behavior with established grading rubrics or standards.

The augmentation approach also enables explainability through the generation of natural language feedback. The LLM can provide detailed explanations for its scoring decisions, highlighting the strengths and weaknesses of an essay across different dimensions. This interpretable feedback can help students understand their performance and guide their improvement efforts.

Generalist Credit Scoring with Large Language Models

Feng et al. [3] introduce the Credit and Risk Assessment Language Model (CALM), a specialized LLM for credit scoring and risk assessment tasks. CALM is built by fine-tuning an existing LLM on a custom instruction dataset, allowing it to handle tabular financial data and generate scores based on table-based and description-based prompts.

The CALM approach showcases the adaptability of LLMs to different data formats and scoring contexts. By fine-tuning on domain-specific instructions and examples, CALM can capture the nuances and complexities of credit and risk assessment. This adaptability enables the integration of LLMs with existing expert systems and predictive models in the

financial domain.

For instance, CALM could be combined with rule-based credit scoring models that encode domain knowledge and regulatory requirements. The LLM’s ability to process and score unstructured data, such as client descriptions or qualitative risk factors, can complement the structured data handled by traditional models. This hybrid approach can lead to more comprehensive and accurate risk assessments.

Moreover, CALM’s scoring process can be made more explainable by generating natural language justifications for its decisions. The LLM can highlight the key factors and characteristics that influenced the credit or risk score, providing interpretable insights for both clients and decision-makers. This explainability is crucial for building trust and accountability in financial assessment systems.

Key Takeaways and Future Directions

The methods employed in the three studies demonstrate the versatility and potential of LLMs for scoring tasks across different domains. From zero-shot essay scoring to credit risk assessment, LLMs can provide accurate, nuanced, and interpretable evaluations by leveraging their linguistic understanding and ability to process unstructured data.

However, to fully realize the potential of LLMs in scoring and decision making, it is essential to explore their integration with other forms of AI, such as rule-based systems and predictive models. By combining the strengths of LLMs with expert knowledge, domain-specific constraints, and structured data, we can create more robust, accurate, and explainable scoring systems.

Moreover, the explainability of LLM-based scoring can be enhanced through the generation of natural language feedback and justifications. By providing clear and interpretable explanations for scoring decisions, LLMs can promote transparency and build trust in automated scoring systems.

Looking forward, further research is needed to investigate the optimal ways of integrating LLMs with other AI approaches and incorporating expert knowledge into the scoring process. Additionally, developing techniques for ensuring the fairness, accountability, and robustness of LLM-based scoring systems will be crucial for their responsible deployment in real-world contexts.

Conclusion

Large language models have demonstrated remarkable capabilities in automating scoring tasks and enhancing decision-making processes. The methods explored in the three studies discussed in this article highlight the potential of LLMs to provide accurate, nuanced, and interpretable evaluations across domains such as essay scoring and credit risk assessment.

By integrating LLMs with rule-based systems, expert knowledge, and predictive models, we can create more comprehensive and explainable scoring systems. The generation of natural language feedback and justifications by LLMs can further enhance the transparency and interpretability of scoring decisions.

As we continue to explore the potential of LLMs in scoring and decision making, it is crucial to prioritize responsible development and deployment practices. By addressing challenges related to fairness, accountability, and robustness, we can harness the power of LLMs to create trustworthy and effective scoring systems that augment human judgment and support better decision making.

References

[1] Luo, Y., Zhang, X., & Zou, Y. (2023). Prompting Large Language Models for Zero-shot Essay Scoring via Multi-trait Specialization. arXiv preprint arXiv:2404.04941.

[2] Gneiting, F. G., Kobylnik, K., & Wolska, M. (2023). From Automation to Augmentation: Large Language Models Elevating Essay Scoring Landscape. arXiv preprint arXiv:2401.06431.

[3] Feng, C. (2023). Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Models. arXiv preprint arXiv:2310.00566.

Connecting a POS Printer to Windows OS Using Django: A Comprehensive Guide

ProspexAI — Mon, 20 May 2024 16:38:22 +0000

In this post, we’ll walk through the process of connecting a thermal printer or any external devices like a barcode reader, passport scanner to a Windows OS using Django. I am using the Oscar POS purchased from Amazon. We’ll cover setting up a virtual environment, creating a Django project, and building an interface for dynamic printing using win32print.

The project code can be found here: GitHub Repository

Before starting, you should install the drivers that come with this printer. Other models do not require any driver to build a program to use them by using the pyusb library. More expensive devices have product_id and vendor_id.

At the end, I will provide a brief detail about using the pyusb library and connecting devices to it.

Prerequisites

Before we begin, ensure you have the following:

Python installed on your system
Django installed in your Python environment
A POS thermal printer for Invoices and Queue Tickets with the necessary drivers installed on your Windows OS

Step 1: Setting Up the Virtual Environment

First, we’ll create a virtual environment for our project. Open your command prompt and run the following commands:

# Install virtualenv if you haven't already
pip install virtualenv

# Create a virtual environment named 'printer_env'
virtualenv printer_env

# Activate the virtual environment
# On Windows
printer_env\Scripts\activate

# On macOS/Linux
source printer_env/bin/activate

Step 2: Installing Required Packages

Within the virtual environment, install Django and other required packages:

pip install django pywin32

Step 3: Setting Up the Django Project

Create a new Django project and app:

# Create a Django project named 'printer_project'
django-admin startproject printer_project

# Navigate to the project directory
cd printer_project

# Create a Django app named 'pos'
python manage.py startapp pos

Add the new app to your INSTALLED_APPS in printer_project/settings.py:

INSTALLED_APPS = [
    'django.contrib.admin',
    'django.contrib.auth',
    'django.contrib.contenttypes',
    'django.contrib.sessions',
    'django.contrib.messages',
    'django.contrib.staticfiles',
    'pos',
]

TEMPLATES = [
    {
        'BACKEND': 'django.template.backends.django.DjangoTemplates',
        'DIRS': [BASE_DIR / "templates"],  # Add this line
        'APP_DIRS': True,
        'OPTIONS': {
            'context_processors': [
                'django.template.context_processors.debug',
                'django.template.context_processors.request',
                'django.contrib.auth.context_processors.auth',
                'django.contrib.messages.context_processors.messages',
            ],
        },
    },
]

Step 4: Setting Up Models

Define the models for saving sequence numbers and printer preferences. In pos/models.py, add:

from django.db import models

class Sequence(models.Model):
    number = models.IntegerField(default=0)

class PrinterPreference(models.Model):
    device_name = models.CharField(max_length=255)
    device_id = models.CharField(max_length=255, unique=True)
    is_default = models.BooleanField(default=False)

    def __str__(self):
        return f"{self.device_name} - Default: {self.is_default}"

Run the migrations to create these models in the database:

python manage.py makemigrations pos
python manage.py migrate

Step 5: Setting Up the Admin Interface

from django.contrib import admin
from .models import PrinterPreference, Sequence

admin.site.register(PrinterPreference)
admin.site.register(Sequence)

Step 6: Creating Views and Templates

Create views for handling sequence numbers and listing USB devices in pos/views.py:

from django.http import JsonResponse, HttpResponse
from django.shortcuts import redirect, render
from django.views.decorators.http import require_http_methods
from django.views.decorators.csrf import csrf_exempt
from .models import Sequence, PrinterPreference
import win32print
import win32com.client
import pythoncom
import logging

def home(request):
    return render(request, "sequence.html")

@csrf_exempt  # For demonstration purposes only, consider CSRF in production
@require_http_methods(["POST"])
def sequence_view(request):
    number_to_print = request.POST.get('number_to_print')
    if 'reset' in request.POST:
        Sequence.objects.update(number=0)
        return JsonResponse({'number': 0, 'status': 'Sequence reset'})

    sequence = Sequence.objects.get(id=1)
    if number_to_print:
        try:
            number_to_print = int(number_to_print)
            sequence.number = number_to_print
            sequence.save()
            response = print_sequence(sequence.number)
            return JsonResponse({'number': number_to_print, 'status': 'Sequence Setting Success', **response})
        except ValueError:
            return JsonResponse({'status': 'Invalid number'}, status=400)
    else:
        sequence.number += 1
        sequence.save()
        response = print_sequence(sequence.number)
    return JsonResponse({'number': sequence.number, **response})

def print_sequence(number):
    data = b'\x1b\x40'
    data += b'\x1b\x37\x06\x15\x10'
    data += b'\x1b\x21\x30'
    data += 'Your Company Name\n'.encode('utf-8')
    data += b'\x1b\x21\x00'
    data += 'Registration\n'.encode('utf-8')
    data += b'\x1b\x21\x10'
    data += 'INFO : \n'.encode('utf-8')
    data += b'--------------------------------\n'
    data += b'\x1b\x21\x30'
    data += 'TOKEN NO:{}\n'.format(number).encode('utf-8')
    data += b'\x1d\x56\x42\x00'
    data += b'\x1B\x42\x00\x00'
    try:
        default_printer = PrinterPreference.objects.filter(is_default=True).first()
        if not default_printer:
            return {"status": "error", "message": "No default printer selected."}

        printer_name = default_printer.device_name
        printer_handle = win32print.OpenPrinter(printer_name)
        try:
            job_info = ("Token Print", None, "RAW")
            job_id = win32print.StartDocPrinter(printer_handle, 1, job_info)
            win32print.StartPagePrinter(printer_handle)
            win32print.WritePrinter(printer_handle, data)
            win32print.EndPagePrinter(printer_handle)
            win32print.EndDocPrinter(printer_handle)
        finally:
            win32print.ClosePrinter(printer_handle)
        return {"status": "success", "message": f"Printed sequence number {number}"}
    except Exception as e:
        return {"status": "error", "message": f"An error occurred: {e}"}

logging.basicConfig(level=logging.DEBUG)

def list_devices():
    try:
        pythoncom.CoInitialize()
        wmi = win32com.client.Dispatch("WbemScripting.SWbemLocator")
        wmi_service = wmi.ConnectServer(".", "root\cimv2")
        devices = wmi_service.ExecQuery("SELECT * FROM Win32_PnPEntity")
        device_list = [{'device_name': device.Name} for device in devices]
        return device_list
    except pythoncom.com_error as e:
        return [{'error': 'Failed to list devices due to a WMI error.'}]
    finally:
        pythoncom.CoUninitialize()

@csrf_exempt  # For demonstration purposes only, consider CSRF in production
@require_http_methods(["GET", "POST"])
def list_usb_devices(request):
    if request.method == "POST":
        default_printer_name = request.POST.get('default_printer')
        if default_printer_name:
            PrinterPreference.objects.all().update(is_default=False)
            our_printer, created = PrinterPreference.objects.get_or_create(device_name=default_printer_name)
            if our_printer:
                our_printer.is_default = True
                our_printer.save()
            return redirect('pos:home')
    device_list = list_devices()
    if not device_list:
        device_list = [{'error': "No USB devices found."}]
    return render(request, 'list_usb_devices.html', {'devices': device_list})

Create the templates sequence.html and list_usb_devices.html in pos/templates/:

sequence.html:

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>Sequence Number</title>
    <link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.5.2/css/bootstrap.min.css">
    <script src="https://code.jquery.com/jquery-3.6.0.min.js"></script>
    <script src="https://stackpath.bootstrapcdn.com/bootstrap/4.5.2/js/bootstrap.bundle.min.js"></script>
    <style>
        .btn-pos { background-color: orange; color: white; padding: 20px 40px; font-size: 1.5rem; margin: 10px 0; }
        .btn-pos:hover { background-color: darkorange; }
        .input-pos { font-size: 1.5rem; padding: 10px

; }
        .logo-corner { position: absolute; top: 10px; left: 10px; }
        .container { max-width: 800px; }
    </style>
    <script>
        $(document).ready(function() {
            $('form').on('submit', function(e) {
                e.preventDefault();
                var numberToPrint = $('#number_to_print').val();
                var reset = $('button[name="reset"]').is(':focus');
                var postData = {'csrfmiddlewaretoken': '{{ csrf_token }}'};
                if (reset) {
                    postData['reset'] = true;
                } else {
                    postData['number_to_print'] = numberToPrint;
                }
                $.ajax({
                    type: 'POST',
                    url: "{% url 'pos:sequence_view' %}",
                    data: postData,
                    success: function(response) {
                        if (response.status === "success" || response.status === "Sequence reset") {
                            $('#number').text(response.number);
                            if (reset) {
                                $('form')[0].reset();
                            }
                        } else if (response.status === "error") {
                            alert("Error: " + response.message);
                        }
                    },
                    error: function(error) {
                        console.log(error);
                        alert('An error occurred.');
                    }
                });
            });
        });
    </script>
</head>
<body>
    <div class="logo-corner">
        <img src="https://eu-central-1.linodeobjects.com/roadslink/images/file.png" alt="Logo" height="60">
    </div>
    <div class="container mt-5">
        <h1 class="mb-3">Sequence Number: <span id="number">{{ number }}</span></h1>
        <form method="post" class="text-center">
            {% csrf_token %}
            <div class="form-group">
                <input type="text" class="form-control input-pos" id="number_to_print" name="number_to_print" placeholder="Enter/Set a number to print">
            </div>
            <button type="submit" name="print_next" class="btn btn-pos btn-block">Print Next Number</button>
            <button type="submit" name="reset" class="btn btn-pos btn-block">Reset</button>
        </form>
        <a href="{% url 'pos:list_usb_devices' %}">Select your device if not running</a>
    </div>
</body>
</html>

list_usb_devices.html:

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>List USB Devices</title>
</head>
<body>
    <h1>Select Default Printer</h1>
    {% if error %}
        <p style="color: red;">Error: {{ error }}</p>
    {% endif %}
    <form method="post">
        {% csrf_token %}
        <ul>
            {% for device in devices %}
                <li>
                    <label>
                        <input type="radio" name="default_printer" value="{{ device.device_name }}">
                        {{ device.device_name }}
                    </label>
                </li>
            {% endfor %}
        </ul>
        <button type="submit">Set as Default Printer</button>
    </form>
</body>
</html>

Step 7: Setting Up URL Routes

Define URL routes for your views in pos/urls.py:

from django.urls import path
from .views import home, list_usb_devices, sequence_view

app_name = 'pos'
urlpatterns = [
    path('', home, name='home'),
    path('sequence_view', sequence_view, name='sequence_view'),
    path('list_usb_devices/', list_usb_devices, name='list_usb_devices'),
]

Include the pos app URLs in the main project URL configuration in printer_project/urls.py:

from django.contrib import admin
from django.urls import include, path

urlpatterns = [
    path('admin/', admin.site.urls),
    path('', include('pos.urls')),
]

Step 8: Running the Project

Start the Django development server:

python manage.py runserver

Navigate to http://127.0.0.1:8000/ in your web browser. You should see the sequence number interface where you can print the next number or reset the sequence. Use the link http://127.0.0.1:8000/list_usb_devices to select and set your default printer.

Testing with pyusb

Using this method is more product support but requires verified product factory who register his devices in all operating systems like Windows or Linux.

pip install pyusb
import usb.core
import usb.util

def list_usb_devices():
    # Find all connected USB devices
    devices = usb.core.find(find_all=True)

    # Iterate over each device and print information
    for device in devices:
        print(f"Device: {device}")
        print(f"  - idVendor: {hex(device.idVendor)}")
        print(f"  - idProduct: {hex(device.idProduct)}")
        print(f"  - Manufacturer: {usb.util.get_string(device, device.iManufacturer)}")
        print(f"  - Product: {usb.util.get_string(device, device.iProduct)}")
        print(f"  - Serial Number: {usb.util.get_string(device, device.iSerialNumber)}")
        print()

if __name__ == "__main__":
    list_usb_devices()

Result:

Device: DEVICE ID 1d6b:0003 on Bus 004 Address 001
 =================
 bLength                :   0x12 (18 bytes)
 bDescriptorType        :    0x1 Device
 bcdUSB                 :  0x310 USB 3.1
 bDeviceClass           :    0x9 Hub
 bDeviceSubClass        :    0x0
 bDeviceProtocol        :    0x3
 bMaxPacketSize0        :    0x9 (9 bytes)
 idVendor               : 0x1d6b
 idProduct              : 0x0003
 bcdDevice              :  0x602 Device 6.02
 iManufacturer          :    0x3 Error Accessing String
 iProduct               :    0x2 Error Accessing String
 iSerialNumber          :    0x1 Error Accessing String
 bNumConfigurations     :    0x1
  CONFIGURATION 1: 0 mA
  ====================================
   bLength              :    0x9 (9 bytes)
   bDescriptorType      :    0x2 Configuration
   wTotalLength         :   0x1f (31 bytes)
   bNumInterfaces       :    0x1
   bConfigurationValue  :    0x1
   iConfiguration       :    0x0 
   bmAttributes         :   0xe0 Self Powered, Remote Wakeup
   bMaxPower            :    0x0 (0 mA)
    INTERFACE 0: Hub
    =======================================
     bLength            :    0x9 (9 bytes)
     bDescriptorType    :    0x4 Interface
     bInterfaceNumber   :    0x0
     bAlternateSetting  :    0x0
     bNumEndpoints      :    0x1
     bInterfaceClass    :    0x9 Hub
     bInterfaceSubClass :    0x0
     bInterfaceProtocol :    0x0
     iInterface         :    0x0 
      ENDPOINT 0x81: Interrupt IN
      ==========================
       bLength          :    0x7 (7 bytes)
       bDescriptorType  :    0x5 Endpoint
       bEndpointAddress :   0x81 IN
       bmAttributes     :    0x3 Interrupt
       wMaxPacketSize   :    0x4 (4 bytes)
       bInterval        :    0xc

- idVendor: 0x1d6b
- idProduct: 0x3

Part 2: I will make another article about pyusb with a passport scanner.

Bonus Section

I have created Docker composing and a Dockerfile for building an image that will work in any Operating Systems.

Docker Installation

To run this project in a Docker container, use the following Dockerfile and docker-compose.yml.

Dockerfile

FROM python:3.11

# Install dependencies
RUN apt-get update && apt-get install -y \
    usbutils \
    libusb-1.0-0-dev \
    && rm -rf /var/lib/apt/lists/*

# Set work directory
WORKDIR /app

# Install Python dependencies
COPY requirements.txt requirements.txt
RUN pip install -r requirements.txt

# Copy project files
COPY . .

# Set the environment variable for Django settings
ENV DJANGO_SETTINGS_MODULE=printer.settings

# Expose port 8000
EXPOSE 8000

# Run the Django server
CMD ["python", "manage.py", "runserver", "0.0.0.0:8000"]

docker-compose.yml

version: '3.10'

services:
  web:
    build: .
    command: sh -c "python manage.py runserver 0.0.0.0:8000"
    volumes:
      - .:/app
    ports:
      - "8000:8000"
    environment:


 - DJANGO_SETTINGS_MODULE=printer.settings
    devices:
      - "/dev/bus/usb:/dev/bus/usb"
    privileged: true

Conclusion

In this post, we covered setting up a virtual environment, creating a Django project, and building an interface for dynamic printing using win32print. By following these steps, you can connect and use a thermal printer with a Django web application on Windows OS. This setup allows for flexible and dynamic printing solutions tailored to your specific needs.

Feel free to customize the code and templates to suit your project requirements. If you have any questions or run into issues, leave a comment below, and I’ll be happy to help.

Happy printing and device integration!!

DEV Community: ProspexAI

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AI && Deep Learning

Table of Contents

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Process Video and Count Objects in Regions:

Community Support

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