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1. Image-Recognition-App-using-FastAPI-and-PyTorch Image-Recognition-App-using-FastAPI-and-PyTorch Public

   This is an image recognition application based on the FastAPI framework and PyTorch which uses pretrained DenseNet 121 model to detect the image.

   Jupyter Notebook 22 5

2. kafka-in-docker-and-python-fastapi kafka-in-docker-and-python-fastapi Public

   Kafka With Python

   Jupyter Notebook 24 7

3. chat-with-phi-3-vision chat-with-phi-3-vision Public

   Chat with Phi 3.5/3 Vision LLMs. Phi-3.5-vision is a lightweight, state-of-the-art open multimodal model built upon datasets which include - synthetic data and filtered publicly available websites …

   Jupyter Notebook 13 6

4. receipt-ocr receipt-ocr Public

   Efficient OCR engine for receipt image processing using Python, FastAPI, and Tesseract

   Python 18 6

5. unet unet Public

   PyTorchUNet : A PyTorch Implementation of UNet Architecture for Semantic Segmentation of Images from scratch.

   Python 5

6. This Python code defines a function ... This Python code defines a function concurrent_task_executor that allows executing tasks concurrently on a list of data objects using ThreadPoolExecutor from the concurrent.futures module. It includes a progress bar using tqdm to track the completion of tasks. The function is useful for processing data in parallel, improving efficiency when dealing with large datasets.

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   import concurrent.futures

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   from typing import Any, Callable, List

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   from tqdm import tqdm

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   def concurrent_task_executor(task: Callable[[Any], None], data_list: List[Any], max_workers: int = 32, description: str = None) -> None: