semvec/pypi-packages huggingface dataset:
filmfarm 1.0.1
A toolkit to manage movie collections.
Requires Python: <4.0,>=3.10

# filmfarm

A toolkit to manage movie collections. It provides utilities to scrape metadata/posters and organize
movies into directories based on their metadata.

It's useful if you have a film collection in a directory where each film is in its own subdirectory
named after the [IMDB ID] of the film. This is easier to demonstrate than explain; in the demo
the directories are empty, but in real life they could contain video files from your DVD backups etc.

[![filmfarm demo](https://asciinema.org/a/osw3RpP45H6jlUc9YNYZ32om0.svg)](https://asciinema.org/a/osw3RpP45H6jlUc9YNYZ32om0)

[IMDB ID]: https://developer.imdb.com/documentation/key-concepts

## Installation

filmfarm is best installed using [pipx](https://pipx.pypa.io/stable/); `pipx install filmfarm`.

## Usage

filmfarm provides a command-line interface (CLI) using [Typer](https://typer.tiangolo.com/).
See `filmfarm --help` for details.

## Prerequisites

filmfarm requires API keys for [OMDB] and [TMDB], as of this writing these keys are freely
available. You must set `OMDB_API_KEY` and `TMDB_API_KEY` in your environment, or create a [dotenv]
file in the project's directory if you're running from source.

[OMDB]: https://www.omdbapi.com/
[TMDB]: https://www.themoviedb.org/
[dotenv]: https://stackoverflow.com/q/68267862

## Development

filmfarm is packaged with [Poetry](https://python-poetry.org/). Once Poetry is installed and you've
cloned the filmfarm source repo, run `poetry install` to install all dependencies. You can then run
`poetry run filmfarm` or use `poetry shell` (see Poetry docs for details).

To run the tests, use `pytest`. There's also `integration.sh`, a little shell script which sort of
does integration tests (it actually uses API keys to download and link some data).

## Attribution
This package uses the [TMDB](https://www.themoviedb.org/) API but is not endorsed or certified by
TMDB. It also uses the [OMDB](https://www.omdbapi.com/) API. A tiny sample of API results is
included as testdata.
OptiVisionNet 0.3.0
Usage example: from OptiVisionNet.model import CNN_BiLSTM_MLP
Requires Python: >=3.6

# OptiVisionNet

OptiVisionNet is a hybrid deep learning model designed for image classification tasks. It combines Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Multi-Layer Perceptrons (MLP) to achieve high performance on various image classification datasets.

## Installation

To install OptiVisionNet, use pip:


pip install OptiVisionNet


## Usage
from OptiVisionNet.model import CNN_BiLSTM_MLP
from OptiVisionNet.utils import train_cnn_bilstm, evaluate_model
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
import torch.optim as optim
import torch.nn as nn

# Data Preparation
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

train_data = datasets.CIFAR10(root="data", train=True, download=True, transform=transform)
test_data = datasets.CIFAR10(root="data", train=False, download=True, transform=transform)

train_loader = DataLoader(train_data, batch_size=64, shuffle=True)
test_loader = DataLoader(test_data, batch_size=64, shuffle=False)

# Initialize Model
model = CNN_BiLSTM_MLP(input_channels=3, lstm_hidden_size=128, lstm_layers=2, output_size=10)

# Train CNN + BiLSTM
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
train_cnn_bilstm(model, train_loader, criterion, optimizer, epochs=5)

# Evaluate the Model
accuracy, f1 = evaluate_model(model, test_loader)
print(f"Test Accuracy: {accuracy:.2f}%")
print(f"Test F1 Score: {f1:.2f}")