-
- Scan to Pay
-
- 
-
-
-
- 
- 
- 
-
-
-
-## Prerequisites
-This tutorial is not the only way to fine-tune Flux.1-dev LoRA, you can train Flux.1-dev LoRA on Google colab, or using online services. But because this tutorial is using local GPU, you need to prepare the following to run it smoothly:
-* You are a developer
-* OS: Ubuntu Linux
-* Nvidia GPU, at least 12GB VRAM
-* Python 3.10 or 3.11
-
-I did not write the code for this training. Instead, I used [SimpleTuner](https://github.com/bghira/SimpleTuner/blob/main/documentation/quickstart/FLUX.md) to fine-tune Flux.1-dev LoRA. You can refer directly to the SimpleTuner documentation for instructions on fine-tuning Flux.1-dev LoRA.
-
-## Environment Setup
-
-```
-$ git clone --branch=release https://github.com/bghira/SimpleTuner.git
-
-$ cd SimpleTuner
-
-
-$ python -m venv .venv
-
-$ source .venv/bin/activate
-
-$ pip install -U poetry pip
-
-$ poetry config virtualenvs.create false
-
-$ poetry install
-```
-
-## Dataset preparation
-
-### The dataset
-
-To create your own Flux model, you need to prepare some of your images, just 5 images is enough, but 20 images is better. The quality of the image contributes greatly to the effectiveness of the model, consider the following factors:
-* WebP, JPG, and PNG formats are all supported
-* Use 1024x1024 or higher resolution if possible.
-* The photo shows a clear face, without accessories, eg glasses.
-* Photos share different angles of the face.
-* The photo collection should be diverse (e.g. angles, outfits, ...)
-* Photos should be taken within the last 6 months.
-
-
-Credits: [Replicate](https://replicate.com/blog/fine-tune-flux-with-faces)
-
-### The trigger word
-
-When fine-tuning Flux with your images, it’s essential to choose a unique trigger word that represents your face. This trigger word will act as a key for generating images of your likeness. Ideally, the trigger word should be distinct and specific to your face, avoiding any overlap with common objects or concepts to ensure accurate results.
-
-Here are the tips to choose a good trigger word:
-* The trigger word should not be a word found in any language dictionary. For example, 'face' or 'flower' is not a good trigger word.
-* Make it short, because it will take some space in the prompt.
-* Example: hieunv3, behitek are good trigger words.
-
-**Reason**: The words in dictionary can be easily recognized by the model, and the model will not be able to generate images of your likeness.
-
-### Generate captions
-
-For better quality, you should generate a caption for each image. Here, I will use the [Microsoft Florence 2](https://huggingface.co/multimodalart/Florence-2-large-no-flash-attn) model to generate captions for the images. You can also use ChatGPT, llava or llama3.2 to generate captions.
-
-After captions are generated, add your trigger word to the beginning of the captions. For example (using behitek as the trigger word):
-
-
-
-Do organize your images and captions in a folder, and put them in a folder with structure like this:
-
-```
-$ tree .
-
-├── 1000003258.jpg
-├── 1000003258.txt
-├── 1000003259.jpg
-├── 1000003259.txt
-├── 1000003262.jpg
-├── 1000003262.txt
-├── 1000003263.jpg
-├── 1000003263.txt
-├── 1000003264.jpg
-├── 1000003264.txt
-├── 1000003265.jpg
-├── 1000003265.txt
-├── IMG_20220326_135114.jpg
-├── IMG_20220326_135114.txt
-├── IMG_20220706_184917.jpg
-├── IMG_20220706_184917.txt
-├── IMG_20230512_150235.jpg
-├── IMG_20230512_150235.txt
-├── IMG_20230719_065425.jpg
-├── IMG_20230719_065425.txt
-```
-
-Where *.JPG is the image, and the same file name with extension *.TXT contains the caption of the image. I will assume this dataset directory is located in `/path/to/datasets/behitek-lora`.
-
-## Training configuration
-
-Go back to SimpleTuner, you can find the configuration sample in the `config` folder.
-
-### Create your own config
-```bash
-cd config/
-cp config.json.example config.json
-cp lycoris_config.json.example lycoris_config.json
-cp user_prompt_library.json.example user_prompt_library.json
-cp multidatabackend.json.example dataset.behitek.json
-```
-
-I will provide you a sample config for you to fine-tune Flux.1-dev LoRA, with trigger word behitek.
-
-### The config.json
-
-This is the config.json file, update the following key:
-* `--validation_prompt`, enter your prompt with trigger word.
-* `--data_backend_config`, `--output_dir`, `--lycoris_config`, and `--user_prompt_library`, make sure they are correct.
-
-```json
-{
- "--resume_from_checkpoint": "latest",
- "--data_backend_config": "config/dataset.behitek.json",
- "--aspect_bucket_rounding": 2,
- "--seed": 42,
- "--minimum_image_size": 0,
- "--output_dir": "output/behitek-lycoris-v0.1",
- "--lora_type": "lycoris",
- "--lycoris_config": "config/lycoris_config.json",
- "--max_train_steps": 10000,
- "--num_train_epochs": 0,
- "--checkpointing_steps": 1000,
- "--checkpoints_total_limit": 5,
- "--hub_model_id": "simpletuner-lora",
- "--push_to_hub": "false",
- "--push_checkpoints_to_hub": "false",
- "--tracker_project_name": "lora-training",
- "--tracker_run_name": "simpletuner-lora",
- "--report_to": "tensorboard",
- "--model_type": "lora",
- "--pretrained_model_name_or_path": "black-forest-labs/FLUX.1-dev",
- "--model_family": "flux",
- "--train_batch_size": 1,
- "--gradient_checkpointing": "true",
- "--caption_dropout_probability": 0.1,
- "--resolution_type": "pixel_area",
- "--resolution": 1024,
- "--validation_seed": 42,
- "--validation_steps": 1000,
- "--validation_resolution": "1024x1024",
- "--validation_guidance": 3.0,
- "--validation_guidance_rescale": "0.0",
- "--validation_num_inference_steps": "20",
- "--validation_prompt": "behitek a man sitting on a chair wearing a white shirt, focused on the book he is holding in his hand. On the table in front of him is an object, and behind him are a few people, trees, and a blue sky",
- "--mixed_precision": "bf16",
- "--optimizer": "adamw_bf16",
- "--learning_rate": "1e-4",
- "--lr_scheduler": "polynomial",
- "--lr_warmup_steps": 100,
- "--validation_torch_compile": "false",
- "--disable_benchmark": "false",
- "--base_model_precision": "int8-quanto",
- "--text_encoder_1_precision": "no_change",
- "--text_encoder_2_precision": "no_change",
- "--lora_rank": 16,
- "--max_grad_norm": 1.0,
- "--base_model_default_dtype": "bf16",
- "--user_prompt_library": "config/user_prompt_library.json"
-}
-```
-**Notes**:
-* I use `base_model_precision=int8-quanto` for VRAM saving.
-* Every `validation_steps`, the model will generate validation images, and save them to the `validation_images` folder inside the `output_dir`.
-* `train_batch_size=1` is a recommended value (by SimpleTuner) for small dataset.
-
-### The user_prompt_library.json
-The `user_prompt_library.json` file is a list of prompts, you can add prompts here. During the model validation, it will generate images based on these prompts. Example:
-
-```json
-{
- "behitek_1": "behitek A 30-year-old man with a rugged yet stylish look, dressed in a beige explorer jacket with cargo pants, standing on the edge of a cliff at sunset, overlooking a vast canyon. His expression is a mix of awe and determination. The scene is rich with glowing orange and red hues, with wind tousling his hair.",
- "behitek_2": "behitek A 30-year-old man with a sharp jawline, wearing a sleek black turtleneck and tailored gray trousers, walking confidently down a bustling city street. Neon lights from billboards reflect on the wet pavement, creating a vibrant, futuristic cityscape."
-}
-```
-
-### The dataset.behitek.json
-
-The `dataset.behitek.json` file:
-
-```json
-
-[
- {
- "id": "behitek-subject",
- "type": "local",
- "crop": true,
- "crop_style": "center",
- "crop_aspect": "square",
- "resolution": 1024,
- "minimum_image_size": 1024,
- "maximum_image_size": 1024,
- "target_downsample_size": 1024,
- "resolution_type": "pixel_area",
- "cache_dir_vae": "cache/vae/flux/behitek-subject",
- "instance_data_dir": "/path/to/datasets/behitek-lora",
- "caption_strategy": "textfile",
- "instance_prompt": "behitek",
- "metadata_backend": "discovery",
- "is_regularisation_data": false,
- "repeats": 1000
- },
- {
- "id": "behitek-subject-512",
- "type": "local",
- "crop": true,
- "crop_style": "center",
- "crop_aspect": "square",
- "resolution": 512,
- "minimum_image_size": 512,
- "maximum_image_size": 512,
- "target_downsample_size": 512,
- "resolution_type": "pixel_area",
- "cache_dir_vae": "cache/vae/flux/behitek-subject-512",
- "instance_data_dir": "/path/to/datasets/behitek-lora",
- "caption_strategy": "textfile",
- "instance_prompt": "behitek",
- "metadata_backend": "discovery",
- "is_regularisation_data": false,
- "repeats": 1000
- },
- {
- "id": "text-embeds",
- "type": "local",
- "dataset_type": "text_embeds",
- "default": true,
- "cache_dir": "cache/text/flux",
- "disabled": false,
- "write_batch_size": 128
- }
-]
-```
-
-**Notes:**
-* Make sure to correct the `instance_data_dir` to your dataset directory.
-* If your image dimension is smaller than 1024x1024, you will got the error: `No images were discovered by the bucket ...`.
-* There is several `caption_strategy`, but `textfile` is the right setting.
-* `repeats` is the number of times to repeat the same image, 1000 is a good number.
-
-
-### The lycoris_config.json
-
-For the `lycoris_config.json` file, leave it as it is, the default setting is fine.
-
-## Training
-
-Still in the root directory of SimpleTuner, run the following command:
-
-```
-$ tmux new -s train # create a new tmux session
-$ source ./venv/bin/activate
-./train.sh
-```
-
-During training, the model will generate images based on the prompts in the `user_prompt_library.json` and `validation_prompt` in the config file, these images is save in your `output_dir`.
-
-### Training Experience
-
-- If your dataset consists only of faces (neck and head) with a clean background, you don't need to generate captions. Simply use `caption_strategy: "instanceprompt"`, where the `instanceprompt` serves as the trigger word. In this case, 2000 training steps are sufficient.
-- For diverse data (e.g., different outfits, backgrounds, or even the same person in varied settings), generating captions is recommended. With more diverse data, training for more steps is beneficial. For example, 10,000 steps is a good benchmark.
-- If you're unsure when the model has converged, check the loss curve in TensorBoard and save additional checkpoints for safety.
-
-## Inference
-
-Once the model is trained, you can use the below Python script (not a clean code) to generate images:
-
-```python
-import torch
-from diffusers import DiffusionPipeline
-from lycoris import create_lycoris_from_weights
-import os
-
-model_id = 'black-forest-labs/FLUX.1-dev'
-adapter_file_path = "/path/to/output_dir/pytorch_lora_weights.safetensors"
-pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
-lora_scale = 1.0
-wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_file_path, pipeline.transformer)
-wrapper.merge_to()
-
-
-## Optional: quantise the model to save on vram.
-## Note: The model was quantised during training, and so it is recommended to do the same during inference time.
-from optimum.quanto import quantize, freeze, qint8
-quantize(pipeline.transformer, weights=qint8)
-freeze(pipeline.transformer)
-
-output_dir = os.path.join("output", "behitek-hitech")
-os.makedirs(output_dir, exist_ok=True)
-
-
-prompts = [
- "behitek A 30-year-old man with a rugged yet stylish look, dressed in a beige explorer jacket with cargo pants, standing on the edge of a cliff at sunset, overlooking a vast canyon. His expression is a mix of awe and determination. The scene is rich with glowing orange and red hues, with wind tousling his hair.",
- "behitek A 30-year-old man with a sharp jawline, wearing a sleek black turtleneck and tailored gray trousers, walking confidently down a bustling city street. Neon lights from billboards reflect on the wet pavement, creating a vibrant, futuristic cityscape.",
- "behitek A young man, 30 years old, sitting in a high-tech office filled with holographic screens and futuristic gadgets. He’s wearing a casual blazer over a graphic T-shirt and glasses with a subtle glow, symbolizing augmented reality.",
- "behitek A 30-year-old warrior with a chiseled physique, clad in intricate silver armor with glowing blue runes. He holds a massive sword that emits a faint magical aura. He stands on a battlefield surrounded by mythical creatures under a dark, stormy sky.",
- "behitek A 30-year-old man with a cheerful demeanor, dressed in outdoor gear, standing beside a sparkling mountain lake. A camera hangs from his neck, and his backpack is packed with supplies. The serene landscape features towering pine trees and a snow-capped peak in the background.",
- "behitek A young man, 30 years old, styled in 1980s adventure fashion—khaki shorts, a buttoned shirt, and a leather satchel. He is mid-action, leaping over a crumbling stone bridge in an ancient jungle temple, vines and artifacts all around.",
- "behitek A 30-year-old man in a contemporary interpretation of samurai attire, blending traditional armor with urban streetwear. He holds a katana and stands in an empty urban alley lit by paper lanterns, exuding a calm yet powerful presence.",
- "behitek A young man in his 30s, dressed in a futuristic spacesuit with a sleek, glowing helmet. He floats in the vastness of space, with a swirling galaxy visible in the background. The stars and nebulae create a stunning, ethereal glow.",
- "behitek A 30-year-old man in Renaissance clothing—ruffled shirt and velvet coat—standing in an ornate studio. He is surrounded by canvases, sculptures, and paints. His face reflects deep concentration as he works on a masterpiece.",
- "behitek A young man, 30 years old, sitting in a grand library filled with towering shelves of ancient books. He’s wearing a long coat with mystical symbols embroidered in gold, surrounded by floating magical texts and glowing orbs."
-]
-
-import random
-for idx, prompt in enumerate(prompts):
- for i in range(30):
- print(f"Generating image {i}")
- seed = random.randint(0, 1000000000)
- pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') # the pipeline is already in its target precision level
- image = pipeline(
- prompt=prompt,
- num_inference_steps=20,
- generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(seed),
- width=1024,
- height=1024,
- guidance_scale=3.0,
- ).images[0]
- image.save(os.path.join(output_dir, f"{idx}_{seed}.png"), format="PNG")
-```
-
-With the same prompt, try changing the seed to get different results. In the above bad code, I do generate 30 images for each prompt. From many generated images, you can select some good ones.
-
-
-That's it! You have now created a Flux LoRa model.
-
-## Reference
-* [Sample work: salmonrusty/simpletuner-lora](https://huggingface.co/salmonrusty/simpletuner-lora#training-settings)
-* [SimpleTuner](https://github.com/bghira/SimpleTuner)
-* [Fine-tune FLUX.1 to create images of yourself](https://replicate.com/blog/fine-tune-flux-with-faces)
diff --git a/blog/2025-08-31-stop-using-requirements.md b/blog/2025-08-31-stop-using-requirements.md
deleted file mode 100644
index fa7b74e..0000000
--- a/blog/2025-08-31-stop-using-requirements.md
+++ /dev/null
@@ -1,669 +0,0 @@
----
-slug: stop-using-requirements-txt
-title: "Stop Using requirements.txt"
-authors: [hieunv]
-description: "The requirements.txt is a legacy dependency management tool that is no longer fit for modern Python projects. We need a better dependency management tool."
-image: /img/blog/stop-using-requirements.jpg
-comments: true
-tags: [python, dependency-management, poetry, uv, devops, best-practices]
----
-
-It's 3 AM, and your production deployment just failed. Again. The same code that worked perfectly on your local machine is now throwing mysterious import errors on the server. Sound familiar?
-
-
-
-## The 3 AM Production Nightmare
-
-Let me paint you a picture that might feel uncomfortably familiar. You're running a Flask web application in production using Docker containers. Your team just deployed what should have been a routine update—running your test suite with pytest to ensure everything works before the new release.
-
-Then the container crashes on startup.
-
-```bash
-ImportError: cannot import name 'url_quote' from 'werkzeug.urls'
-(/opt/conda/lib/python3.10/site-packages/werkzeug/urls.py)
-```
-
-Your heart sinks. The exact same code that runs perfectly when you execute `python run.py` is now failing during pytest execution. You haven't changed any Flask or Werkzeug code—so what went wrong?
-
-Your `requirements.txt` looked innocent enough:
-
-```txt
-Flask==2.2.2
-pytest>=7.0.0
-# ... other dependencies
-```
-
-The problem? Flask 2.2.2 specified `Werkzeug>=2.2.0` in its dependencies, which seemed reasonable. Your local environment had been running Werkzeug 2.3.7 for weeks without issues. But when pytest was installed fresh in the Docker container, it pulled in the latest available Werkzeug version: 3.0.0.
-
-Here's the kicker: [Werkzeug 3.0.0 removed the deprecated `url_quote` function](https://werkzeug.palletsprojects.com/en/3.0.x/changes/#version-3-0-0) that Flask 2.2.2 still relied on. Your "compatible" dependency specification had just broken your entire application.
-
-The impact was immediate and severe. Your CI/CD pipeline was blocked, preventing any deployments. The development team couldn't run tests locally after pulling the latest changes. What should have been a 5-minute deployment turned into a 3-hour emergency debugging session, with the fix being a simple line: `Werkzeug==2.3.7`.
-
-This scenario plays out in Python teams worldwide, every single day. The root cause isn't developer incompetence or bad luck—it's the fundamental limitations of `requirements.txt` as a dependency management solution. What worked fine for simple scripts in 2008 simply cannot handle the complexity of modern Python applications with dozens of dependencies, each with their own sub-dependencies and version constraints.
-
-The good news? There's a better way, and migrating is easier than you might think.
-
-## The Fundamental Problems with requirements.txt
-
-### Dependency Resolution Hell
-
-The most critical flaw in `requirements.txt` is its complete lack of intelligent dependency resolution. When you run `pip install -r requirements.txt`, pip installs packages in the order they appear, attempting to satisfy version constraints as it goes. But it has no global view of all requirements and cannot backtrack when conflicts arise.
-
-Consider this real-world scenario:
-
-```txt
-# requirements.txt
-django==4.2.0
-django-extensions==3.2.1
-celery==5.3.0
-kombu==5.2.4
-```
-
-This looks reasonable, but there's a hidden time bomb. `django-extensions` 3.2.1 requires `Django>=3.2,<4.2`, while you've specified `Django==4.2.0`. The installation might succeed if Django is installed first, but you're running with an unsupported configuration that could break at any time.
-
-Poetry's dependency resolver would catch this immediately:
-
-```bash
-$ poetry add django==4.2.0 django-extensions==3.2.1
-
-Because django-extensions (3.2.1) depends on Django (>=3.2,<4.2)
- and no versions of django-extensions match >3.2.1,<4.0.0,
-django-extensions is forbidden.
-So, because your-project depends on django-extensions (^3.2.1),
-version solving failed.
-```
-
-This upfront conflict detection prevents the production surprises that plague `requirements.txt` users.
-
-### Reproducible Environment Issues
-
-`requirements.txt` only captures your direct dependencies, not the entire dependency tree. This creates a reproducibility nightmare that every Python developer has experienced.
-
-When you install `requests==2.31.0`, you're also installing:
-
-```
-requests==2.31.0
-├── certifi>=2017.4.17
-├── charset-normalizer>=2.0.0,<4
-├── idna>=2.5,<4
-└── urllib3>=1.21.1,<3
-```
-
-Your `requirements.txt` doesn't capture these sub-dependency versions. Six months later, when `urllib3` releases version 2.1.0 with breaking changes, your production deployment might install this newer version while your development environment still runs the older one.
-
-The "works on my machine" syndrome isn't a developer joke—it's a systematic failure of dependency management that costs teams thousands of hours annually.
-
-### Development vs Production Separation
-
-Most Python projects struggle with cleanly separating development tools from production dependencies. You end up with solutions like:
-
-```txt
-# requirements.txt
-django==4.2.0
-psycopg2-binary==2.9.7
-gunicorn==21.2.0
-
-# requirements-dev.txt
--r requirements.txt
-pytest==7.4.0
-black==23.7.0
-mypy==1.5.0
-```
-
-This approach has several problems:
-
-1. **Security risk**: Development tools often have broader permissions and additional attack surface
-2. **Performance impact**: Extra packages increase container size and startup time
-3. **Maintenance burden**: Multiple files get out of sync easily
-4. **Deployment complexity**: CI/CD pipelines need special handling for different requirement files
-
-### Manual Dependency Management Burden
-
-Managing dependencies with `requirements.txt` is entirely manual. When you want to add a new package, you need to:
-
-1. Research compatible versions
-2. Manually add it to `requirements.txt`
-3. Install and test
-4. Update version constraints if conflicts arise
-5. Remember to update related packages
-6. Manually check for security vulnerabilities
-
-This process is error-prone and time-consuming. Teams often defer dependency updates, leading to technical debt and security vulnerabilities. The manual overhead grows exponentially with project complexity.
-
-## Modern Solutions: Poetry and uv
-
-The Python ecosystem has evolved significantly since `requirements.txt` was introduced. Two tools have emerged as clear leaders in modern dependency management: Poetry and uv. Both solve the fundamental problems we've discussed, but with different approaches and trade-offs.
-
-### Poetry - The Established Choice
-
-Poetry has been the gold standard for Python dependency management since 2018. It's a mature, battle-tested tool that combines dependency management, virtual environment handling, and package publishing into a cohesive workflow.
-
-Key advantages:
-- **Mature ecosystem**: Extensive plugin ecosystem and community support
-- **Integrated toolchain**: Handles everything from dependency resolution to PyPI publishing
-- **Proven reliability**: Used by thousands of production projects
-- **Rich configuration**: Extensive customization options for complex projects
-
-Poetry uses a SAT solver for dependency resolution, ensuring that all package requirements are satisfied before installation begins. This prevents the conflicts that plague `requirements.txt` users.
-
-### uv - The New Performance King
-
-uv represents the next generation of Python tooling. Developed by Astral (the team behind Ruff), it's written in Rust and focuses on speed and simplicity. While newer than Poetry, it's rapidly gaining adoption due to its exceptional performance.
-
-Key advantages:
-- **Blazing performance**: 10-100x faster than pip for most operations
-- **Universal toolchain**: Combines package management, Python installation, and virtual environments
-- **Modern architecture**: Built from the ground up with current Python standards
-- **Seamless compatibility**: Works with existing `requirements.txt` and `pyproject.toml` files
-
-### Performance Comparison
-
-Let's look at concrete benchmarks for a typical Django project with 50 dependencies:
-
-| Operation | pip | Poetry | uv |
-|-----------|-----|---------|-----|
-| Fresh install | 45s | 32s | 3s |
-| Cached install | 12s | 8s | 0.5s |
-| Lock file generation | N/A | 15s | 1s |
-| Memory usage | 150MB | 200MB | 50MB |
-
-These performance improvements aren't just nice-to-have—they fundamentally change developer experience. Faster dependency resolution means shorter feedback loops, more frequent updates, and less time waiting for CI/CD pipelines.
-
-## Poetry Deep Dive
-
-### Smart Dependency Resolution
-
-Poetry's dependency resolver is built on a SAT (Boolean satisfiability) solver, similar to those used in advanced package managers like Conda and APT. This means Poetry can:
-
-1. **Analyze all constraints simultaneously**: Instead of installing packages sequentially, Poetry builds a complete dependency graph first
-2. **Detect conflicts before installation**: No more failed deployments due to incompatible packages
-3. **Find optimal solutions**: When multiple valid solutions exist, Poetry chooses the one that maximizes compatibility
-
-Here's how Poetry handles the Django conflict we discussed earlier:
-
-```bash
-$ poetry init
-$ poetry add django@^4.2.0
-$ poetry add django-extensions@^3.2.1
-
-Creating virtualenv myproject in /Users/dev/myproject/.venv
-Updating dependencies
-Resolving dependencies... (0.5s)
-
-Because django-extensions (3.2.1) depends on Django (>=3.2,<4.2)
- and no versions of django-extensions match >3.2.1,<4.0.0,
-django-extensions is forbidden.
-
-Would you like to try django-extensions@^3.1.0 instead? [y/N]
-```
-
-Poetry not only detects the conflict but suggests a resolution. This proactive approach prevents the production surprises that plague `requirements.txt` users.
-
-### Lock File System
-
-Poetry's lock file (`poetry.lock`) is the secret to reproducible environments. Unlike `requirements.txt`, which only specifies direct dependencies, the lock file captures the exact version of every package in your dependency tree:
-
-```toml
-# poetry.lock (excerpt)
-[[package]]
-name = "requests"
-version = "2.31.0"
-description = "Python HTTP for Humans."
-category = "main"
-optional = false
-python-versions = ">=3.7"
-
-[package.dependencies]
-certifi = ">=2017.4.17"
-charset-normalizer = ">=2,<4"
-idna = ">=2.5,<4"
-urllib3 = ">=1.21.1,<3"
-
-[[package]]
-name = "urllib3"
-version = "1.26.16"
-description = "HTTP library with thread-safe connection pooling"
-# ... exact hash and metadata
-```
-
-This lock file ensures that every developer, CI system, and production server installs identical package versions. The lock file also includes cryptographic hashes for security verification.
-
-**Pro tip**: Always commit `poetry.lock` to version control, but never edit it manually. Poetry manages this file automatically.
-
-### Dependency Groups Management
-
-One of Poetry's most powerful features is dependency groups, which cleanly separate different types of dependencies:
-
-```toml
-# pyproject.toml
-[tool.poetry.dependencies]
-python = "^3.11"
-django = "^4.2.0"
-psycopg2-binary = "^2.9.0"
-celery = "^5.3.0"
-
-[tool.poetry.group.dev.dependencies]
-pytest = "^7.4.0"
-black = "^23.7.0"
-mypy = "^1.5.0"
-pytest-django = "^4.5.0"
-
-[tool.poetry.group.docs.dependencies]
-mkdocs = "^1.5.0"
-mkdocs-material = "^9.0.0"
-
-[tool.poetry.group.test.dependencies]
-coverage = "^7.2.0"
-pytest-cov = "^4.1.0"
-```
-
-You can install specific groups as needed:
-
-```bash
-# Production: only main dependencies
-poetry install --only=main
-
-# Development: main + dev dependencies
-poetry install --with=dev
-
-# Documentation: main + docs dependencies
-poetry install --with=docs
-
-# Everything for local development
-poetry install
-```
-
-This approach eliminates the security and performance issues of installing development tools in production.
-
-### Virtual Environment Integration
-
-Poetry automatically creates and manages virtual environments, eliminating the manual `python -m venv` and `source activate` dance:
-
-```bash
-# Poetry handles everything automatically
-poetry install # Creates venv if needed, installs dependencies
-poetry shell # Activates the virtual environment
-poetry run python app.py # Runs commands in the venv without activation
-poetry run pytest # Run tests in the isolated environment
-```
-
-The virtual environment is created in a predictable location (`{cache-dir}/virtualenvs/{project-name}-{hash}`) and automatically activated when needed. This eliminates common issues like accidentally installing packages globally or using the wrong Python version.
-
-### Publishing and Packaging
-
-Poetry integrates package building and publishing, making it easy to distribute your code:
-
-```bash
-# Build distribution packages
-poetry build
-
-# Publish to PyPI
-poetry publish
-
-# Publish to private repository
-poetry publish -r private-repo
-```
-
-The `pyproject.toml` file contains all metadata needed for packaging:
-
-```toml
-[tool.poetry]
-name = "my-awesome-package"
-version = "0.1.0"
-description = "A fantastic Python package"
-authors = ["Your Name Here are some images of myself generated by Flux.1-dev LoRA
-
-
-
-- 1. This is a sample project. -
-- 2. This is a sample project. -
-- 3. This is a sample project. -
- - ), - links: [ - { - name: "GitHub repository", - link: "https://github.com/behitek", - }, - ], - }, -]; - -export default projects; diff --git a/src/data/_SocialLinks.js b/src/data/_SocialLinks.js deleted file mode 100644 index 16a7e13..0000000 --- a/src/data/_SocialLinks.js +++ /dev/null @@ -1,27 +0,0 @@ -import { faGithub, faLinkedin, faTwitter } from "@fortawesome/free-brands-svg-icons"; -import { faPaperPlane } from "@fortawesome/free-solid-svg-icons"; - -const socialLinks = [ - { - faIcon: faLinkedin, - alt: "Linkedin", - url: "https://www.linkedin.com/in/behitek", - }, - { - faIcon: faGithub, - alt: "GitHub", - url: "https://github.com/behitek", - }, - { - faIcon: faTwitter, - alt: "Twitter", - url: "https://twitter.com/behitek_", - }, - { - faIcon: faPaperPlane, - alt: "Email", - url: "mailto:hello@behitek.com", - }, -]; - -export default socialLinks; \ No newline at end of file diff --git a/new-site/src/env.d.ts b/src/env.d.ts similarity index 100% rename from new-site/src/env.d.ts rename to src/env.d.ts diff --git a/new-site/src/i18n/en.ts b/src/i18n/en.ts similarity index 100% rename from new-site/src/i18n/en.ts rename to src/i18n/en.ts diff --git a/new-site/src/i18n/utils.ts b/src/i18n/utils.ts similarity index 100% rename from new-site/src/i18n/utils.ts rename to src/i18n/utils.ts diff --git a/new-site/src/i18n/vi.ts b/src/i18n/vi.ts similarity index 100% rename from new-site/src/i18n/vi.ts rename to src/i18n/vi.ts diff --git a/new-site/src/layouts/BaseLayout.astro b/src/layouts/BaseLayout.astro similarity index 100% rename from new-site/src/layouts/BaseLayout.astro rename to src/layouts/BaseLayout.astro diff --git a/new-site/src/layouts/BlogPostLayout.astro b/src/layouts/BlogPostLayout.astro similarity index 100% rename from new-site/src/layouts/BlogPostLayout.astro rename to src/layouts/BlogPostLayout.astro diff --git a/new-site/src/pages/404.astro b/src/pages/404.astro similarity index 100% rename from new-site/src/pages/404.astro rename to src/pages/404.astro diff --git a/src/pages/bank/index.js b/src/pages/bank/index.js deleted file mode 100644 index 94b94de..0000000 --- a/src/pages/bank/index.js +++ /dev/null @@ -1,25 +0,0 @@ -// index.js -import Layout from '@theme/Layout'; -import React from 'react'; -import styles from './styles.module.css'; - -export default function QRCode() { - return ( -
-
-
- 
-
-
- Loading clan data...
-
-
-
-
- Error loading clan data: {error}
-
-
-
- {/* Rest of your JSX remains the same */}
-
-
-
-
-
- {clanData.name || "N/A"}
- -
- 
- {clanData.warLeague?.name || "N/A"}
-
- {/* Description */}
-
- {clanData.warLeague?.name || "N/A"}
-
- {clanData.description}
-
- {/* Action Buttons */}
-
-
-
-
- -
- {/* Clan Badge and Labels */}
-
-
- {/* Current War Section */}
- {warData && (
-
- 
-
-
- {/* Stats Section */}
-
- {clanData.labels?.map((label) => (
- 
- ))}
-
-
-
-
- {/* Clan Perks */}
-
- {Object.entries({
- 'Total Points:': `${clanData.clanPoints || 0} | ${clanData.clanVersusPoints || 0}`,
- 'Wars Won:': `${clanData.warWins || 0} (${((clanData.warWins / (clanData.warWins + clanData.warLosses + (clanData.warTies || 0))) * 100).toFixed(1)}%)`,
- 'War Win Streak:': clanData.warWinStreak || 0,
- 'War Results:': (
-
- {clanData.warWins || 0}
- {' | '}
- {clanData.warTies || 0}
- {' | '}
- {clanData.warLosses || 0}
-
- ),
- 'Members:': `${clanData.members || 0} / 50`,
- 'Status:': clanData.type ? clanData.type.charAt(0).toUpperCase() + clanData.type.slice(1) : "N/A",
- 'War Frequency:': clanData.warFrequency ? clanData.warFrequency.charAt(0).toUpperCase() + clanData.warFrequency.slice(1) : "N/A",
- 'Location:': (
-
- {clanData.location?.countryCode && (
- 
- )}
- {clanData.location?.name || "N/A"}
-
- ),
- }).map(([label, value]) => (
-
-
- )}
- {clanData.location?.name || "N/A"}
-
- ),
- }).map(([label, value]) => (
-
- {label}
- {value}
-
- ))}
-
-
- Clan Perks
-
- {Object.entries({
- 'Donation Wait Time:': clanPerks.donationWaitTime,
- 'Donation Limit (Troops|Spells|Sieges):': clanPerks.donationLimits,
- 'Donation Level Boost:': clanPerks.donationLevelBoost,
- 'Treasury Capacity:': clanPerks.treasuryCapacity,
- 'War Loot:': clanPerks.warLoot,
- }).map(([label, value]) => (
-
-
- {label}
- {value}
-
- ))}
-
-
- )}
-
-
- {warData.state === 'notInWar' ? (
- Current War
- -
-
- ) : (
-
-
-
-
-
- Not Currently In War
-The clan is currently not participating in any war
- - ); - } - return null; - })(
-
- )}
-
-
-
-
- 
-
- {warData.clan.name}
-
- ⚔ {warData.clan.attacks}/{warData.teamSize * warData.attacksPerMember}
-
-
-
-
- ⭐ {warData.clan.stars}/{warData.teamSize * 3}
-
- {Object.entries(
- warData.clan.members?.reduce((acc, member) => {
- const th = member.townhallLevel;
- acc[th] = (acc[th] || 0) + 1;
- return acc;
- }, {}) || {}
- ).sort((a, b) => b[0] - a[0]).map(([th, count]) => (
-
-
- 
- {count}
-
- ))}
-
- {count}
-
-
-
-
- vs
-
-
- {warData.state === 'preparation' ? 'Preparation Day' : 'Battle Day'}
-{warData.teamSize} vs {warData.teamSize}
-
-
-
- 
-
- {warData.opponent.name}
-
- ⚔ {warData.opponent.attacks}/{warData.teamSize * warData.attacksPerMember}
-
-
-
-
- ⭐ {warData.opponent.stars}/{warData.teamSize * 3}
-
- {Object.entries(warData.opponent.members?.reduce((acc, member) => {
- const th = member.townhallLevel;
- acc[th] = (acc[th] || 0) + 1;
- return acc;
- }, {}) || {}
- ).sort((a, b) => b[0] - a[0]).map(([th, count]) => (
-
-
-
- {count}
-
- ))}
-
- {count}
-
- ⚔️
-
- );
- }
-
- return warLeagueStatus;
-}
-
-function WarTimer({ warData }) {
- const [timeRemaining, setTimeRemaining] = useState('N/A');
-
- useEffect(() => {
- const updateTime = () => {
- if (!warData.startTime && !warData.endTime) {
- setTimeRemaining('N/A');
- return;
- }
-
- const now = new Date();
- let targetTime;
-
- if (warData.state === 'preparation') {
- targetTime = new Date(warData.startTime.replace(/^(\d{4})(\d{2})(\d{2})T(\d{2})(\d{2})(\d{2})/, '$1-$2-$3T$4:$5:$6'));
- } else {
- targetTime = new Date(warData.endTime.replace(/^(\d{4})(\d{2})(\d{2})T(\d{2})(\d{2})(\d{2})/, '$1-$2-$3T$4:$5:$6'));
- }
-
- const diffMs = targetTime.getTime() - now.getTime();
-
- if (diffMs > 0) {
- const hours = Math.floor(diffMs / (1000 * 60 * 60));
- const minutes = Math.floor((diffMs % (1000 * 60 * 60)) / (1000 * 60));
- setTimeRemaining(`${hours}h ${minutes}m`);
- } else {
- setTimeRemaining('N/A');
- }
- };
-
- // Initial update
- updateTime();
-
- // Set up interval to update every 20 seconds
- const interval = setInterval(updateTime, 20000);
-
- // Cleanup interval on unmount
- return () => clearInterval(interval);
- }, [warData.startTime, warData.endTime, warData.state]);
-
- return timeRemaining;
-}
\ No newline at end of file
diff --git a/src/pages/coc/styles.module.css b/src/pages/coc/styles.module.css
deleted file mode 100644
index cc338de..0000000
--- a/src/pages/coc/styles.module.css
+++ /dev/null
@@ -1,376 +0,0 @@
-.wrapper {
- background-color: #1a1a1a;
- min-height: 100vh;
- color: #ffffff;
- padding: 2rem 0;
-}
-
-.container {
- max-width: 1200px;
- margin: 0 auto;
- padding: 0 1rem;
-}
-
-.header {
- text-align: center;
- margin-bottom: 2rem;
-}
-
-.header h1 {
- font-size: 2.5rem;
- margin-bottom: 0.5rem;
- font-weight: bold;
-}
-
-.tag {
- font-size: 1.3rem;
- color: #888;
- margin-bottom: 1rem;
-}
-
-.header hr {
- border: 0;
- border-top: 1px solid #333;
- margin: 2rem 0;
-}
-
-.leagueBadge {
- display: inline-flex;
- align-items: center;
- gap: 0.5rem;
- background: #2a2a2a;
- padding: 0.5rem 1rem;
- border-radius: 4px;
-}
-
-.leagueBadge img {
- width: 24px;
- height: 24px;
-}
-
-.content {
- display: grid;
- grid-template-columns: 200px 1fr 400px;
- gap: 2rem;
- margin-bottom: 2rem;
-}
-
-.badgeSection {
- text-align: center;
- align-content: space-around;
-}
-
-.mainBadge {
- width: 200px;
- height: 200px;
- margin-bottom: 1rem;
-}
-
-.clanLabels {
- display: flex;
- justify-content: center;
- gap: 0.5rem;
-}
-
-.clanLabels img {
- width: 40px;
- height: 40px;
- border-radius: 4px;
- padding: 4px;
- background: #2a2a2a;
-}
-
-.statsSection, .perksSection {
- background: #2a2a2a;
- border-radius: 8px;
- padding: 1.5rem;
-}
-
-.perksSection h3 {
- margin-top: 0;
- margin-bottom: 1rem;
- font-size: 1.2rem;
-}
-
-.statRow {
- display: flex;
- justify-content: space-between;
- align-items: center;
- padding: 0.25rem 0;
- border-bottom: 1px solid #333;
-}
-
-.statRow:last-child {
- border-bottom: none;
-}
-
-.textSuccess {
- color: #74d67e;
-}
-
-.textDanger {
- color: #f76608;
-}
-
-.textGray {
- color: #CCCCCC;
-}
-
-.location {
- display: flex;
- align-items: center;
- gap: 0.5rem;
-}
-
-.location img {
- width: 20px;
- height: 15px;
-}
-
-.description {
- margin-top: 2rem;
- color: #ccc;
- display: grid;
- justify-content: center;
- text-align: justify;
- white-space: pre-line;
-}
-
-.warSection {
- margin-top: 2rem;
- background: #2a2a2a;
- border-radius: 8px;
- padding: 1.5rem;
-}
-
-.warSection h3 {
- margin: 0;
- font-size: 1.2rem;
-}
-
-.warSection > div {
- display: flex;
- justify-content: space-between;
- align-items: center;
- margin-bottom: 2rem;
-}
-
-.refreshButton {
- padding: 0.5rem !important;
- background: none !important;
-}
-
-
-.warContent {
- display: flex;
- flex-wrap: wrap;
- justify-content: center;
- align-items: center;
- gap: 2rem;
- width: 100%;
-}
-
-.warTeam {
- flex: 1;
- min-width: 250px;
- text-align: center;
-}
-
-.warInfo {
- flex: 0 1 auto;
- min-width: 200px;
- max-width: 400px;
- text-align: center;
- display: flex;
- flex-direction: column;
- justify-content: center;
- align-items: center;
- margin: 0 auto;
-}
-
-@media (max-width: 768px) {
- .warContent {
- flex-direction: column;
- align-items: center;
- }
-
- .warTeam {
- width: 100%;
- }
-
- .warInfo {
- width: 100%;
- margin: 1rem 0;
- }
-}
-
-.warTeam img {
- width: 40px;
- height: 40px;
- vertical-align: middle;
-}
-
-.warTeam h4 {
- margin: 0;
- font-size: 1.1rem;
- display: inline-flex;
- align-items: center;
-}
-
-.warProgress {
- display: flex;
- align-items: center;
- gap: 0.5rem;
- margin-bottom: 1rem;
-}
-
-.warProgress img {
- width: 20px;
- height: 20px;
- vertical-align: middle;
-}
-
-.progressBar {
- flex: 1;
- height: 8px;
- background: #1a1a1a;
- border-radius: 4px;
- overflow: hidden;
-}
-
-.progressFill {
- height: 100%;
- background: #4CAF50;
- transition: width 0.3s ease;
-}
-
-.townHallDistribution {
- display: flex;
- justify-content: center;
- gap: 0.5rem;
-}
-
-.townHall {
- /* background: #1a1a1a; */
- padding: 0.5rem;
- border-radius: 4px;
- font-size: 0.9rem;
- display: flex;
- flex-direction: column;
- align-items: center;
- gap: 0.25rem;
-}
-
-.townHall img {
- width: 32px;
- height: 32px;
-}
-
-.warInfo {
- text-align: center;
-}
-
-.warStatus {
- margin-bottom: 1rem;
-}
-
-.warStatus span {
- font-size: 1.5rem;
- font-weight: bold;
- color: #888;
-}
-
-.warStatus p {
- margin: 0.5rem 0;
- color: #888;
-}
-
-.warDetailsBtn {
- background: #1a1a1a;
- border: none;
- color: #fff;
- padding: 0.5rem 1rem;
- border-radius: 4px;
- cursor: pointer;
- transition: background-color 0.3s ease;
-}
-
-.warDetailsBtn:hover {
- background: #333;
-}
-
-@media (max-width: 1024px) {
- .content {
- grid-template-columns: 1fr;
- gap: 1.5rem;
- }
-
- .badgeSection {
- margin-bottom: 1rem;
- }
-}
-
-@media (max-width: 768px) {
- .header h1 {
- font-size: 2rem;
- }
-
- .tag {
- font-size: 1.2rem;
- }
-
- .statRow {
- font-size: 0.9rem;
- }
-}
-
-@keyframes spin {
- 0% { transform: rotate(0deg); }
- 100% { transform: rotate(360deg); }
-}
-
-@keyframes fadeOut {
- 0% { opacity: 1; }
- 50% { opacity: 1; }
- 100% { opacity: 0; }
-}
-
-.refreshButton {
- position: relative;
-}
-
-.refreshButton svg {
- transition: transform 0.3s ease;
-}
-
-.refreshButton.processing svg {
- animation: spin 1s linear infinite;
-}
-
-.refreshButton.success svg,
-.refreshButton.failure svg {
- opacity: 0;
-}
-
-.refreshButton.success::after,
-.refreshButton.failure::after {
- content: '';
- position: absolute;
- top: 50%;
- left: 50%;
- transform: translate(-50%, -50%);
- width: 20px;
- height: 20px;
- background-size: contain;
- background-repeat: no-repeat;
- animation: fadeOut 2s ease forwards;
- opacity: 1;
-}
-
-.refreshButton.success::after {
- background-image: url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20' fill='%234CAF50'%3E%3Cpath fill-rule='evenodd' d='M16.707 5.293a1 1 0 010 1.414l-8 8a1 1 0 01-1.414 0l-4-4a1 1 0 011.414-1.414L8 12.586l7.293-7.293a1 1 0 011.414 0z' clip-rule='evenodd'/%3E%3C/svg%3E");
-}
-
-.refreshButton.failure::after {
- background-image: url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20' fill='%23f44336'%3E%3Cpath fill-rule='evenodd' d='M4.293 4.293a1 1 0 011.414 0L10 8.586l4.293-4.293a1 1 0 111.414 1.414L11.414 10l4.293 4.293a1 1 0 01-1.414 1.414L10 11.414l-4.293 4.293a1 1 0 01-1.414-1.414L8.586 10 4.293 5.707a1 1 0 010-1.414z' clip-rule='evenodd'/%3E%3C/svg%3E");
-}
\ No newline at end of file
diff --git a/src/pages/components/_SocialLinks.js b/src/pages/components/_SocialLinks.js
deleted file mode 100644
index dabe649..0000000
--- a/src/pages/components/_SocialLinks.js
+++ /dev/null
@@ -1,27 +0,0 @@
-import { FontAwesomeIcon } from "@fortawesome/react-fontawesome";
-import React from "react";
-import styles from "./styles.module.css";
-
-import socialLinks from "../../data/_SocialLinks";
-
-function SocialLink({ faIcon, alt, url }) {
- return (
- Participating in War League
-The clan is currently in CWL (Clan War League)
-
-
-
- );
-}
-
-function SocialLinks() {
- return (
-
- {socialLinks.map((props, idx) => (
-
- );
-}
-
-export default SocialLinks;
diff --git a/src/pages/components/styles.module.css b/src/pages/components/styles.module.css
deleted file mode 100644
index 97ee1ae..0000000
--- a/src/pages/components/styles.module.css
+++ /dev/null
@@ -1,30 +0,0 @@
-/* stylelint-disable docusaurus/copyright-header */
-
-.socialLinks {
- display: flex;
- flex-direction: row;
- justify-content: center;
- margin: 1.2rem 0;
- padding-left: 0.3rem;
-}
-
-.socialLinks div {
- margin-right: 1rem;
-}
-
-.socialLinks a {
- font-size: 1rem;
- width: 100%;
-}
-
-@media screen and (max-width: 966px) {
- .socialLinks {
- justify-content: space-between;
- padding: 0 1.6rem;
- padding-bottom: 0.4rem;
- }
-
- .socialLinks div {
- margin-right: 0;
- }
-}
diff --git a/new-site/src/pages/contact.astro b/src/pages/contact.astro
similarity index 100%
rename from new-site/src/pages/contact.astro
rename to src/pages/contact.astro
diff --git a/new-site/src/pages/index.astro b/src/pages/index.astro
similarity index 100%
rename from new-site/src/pages/index.astro
rename to src/pages/index.astro
diff --git a/src/pages/index.js b/src/pages/index.js
deleted file mode 100644
index 18c247a..0000000
--- a/src/pages/index.js
+++ /dev/null
@@ -1,123 +0,0 @@
-import React, { useEffect, useRef, useState } from "react";
-
-import Link from "@docusaurus/Link";
-import useBaseUrl from "@docusaurus/useBaseUrl";
-import useDocusaurusContext from "@docusaurus/useDocusaurusContext";
-import Layout from "@theme/Layout";
-
-import SocialLinks from "./components/_SocialLinks";
-import styles from "./styles.module.css";
-
-function Home() {
- const context = useDocusaurusContext();
- const { siteConfig = {} } = context;
-
- const mainRef = useRef(null);
- const [isLoading, setIsLoading] = useState(true);
- const [headerHeight, setHeaderHeight] = useState(1536);
- const [bannerHeight, setBannerHeight] = useState(256);
-
- useEffect(() => {
- const tempHeaderHeight = Math.max(384, window.innerHeight);
- setHeaderHeight(tempHeaderHeight);
- setBannerHeight(Math.max(256, tempHeaderHeight / 2));
- setIsLoading(false);
- mainRef.current.hidden = false;
- }, []);
-
- return (
-
-
- - About me -
-
-
-
- ![]({useBaseUrl("img/me.jpeg")})
-
-
-
-
- Hello there 👋
-- 🇻🇳 I am Hieu Nguyen, an AI Engineer from Vietnam. -
-- 🚀 I love bringing AI into production to solve real problems, with a focus on natural language processing, retrieval augmented generation (RAG), LLMs, and ìnformation retrieval. -
-- 🎓 I hold a Master of Information Science degree from the Japan Advanced Institute of Science and Technology (JAIST). -
-- 💻 Besides coding, I enjoy exploring self-hosted solutions on Reddit's r/selfhosted and playing Clash of Clans. -
-
-
- Continue exploring?
- - -
-
-
-
-
- Minesweeper
-
-
-
- {Object.keys(DIFFICULTIES).map(mode => (
-
- ))}
-
-
-
-
- 💣 {DIFFICULTIES[difficulty].mines - flagsPlaced}
- ⏱️ {String(Math.floor(time / 60)).padStart(2, '0')}:{String(time % 60).padStart(2, '0')}
-
- {gameOver ? '💥 Game Over!' : gameWon ? '🎉 You Win!' : 'Playing...'}
-
-
-
-
- {board.map((row, rowIndex) =>
- row.map((cell, colIndex) => renderCell(cell, rowIndex, colIndex))
- )}
-
-
- {projects.map((project) => (
-
- );
-}
-
-export function ProjectListing(props) {
- const projectItem = props.projectItem;
- return (
-
-
- {project.imageUrl ? (
-
- ))}
-
- })
-
- ) : (
-
-
- )}
- {project.title}
-
-
-
- {project.title}
-{project.period}
-{project.subtitle}
-Read more
-
-
-
-
- })
- )}
-
- );
-}
-
-function FollowUp() {
- return (
- {projectItem.title}
-{projectItem.subtitle}
- {projectItem.imageUrl && ( -
-
-
-
-
- -
-
-
-
{" "} - {projectItem.category} -
- -
-
{" "} - {projectItem.period} -
- -
-
{projectItem.tech} -
- {projectItem.team && (
- -
-
{" "} - {projectItem.team.map((member, i) => ( - - {member.link && {member.name}} - {!member.link && member.name} - {i < projectItem.team.length - 1 ? ", " : ""} - - ))} -
- )}
-
{projectItem.description}
- {projectItem.links && (
- <>
- Links
- -
- {projectItem.links.map((link, i) => (
-
-
-
-
{link.name} - -
- ))}
-
-
- Continue exploring?
- -- My projects -
-
-
-
- {!showProjectItem &&
-