resolve twine check issues

.github/workflows/release-python.yml

@@ -111,8 +111,6 @@ jobs:
         with:
           name: sdist-${{ github.run_id }}
           path: target/wheels/*.tar.gz
-          compression-level: 9
-          overwrite: true
 
   build-wheels:
     needs: [coverage, integration-tests]
@@ -191,8 +189,6 @@ jobs:
         with:
           name: wheels-${{ github.run_id }}
           path: target/wheels/*.whl        
-          compression-level: 9
-          overwrite: true
 
   publish-to-pypi:
     needs: [create-sdist, build-wheels]

README.md

@@ -39,7 +39,7 @@ import imgdd as dd
 results = dd.hash(
     path="path/to/images",
     algo="dhash",  # Optional: default = dhash
-    filter="triangle"  # Optional: default = triangle
+    filter="triangle",  # Optional: default = triangle
     sort=False # Optional: default = False
 )
 print(results)

crates/imgddpy/README.md

@@ -39,7 +39,7 @@ import imgdd as dd
 results = dd.hash(
     path="path/to/images",
     algo="dhash",  # Optional: default = dhash
-    filter="triangle"  # Optional: default = triangle
+    filter="triangle",  # Optional: default = triangle
     sort=False # Optional: default = False
 )
 print(results)
@@ -70,7 +70,7 @@ print(duplicates)
 - `Nearest`, `Triangle`, `CatmullRom`, `Gaussian`, `Lanczos3`
 
 ## Contributing
-Contributions are always welcome! 🚀  
+Contributions are always welcome! 🚀
 
 Found a bug or have a question? Open a GitHub issue. Pull requests for new features or fixes are encouraged!
 

crates/imgddpy/docs/quickstart.md

@@ -15,7 +15,7 @@ import imgdd as dd
 results = dd.hash(
     path="path/to/images",
     algo="dhash",  # Optional: default = dhash
-    filter="triangle"  # Optional: default = triangle
+    filter="triangle",  # Optional: default = triangle
     sort=False # Optional: default = False
 )
 print(results)

crates/imgddpy/pyproject.toml

@@ -4,14 +4,10 @@ build-backend = "maturin"
 
 [project]
 name = "imgdd"
-version = "0.1.3"
-description = """
-Performance-first perceptual hashing library; perfect for handling large datasets. 
-Designed to quickly process nested folder structures, commonly found in image datasets
-"""
+description = "Performance-first perceptual hashing library; perfect for handling large datasets. Designed to quickly process nested folder structures, commonly found in image datasets"
 requires-python = ">=3.9"
 license = { file = "LICENSE" }
-dynamic = ["readme"]
+dynamic = ["readme", "version"]
 keywords = [
     "rust", 
     "imagehash", 
@@ -42,8 +38,8 @@ classifiers = [
   "Topic :: Scientific/Engineering",
 ]
 
-[tool.setuptools.dynamic]
-readme = { file = "README.md", content-type = "text/markdown"}
+[tool.maturin.dynamic]
+readme = {file = "README.md", content-type = "text/markdown"}
 
 [project.urls]
 homepage = "https://github.com/aastopher/imgdd"
@@ -70,5 +66,6 @@ test = [
 [tool.maturin]
 bindings = "pyo3"
 module-name = "imgdd"
-include = ["LICENSE", "README.md"]
-sdist-include = ["LICENSE", "README.md"]
+packages = "imgddpy"
+long_description = "README.md"
+long_description_content_type = "text/markdown"

crates/imgddpy/src/lib.rs

@@ -56,7 +56,7 @@ fn select_algo(algo: Option<&str>) -> &'static str {
 /// results = dd.hash(
 ///     path="path/to/images",
 ///     algo="dhash",
-///     filter="triangle"
+///     filter="triangle",
 ///     sort=False
 /// )
 /// print(results)