Add Git LFS support for binary files

.gitattributes

@@ -0,0 +1,2 @@
+*.db filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,128 +1,10 @@
-# LUT Transformation API
-
-A FastAPI-based service for transforming .cube LUT files using AI-generated color adjustments and creating split preview images.
-
-## Features
-
-- **LUT Transformation**: Transform .cube files using JSON-based color adjustments
-- **AI Integration**: Generate color adjustments based on natural language prompts
-- **Split Preview**: Create side-by-side comparison images showing before/after effects
-- **Base64 Support**: Handle file uploads and image responses in base64 format
-
-## Setup
-
-### 1. Install Dependencies
-
-```bash
-pip install -r requirements.txt
-```
-
-### 2. Run the Server
-
-```bash
-uvicorn main:app --reload
-```
-
-The API will be available at `http://localhost:8000`
-
-### 3. API Documentation
-
-Visit `http://localhost:8000/docs` for interactive API documentation.
-
-## API Endpoints
-
-### POST /transform-lut
-
-Transform a LUT file using a text prompt.
-
-**Request Body:**
-```json
-{
-  "cube_file_base64": "base64_encoded_cube_file",
-  "user_prompt": "Make this LUT more cinematic with cool shadows"
-}
-```
-
-**Response:**
-```json
-{
-  "success": true,
-  "message": "LUT transformation completed successfully",
-  "adjustments_applied": {
-    "shadows": {"r": 0.9, "g": 1.0, "b": 1.2},
-    "midtones": {"r": 1.0, "g": 1.0, "b": 1.0},
-    "highlights": {"r": 1.1, "g": 1.05, "b": 0.95},
-    "global": {"r": 1.0, "g": 1.0, "b": 1.0}
-  },
-  "split_preview_base64": "base64_encoded_preview_image"
-}
-```
-
-### GET /health
-
-Check API health and sample image availability.
-
-**Response:**
-```json
-{
-  "status": "healthy",
-  "sample_image_exists": true
-}
-```
-
-## How It Works
-
-1. **Upload**: Send a .cube file as base64 and a text prompt
-2. **AI Processing**: The `generate_new_cube()` function processes the prompt and returns JSON adjustments
-3. **LUT Transformation**: Apply the adjustments to the original LUT using the `LUTTransformer` class
-4. **Image Processing**: Apply both original and modified LUTs to the sample image
-5. **Split Preview**: Create a side-by-side comparison with a vertical divider line
-6. **Response**: Return the preview image as base64
-
-## LUT Adjustment Format
-
-The AI generates adjustments in this JSON format:
-
-```json
-{
-  "shadows": {"r": 0.9, "g": 1.0, "b": 1.2},
-  "midtones": {"r": 1.0, "g": 1.0, "b": 1.0},
-  "highlights": {"r": 1.1, "g": 1.05, "b": 0.95},
-  "global": {"r": 1.0, "g": 1.0, "b": 1.0}
-}
-```
-
-- **shadows**: Adjustments for darker regions (luminance < 0.33)
-- **midtones**: Adjustments for medium regions (0.33 ≤ luminance < 0.66)
-- **highlights**: Adjustments for brighter regions (luminance ≥ 0.66)
-- **global**: Overall adjustments applied to all regions
-
-## Testing
-
-Use the provided `test_main.http` file to test the endpoints, or use curl:
-
-```bash
-curl -X POST "http://localhost:8000/transform-lut" \
-  -H "Content-Type: application/json" \
-  -d '{
-    "cube_file_base64": "VElUTEUgIlRlc3QgTFVUIgpMVVRfM0RfU0laRSAyCg...",
-    "user_prompt": "Make this LUT more cinematic with cool shadows"
-  }'
-```
-
-## Sample Image
-
-The API uses `sample.jpg` as the standard test image for preview generation. Make sure this file exists in the project root.
-
-## AI Integration
-
-Replace the placeholder `generate_new_cube()` function with your actual AI implementation that takes a user prompt and returns color adjustment JSON.
-
-## Error Handling
-
-The API includes comprehensive error handling for:
-- Invalid cube file formats
-- Missing sample images
-- Base64 decoding errors
-- Image processing failures
-- File system operations 
+---
+title: Lut Ai
+emoji: 🏃
+colorFrom: yellow
+colorTo: gray
+sdk: docker
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

main.py

@@ -56,14 +56,16 @@ def init_database():
     conn = sqlite3.connect(DATABASE_PATH)
     cursor = conn.cursor()
 
-    cursor.execute("""
+    cursor.execute(
+        """
         CREATE TABLE IF NOT EXISTS cube_files (
             id TEXT PRIMARY KEY,
             file_name TEXT NOT NULL,
             file_data BLOB NOT NULL,
             upload_date TIMESTAMP DEFAULT CURRENT_TIMESTAMP
         )
-    """)
+    """
+    )
 
     conn.commit()
     conn.close()
@@ -77,7 +79,7 @@ def save_cube_file_to_db(file_name: str, file_data: bytes) -> str:
 
     cursor.execute(
         "INSERT INTO cube_files (id, file_name, file_data) VALUES (?, ?, ?)",
-        (file_id, file_name, file_data)
+        (file_id, file_name, file_data),
     )
 
     conn.commit()
@@ -91,8 +93,7 @@ def get_cube_file_from_db(file_id: str) -> Optional[tuple]:
     cursor = conn.cursor()
 
     cursor.execute(
-        "SELECT file_name, file_data FROM cube_files WHERE id = ?",
-        (file_id,)
+        "SELECT file_name, file_data FROM cube_files WHERE id = ?", (file_id,)
     )
 
     result = cursor.fetchone()
@@ -272,7 +273,7 @@ def apply_lut_to_image(image_path: str, lut_path: str) -> np.ndarray:
 
 
 def create_split_preview(
-        original_lut_path: str, new_lut_path: str, sample_image_path: str
+    original_lut_path: str, new_lut_path: str, sample_image_path: str
 ) -> str:
     """Create a split preview image and return as base64"""
     try:
@@ -318,17 +319,14 @@ async def upload_cube_file(file: UploadFile = File(...)):
     Upload a .cube file and save it to the database
     """
     try:
-        if not file.filename.endswith('.cube'):
+        if not file.filename.endswith(".cube"):
             raise HTTPException(status_code=400, detail="Only .cube files are allowed")
 
         file_data = await file.read()
 
         file_id = save_cube_file_to_db(file.filename, file_data)
 
-        return CubeFileResponse(
-            file_id=file_id,
-            file_name=file.filename
-        )
+        return CubeFileResponse(file_id=file_id, file_name=file.filename)
 
     except Exception as e:
         raise HTTPException(status_code=500, detail=f"Error uploading file: {str(e)}")
@@ -343,9 +341,7 @@ async def list_cube_files():
         files = list_cube_files_from_db()
         return [
             CubeFileListItem(
-                file_id=file_id,
-                file_name=file_name,
-                upload_date=upload_date
+                file_id=file_id, file_name=file_name, upload_date=upload_date
             )
             for file_id, file_name, upload_date in files
         ]
@@ -366,7 +362,7 @@ async def transform_lut(request: LUTTransformRequest):
         file_name, cube_data = file_data
 
         with tempfile.NamedTemporaryFile(
-                mode="wb", suffix=".cube", delete=False
+            mode="wb", suffix=".cube", delete=False
         ) as temp_cube:
             temp_cube.write(cube_data)
             original_cube_path = temp_cube.name
@@ -383,12 +379,12 @@ async def transform_lut(request: LUTTransformRequest):
                 )
 
             with tempfile.NamedTemporaryFile(
-                    mode="w", suffix=".cube", delete=False
+                mode="w", suffix=".cube", delete=False
             ) as temp_new_cube:
                 new_cube_path = temp_new_cube.name
 
             if not transformer.save_cube_file(
-                    new_cube_path, f"{transformer.title}_AI_Modified"
+                new_cube_path, f"{transformer.title}_AI_Modified"
             ):
                 raise HTTPException(
                     status_code=500, detail="Failed to save new cube file"