Update Dockerfile to optimize application setup and streamline dependencies

.gitignore

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+.env

.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

.idea/LutAI.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$">
+      <excludeFolder url="file://$MODULE_DIR$/.venv" />
+    </content>
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/inspectionProfiles/Project_Default.xml

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+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="DuplicatedCode" enabled="true" level="WEAK WARNING" enabled_by_default="true">
+      <Languages>
+        <language minSize="74" name="Python" />
+      </Languages>
+    </inspection_tool>
+    <inspection_tool class="Eslint" enabled="true" level="WARNING" enabled_by_default="true" />
+    <inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
+      <option name="ignoredPackages">
+        <value>
+          <list size="123">
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+            <item index="121" class="java.lang.String" itemvalue="click" />
+            <item index="122" class="java.lang.String" itemvalue="vellum-ai" />
+          </list>
+        </value>
+      </option>
+    </inspection_tool>
+    <inspection_tool class="PyPep8NamingInspection" enabled="true" level="WEAK WARNING" enabled_by_default="true">
+      <option name="ignoredErrors">
+        <list>
+          <option value="N802" />
+        </list>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>

.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Python 3.12 (LutAI)" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.12 (LutAI)" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/LutAI.iml" filepath="$PROJECT_DIR$/.idea/LutAI.iml" />
+    </modules>
+  </component>
+</project>

README.md

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+# 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 

ai.py

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+from langchain_core.prompts import ChatPromptTemplate
+from langchain_openai import ChatOpenAI
+
+from dotenv import load_dotenv
+from pydantic import BaseModel, Field
+
+load_dotenv()
+
+
+class RGB(BaseModel):
+    r: float = Field(
+        description="Red channel multiplier (0.0-2.0). Values above 1.0 increase red, below 1.0 decrease red. Use for color temperature adjustments - lower for cool tones, higher for warm tones.",
+        ge=0.0,
+        le=2.0
+    )
+    g: float = Field(
+        description="Green channel multiplier (0.0-2.0). Values above 1.0 increase green, below 1.0 decrease green. Affects magenta/green color balance.",
+        ge=0.0,
+        le=2.0
+    )
+    b: float = Field(
+        description="Blue channel multiplier (0.0-2.0). Values above 1.0 increase blue, below 1.0 decrease blue. Higher values create cooler tones, lower values create warmer tones.",
+        ge=0.0,
+        le=2.0
+    )
+
+
+class CubeAI(BaseModel):
+    shadows: RGB = Field(
+        description="RGB multipliers for shadow tones (darker areas of the image, typically 0-33% luminance). Use to adjust color temperature and mood in dark areas."
+    )
+    midtones: RGB = Field(
+        description="RGB multipliers for midtone areas (medium brightness areas, typically 33-66% luminance). Primary area for overall color grading and mood."
+    )
+    highlights: RGB = Field(
+        description="RGB multipliers for highlight areas (brightest areas of the image, typically 66-100% luminance). Use for sky tones, bright surfaces, and overall brightness balance."
+    )
+    glob: RGB = Field(
+        description="Global RGB multipliers applied to the entire image across all tonal ranges. Use for overall color temperature shifts and global corrections."
+    )
+
+
+PROMPT = """
+You are an expert color grading AI that generates precise LUT (Look-Up Table) adjustments for cinematic color grading.
+
+Your task is to analyze the user's style request and generate RGB channel multipliers for different tonal ranges (shadows, midtones, highlights, and global).
+
+## Understanding LUT Color Grading:
+- A LUT transforms input colors to output colors for consistent color grading
+- RGB multipliers adjust the intensity of red, green, and blue channels
+- Values: 1.0 = no change, >1.0 = increase channel, <1.0 = decrease channel
+- Range: 0.0 to 2.0 for each channel
+
+## Tonal Ranges:
+- **Shadows** (0-33% luminance): Dark areas, deep shadows, black levels
+- **Midtones** (33-66% luminance): Primary subject matter, skin tones, main image content  
+- **Highlights** (66-100% luminance): Bright areas, sky, light sources, white levels
+- **Global**: Overall adjustment applied to entire image
+
+## Common Style Guidelines:
+
+### Cinematic Styles:
+- **Cool/Blue shadows**: shadows.b = 1.1-1.3, shadows.r = 0.8-0.9
+- **Warm highlights**: highlights.r = 1.1-1.2, highlights.b = 0.9-0.95
+- **Desaturated midtones**: midtones values closer to 1.0
+- **Teal & Orange**: shadows.b = 1.2, highlights.r = 1.15, midtones.g = 0.95
+
+### Mood Adjustments:
+- **Warm/Golden**: Increase red/green, decrease blue across tones
+- **Cool/Cold**: Increase blue, decrease red across tones  
+- **Vintage**: Slightly reduce contrast, warm highlights, cool shadows
+- **Modern/Clean**: Balanced adjustments, slight contrast enhancement
+
+### Technical Considerations:
+- Subtle adjustments (0.05-0.2 change) for natural looks
+- Dramatic adjustments (0.3+ change) for stylized looks
+- Maintain skin tone integrity in midtones
+- Avoid extreme values that cause color clipping
+
+## Examples:
+User: "Make this more cinematic with cool shadows"
+Response: shadows with blue increased (1.2), red decreased (0.85), warm highlights
+
+User: "Vintage film look with warm tones"  
+Response: Overall warm adjustment, slightly reduced contrast, golden highlights
+
+User: "Dark and moody atmosphere"
+Response: Reduced overall brightness, cool shadows, maintain highlight detail
+
+## Output Requirements:
+- Generate precise RGB multipliers for shadows, midtones, highlights, and global
+- Each RGB value must be between 0.0 and 2.0
+- Consider the interaction between different tonal ranges
+- Aim for cohesive, professional color grading results
+
+Analyze the user's request and generate appropriate LUT adjustments that achieve their desired look while maintaining image quality and professional color grading standards.
+"""
+
+
+def generate_cube(user_prompt: str) -> CubeAI:
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            ("system", PROMPT),
+            ("user", "{user_prompt}")
+        ]
+    )
+    model = ChatOpenAI(model="gpt-4.1-mini", temperature=0).with_structured_output(
+        CubeAI
+    )
+    chain = prompt | model
+    response = chain.invoke({"user_prompt": user_prompt})
+    return response

main.py

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+import base64
+import os
+import re
+import sqlite3
+import tempfile
+import uuid
+from io import BytesIO
+from typing import Dict, List, Optional
+
+import cv2
+import numpy as np
+from PIL import Image
+from fastapi import FastAPI, HTTPException, UploadFile, File
+from pydantic import BaseModel
+from starlette.middleware.cors import CORSMiddleware
+
+from ai import generate_cube
+
+app = FastAPI(title="LUT Transformation API", version="1.0.0")
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+class LUTRequest(BaseModel):
+    file_id: str
+    user_prompt: str
+
+
+class LUTTransformRequest(BaseModel):
+    file_id: str
+    user_prompt: str
+
+
+class CubeFileResponse(BaseModel):
+    file_id: str
+    file_name: str
+
+
+class CubeFileListItem(BaseModel):
+    file_id: str
+    file_name: str
+    upload_date: str
+
+
+DATABASE_PATH = "cube_files.db"
+
+
+def init_database():
+    """Initialize SQLite database and create tables"""
+    conn = sqlite3.connect(DATABASE_PATH)
+    cursor = conn.cursor()
+
+    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()
+
+
+def save_cube_file_to_db(file_name: str, file_data: bytes) -> str:
+    """Save cube file to database and return file ID"""
+    file_id = str(uuid.uuid4())
+    conn = sqlite3.connect(DATABASE_PATH)
+    cursor = conn.cursor()
+
+    cursor.execute(
+        "INSERT INTO cube_files (id, file_name, file_data) VALUES (?, ?, ?)",
+        (file_id, file_name, file_data)
+    )
+
+    conn.commit()
+    conn.close()
+    return file_id
+
+
+def get_cube_file_from_db(file_id: str) -> Optional[tuple]:
+    """Retrieve cube file from database by ID"""
+    conn = sqlite3.connect(DATABASE_PATH)
+    cursor = conn.cursor()
+
+    cursor.execute(
+        "SELECT file_name, file_data FROM cube_files WHERE id = ?",
+        (file_id,)
+    )
+
+    result = cursor.fetchone()
+    conn.close()
+    return result
+
+
+def list_cube_files_from_db() -> List[tuple]:
+    """List all cube files from database"""
+    conn = sqlite3.connect(DATABASE_PATH)
+    cursor = conn.cursor()
+
+    cursor.execute(
+        "SELECT id, file_name, upload_date FROM cube_files ORDER BY upload_date DESC"
+    )
+
+    results = cursor.fetchall()
+    conn.close()
+    return results
+
+
+class LUTTransformer:
+    def __init__(self):
+        self.title = ""
+        self.size = 0
+        self.lut_data = []
+
+    def parse_cube_file(self, filepath: str) -> bool:
+        """Parse .cube file and extract LUT data"""
+        try:
+            with open(filepath, "r") as file:
+                lines = file.readlines()
+
+            self.lut_data = []
+
+            for line in lines:
+                line = line.strip()
+
+                if not line or line.startswith("#"):
+                    continue
+
+                if line.startswith("TITLE"):
+                    self.title = line.split('"')[1] if '"' in line else line.split()[1]
+
+                elif line.startswith("LUT_3D_SIZE"):
+                    self.size = int(line.split()[1])
+
+                else:
+                    rgb_match = re.findall(r"[\d.]+", line)
+                    if len(rgb_match) >= 3:
+                        r, g, b = map(float, rgb_match[:3])
+                        self.lut_data.append([r, g, b])
+
+            return len(self.lut_data) > 0
+
+        except Exception as e:
+            print(f"Error parsing cube file: {e}")
+            return False
+
+    def apply_json_transformation(self, json_adjustments: Dict) -> bool:
+        """Apply JSON color adjustments to LUT data"""
+        try:
+            lut_array = np.array(self.lut_data)
+
+            for i, (r, g, b) in enumerate(lut_array):
+                luminance = 0.299 * r + 0.587 * g + 0.114 * b
+
+                if luminance < 0.33:
+                    if "shadows" in json_adjustments:
+                        adj = json_adjustments["shadows"]
+                        lut_array[i] *= [
+                            adj.get("r", 1.0),
+                            adj.get("g", 1.0),
+                            adj.get("b", 1.0),
+                        ]
+
+                elif luminance < 0.66:
+                    if "midtones" in json_adjustments:
+                        adj = json_adjustments["midtones"]
+                        lut_array[i] *= [
+                            adj.get("r", 1.0),
+                            adj.get("g", 1.0),
+                            adj.get("b", 1.0),
+                        ]
+
+                else:
+                    if "highlights" in json_adjustments:
+                        adj = json_adjustments["highlights"]
+                        lut_array[i] *= [
+                            adj.get("r", 1.0),
+                            adj.get("g", 1.0),
+                            adj.get("b", 1.0),
+                        ]
+
+            if "glob" in json_adjustments:
+                global_adj = json_adjustments["glob"]
+                lut_array *= [
+                    global_adj.get("r", 1.0),
+                    global_adj.get("g", 1.0),
+                    global_adj.get("b", 1.0),
+                ]
+
+            lut_array = np.clip(lut_array, 0.0, 1.0)
+            self.lut_data = lut_array.tolist()
+
+            return True
+
+        except Exception as e:
+            print(f"Error applying transformation: {e}")
+            return False
+
+    def save_cube_file(self, output_path: str, new_title: str = None) -> bool:
+        """Save modified LUT as .cube file"""
+        try:
+            with open(output_path, "w") as file:
+                title = new_title if new_title else f"{self.title}_modified"
+                file.write(f'TITLE "{title}"\n')
+                file.write(f"LUT_3D_SIZE {self.size}\n\n")
+
+                for r, g, b in self.lut_data:
+                    file.write(f"{r:.6f} {g:.6f} {b:.6f}\n")
+
+            return True
+
+        except Exception as e:
+            print(f"Error saving cube file: {e}")
+            return False
+
+
+def generate_new_cube(user_prompt: str) -> dict:
+    """
+    Placeholder for AI function that generates JSON adjustments based on user prompt.
+    This function should be replaced with the actual AI implementation.
+    """
+    response = generate_cube(user_prompt)
+    return response.model_dump(mode="json")
+
+
+def apply_lut_to_image(image_path: str, lut_path: str) -> np.ndarray:
+    """Apply LUT to image using OpenCV"""
+    try:
+        img = cv2.imread(image_path)
+        if img is None:
+            raise ValueError(f"Could not load image: {image_path}")
+
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+        transformer = LUTTransformer()
+        if not transformer.parse_cube_file(lut_path):
+            raise ValueError(f"Could not parse LUT file: {lut_path}")
+
+        lut_data = np.array(transformer.lut_data)
+        lut_size = transformer.size
+
+        lut_3d = lut_data.reshape((lut_size, lut_size, lut_size, 3))
+
+        img_normalized = img.astype(np.float32) / 255.0
+
+        result = np.zeros_like(img_normalized)
+
+        for i in range(img.shape[0]):
+            for j in range(img.shape[1]):
+                r, g, b = img_normalized[i, j]
+
+                r_idx = min(int(r * (lut_size - 1)), lut_size - 1)
+                g_idx = min(int(g * (lut_size - 1)), lut_size - 1)
+                b_idx = min(int(b * (lut_size - 1)), lut_size - 1)
+
+                result[i, j] = lut_3d[r_idx, g_idx, b_idx]
+
+        result = np.clip(result * 255, 0, 255).astype(np.uint8)
+        return result
+
+    except Exception as e:
+        print(f"Error applying LUT to image: {e}")
+        raise
+
+
+def create_split_preview(
+        original_lut_path: str, new_lut_path: str, sample_image_path: str
+) -> str:
+    """Create a split preview image and return as base64"""
+    try:
+        original_processed = apply_lut_to_image(sample_image_path, original_lut_path)
+        new_processed = apply_lut_to_image(sample_image_path, new_lut_path)
+
+        height, width = original_processed.shape[:2]
+        split_image = np.zeros_like(original_processed)
+
+        mid_point = width // 2
+        split_image[:, :mid_point] = original_processed[:, :mid_point]
+        split_image[:, mid_point:] = new_processed[:, mid_point:]
+
+        cv2.line(split_image, (mid_point, 0), (mid_point, height), (255, 255, 255), 2)
+
+        pil_image = Image.fromarray(split_image)
+
+        buffer = BytesIO()
+        pil_image.save(buffer, format="PNG")
+        buffer.seek(0)
+
+        base64_string = base64.b64encode(buffer.getvalue()).decode("utf-8")
+        return base64_string
+
+    except Exception as e:
+        print(f"Error creating split preview: {e}")
+        raise
+
+
+@app.on_event("startup")
+async def startup_event():
+    init_database()
+
+
+@app.get("/")
+async def root():
+    return {"message": "LUT Transformation API", "version": "1.0.0"}
+
+
+@app.post("/upload-cube", response_model=CubeFileResponse)
+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'):
+            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
+        )
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error uploading file: {str(e)}")
+
+
+@app.get("/cube-files", response_model=List[CubeFileListItem])
+async def list_cube_files():
+    """
+    List all uploaded cube files with their IDs and names
+    """
+    try:
+        files = list_cube_files_from_db()
+        return [
+            CubeFileListItem(
+                file_id=file_id,
+                file_name=file_name,
+                upload_date=upload_date
+            )
+            for file_id, file_name, upload_date in files
+        ]
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error listing files: {str(e)}")
+
+
+@app.post("/transform-lut")
+async def transform_lut(request: LUTTransformRequest):
+    """
+    Transform a LUT based on file ID and user prompt, return split preview image
+    """
+    try:
+        file_data = get_cube_file_from_db(request.file_id)
+        if not file_data:
+            raise HTTPException(status_code=404, detail="Cube file not found")
+
+        file_name, cube_data = file_data
+
+        with tempfile.NamedTemporaryFile(
+                mode="wb", suffix=".cube", delete=False
+        ) as temp_cube:
+            temp_cube.write(cube_data)
+            original_cube_path = temp_cube.name
+
+        try:
+            adjustments = generate_new_cube(request.user_prompt)
+            transformer = LUTTransformer()
+            if not transformer.parse_cube_file(original_cube_path):
+                raise HTTPException(status_code=400, detail="Failed to parse cube file")
+
+            if not transformer.apply_json_transformation(adjustments):
+                raise HTTPException(
+                    status_code=500, detail="Failed to apply transformations"
+                )
+
+            with tempfile.NamedTemporaryFile(
+                    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"
+            ):
+                raise HTTPException(
+                    status_code=500, detail="Failed to save new cube file"
+                )
+
+            sample_image_path = "sample.jpg"
+            if not os.path.exists(sample_image_path):
+                raise HTTPException(status_code=404, detail="Sample image not found")
+
+            split_preview_base64 = create_split_preview(
+                original_cube_path, new_cube_path, sample_image_path
+            )
+
+            return {
+                "success": True,
+                "message": "LUT transformation completed successfully",
+                "file_name": file_name,
+                "adjustments_applied": adjustments,
+                "split_preview_base64": split_preview_base64,
+            }
+
+        finally:
+            if os.path.exists(original_cube_path):
+                os.unlink(original_cube_path)
+            if "new_cube_path" in locals() and os.path.exists(new_cube_path):
+                os.unlink(new_cube_path)
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")
+
+
+@app.get("/health")
+async def health_check():
+    return {"status": "healthy", "sample_image_exists": os.path.exists("sample.jpg")}

requirements.txt

@@ -0,0 +1,7 @@
+fastapi==0.104.1
+uvicorn[standard]==0.24.0
+python-multipart==0.0.6
+pillow==10.1.0
+numpy==1.24.3
+opencv-python==4.8.1.78
+pydantic==2.4.2 

test_cube_generator.py

@@ -0,0 +1,63 @@
+#!/usr/bin/env python3
+"""
+Utility script to generate a sample .cube file and convert it to base64 for testing the LUT API.
+"""
+
+import base64
+import json
+
+def create_sample_cube_file(filename: str = "sample.cube", size: int = 8):
+    """Create a simple identity LUT cube file for testing"""
+    with open(filename, 'w') as f:
+        f.write('TITLE "Sample Identity LUT"\n')
+        f.write(f'LUT_3D_SIZE {size}\n\n')
+        
+        for r in range(size):
+            for g in range(size):
+                for b in range(size):
+                    red_val = r / (size - 1)
+                    green_val = g / (size - 1)
+                    blue_val = b / (size - 1)
+                    f.write(f'{red_val:.6f} {green_val:.6f} {blue_val:.6f}\n')
+    
+    print(f"Created sample cube file: {filename}")
+    return filename
+
+def cube_file_to_base64(cube_file_path: str) -> str:
+    """Convert a cube file to base64 string"""
+    with open(cube_file_path, 'rb') as f:
+        cube_data = f.read()
+    
+    base64_string = base64.b64encode(cube_data).decode('utf-8')
+    return base64_string
+
+def generate_test_request(cube_file_path: str, prompt: str = "Make this LUT more cinematic with cool shadows") -> dict:
+    """Generate a complete test request JSON"""
+    base64_cube = cube_file_to_base64(cube_file_path)
+    
+    request_data = {
+        "cube_file_base64": base64_cube,
+        "user_prompt": prompt
+    }
+    
+    return request_data
+
+if __name__ == "__main__":
+    cube_file = create_sample_cube_file()
+    
+    test_request = generate_test_request(cube_file)
+    
+    print(f"\nBase64 encoded cube file:")
+    print(f"Length: {len(test_request['cube_file_base64'])} characters")
+    print(f"First 100 chars: {test_request['cube_file_base64'][:100]}...")
+    
+    with open("test_request.json", "w") as f:
+        json.dump(test_request, f, indent=2)
+    
+    print(f"\nSaved complete test request to: test_request.json")
+    print(f"You can use this to test the API endpoint.")
+    
+    print(f"\nExample curl command:")
+    print(f"curl -X POST \"http://localhost:8000/transform-lut\" \\")
+    print(f"  -H \"Content-Type: application/json\" \\")
+    print(f"  -d @test_request.json") 

test_main.http

@@ -0,0 +1,14 @@
+### Test the root endpoint
+GET http://localhost:8000/
+
+### Test health check
+GET http://localhost:8000/health
+
+### Test LUT transformation endpoint
+POST http://localhost:8000/transform-lut
+Content-Type: application/json
+
+{
+  "cube_file_base64": "VElUTEUgIlRlc3QgTFVUIgpMVVRfM0RfU0laRSAyCgowLjAwMDAwMCAwLjAwMDAwMCAwLjAwMDAwMAowLjUwMDAwMCAwLjAwMDAwMCAwLjAwMDAwMAowLjAwMDAwMCAwLjUwMDAwMCAwLjAwMDAwMAowLjUwMDAwMCAwLjUwMDAwMCAwLjAwMDAwMAowLjAwMDAwMCAwLjAwMDAwMCAwLjUwMDAwMAowLjUwMDAwMCAwLjAwMDAwMCAwLjUwMDAwMAowLjAwMDAwMCAwLjUwMDAwMCAwLjUwMDAwMAoxLjAwMDAwMCAxLjAwMDAwMCAxLjAwMDAwMA==",
+  "user_prompt": "Make this LUT more cinematic with cool shadows"
+}