Added web ui;

Added start.command for one-click setup; Updated readme.md for one-click setup;
Added images of web-ui;
Added new web viewer after ply is generated;
Cleaned up project by moving styles and js into seperate files;

Author: cjgaspari

README.md

@@ -89,6 +89,21 @@ If you find our work useful, please cite the following paper:
 
 Our codebase is built using multiple opensource contributions, please see [ACKNOWLEDGEMENTS](ACKNOWLEDGEMENTS) for more details.
 
+## Web Interface (One-Click Setup)
+
+For a simple web interface where you can upload images and download 3D Gaussians:
+
+**macOS:** Double-click `start.command` in Finder.
+
+This will automatically:
+- Create the conda environment if it doesn't exist
+- Install all dependencies if needed
+- Start the web server at http://localhost:8000
+
+![](data/web-dark.png)
+![](data/web-light.png)
+
+
 ## License
 
 Please check out the repository [LICENSE](LICENSE) before using the provided code and

data/web-dark.png

@@ -0,0 +1,33 @@
+# Sharp Web Interface
+
+This is a web interface for the Sharp 3D prediction model.
+
+## Prerequisites
+
+Make sure you have the `sharp` package installed (see root README).
+Install the web dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+## Running the Server
+
+Run the following command from the `web` directory:
+
+```bash
+python app.py
+```
+
+Or using uvicorn directly:
+
+```bash
+uvicorn app:app --reload --host 0.0.0.0 --port 8000
+```
+
+## Usage
+
+1. Open your browser and navigate to `http://localhost:8000`.
+2. Drag and drop images or click to select them.
+3. Click "Predict 3D Gaussians".
+4. A zip file containing the resulting `.ply` files will be downloaded automatically.

data/web-light.png

@@ -0,0 +1,184 @@
+import sys
+from pathlib import Path
+import logging
+import shutil
+import tempfile
+import zipfile
+import io as python_io
+import base64
+
+from fastapi import FastAPI, Request, UploadFile, File
+from fastapi.responses import HTMLResponse, StreamingResponse, JSONResponse
+from fastapi.staticfiles import StaticFiles
+from fastapi.templating import Jinja2Templates
+import torch
+import numpy as np
+
+# Add src to path so we can import sharp
+sys.path.append(str(Path(__file__).parent.parent / "src"))
+
+from sharp.models import (
+    PredictorParams,
+    RGBGaussianPredictor,
+    create_predictor,
+)
+from sharp.utils import io as sharp_io
+from sharp.utils.gaussians import save_ply
+from sharp.cli.predict import predict_image, DEFAULT_MODEL_URL
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+LOGGER = logging.getLogger(__name__)
+
+app = FastAPI()
+
+# Mount static files if needed (we created the dir)
+app.mount("/static", StaticFiles(directory=Path(__file__).parent / "static"), name="static")
+
+templates = Jinja2Templates(directory=Path(__file__).parent / "templates")
+
+# Global variables for the model
+predictor: RGBGaussianPredictor = None
+device: torch.device = None
+
+@app.on_event("startup")
+async def startup_event():
+    global predictor, device
+    
+    # Determine device
+    if torch.cuda.is_available():
+        device_str = "cuda"
+    elif torch.mps.is_available():
+        device_str = "mps"
+    else:
+        device_str = "cpu"
+    
+    device = torch.device(device_str)
+    LOGGER.info(f"Using device: {device}")
+
+    # Load model
+    LOGGER.info("Loading model...")
+    try:
+        # Try to load from cache or download
+        state_dict = torch.hub.load_state_dict_from_url(DEFAULT_MODEL_URL, progress=True, map_location=device)
+        
+        predictor = create_predictor(PredictorParams())
+        predictor.load_state_dict(state_dict)
+        predictor.eval()
+        predictor.to(device)
+        LOGGER.info("Model loaded successfully.")
+    except Exception as e:
+        LOGGER.error(f"Failed to load model: {e}")
+        raise e
+
+@app.get("/", response_class=HTMLResponse)
+async def read_root(request: Request):
+    return templates.TemplateResponse("index.html", {"request": request})
+
+@app.post("/predict")
+async def predict(files: list[UploadFile] = File(...)):
+    """Process images and return PLY data for viewing or download."""
+    if not predictor:
+        return JSONResponse({"error": "Model not loaded"}, status_code=500)
+
+    # Create a temporary directory to process files
+    with tempfile.TemporaryDirectory() as temp_dir:
+        temp_path = Path(temp_dir)
+        results = []
+        
+        for file in files:
+            try:
+                # Save uploaded file
+                file_path = temp_path / file.filename
+                with open(file_path, "wb") as buffer:
+                    shutil.copyfileobj(file.file, buffer)
+                
+                LOGGER.info(f"Processing {file.filename}")
+                
+                # Load image using sharp's IO to get focal length and handle rotation
+                image, _, f_px = sharp_io.load_rgb(file_path)
+                
+                # Run prediction
+                gaussians = predict_image(predictor, image, f_px, device)
+                
+                # Save PLY
+                ply_filename = f"{file_path.stem}.ply"
+                ply_path = temp_path / ply_filename
+                
+                height, width = image.shape[:2]
+                save_ply(gaussians, f_px, (height, width), ply_path)
+                
+                # Read PLY file and encode as base64
+                with open(ply_path, "rb") as f:
+                    ply_data = base64.b64encode(f.read()).decode("utf-8")
+                
+                results.append({
+                    "filename": file.filename,
+                    "ply_filename": ply_filename,
+                    "ply_data": ply_data,
+                    "width": width,
+                    "height": height,
+                    "focal_length": f_px,
+                })
+                
+            except Exception as e:
+                LOGGER.error(f"Error processing {file.filename}: {e}")
+                results.append({
+                    "filename": file.filename,
+                    "error": str(e),
+                })
+        
+        return JSONResponse({"results": results})
+
+
+@app.post("/predict/download")
+async def predict_download(files: list[UploadFile] = File(...)):
+    """Process images and return a ZIP file for download."""
+    if not predictor:
+        return HTMLResponse("Model not loaded", status_code=500)
+
+    # Create a temporary directory to process files
+    with tempfile.TemporaryDirectory() as temp_dir:
+        temp_path = Path(temp_dir)
+        output_zip = python_io.BytesIO()
+        
+        with zipfile.ZipFile(output_zip, "w") as zf:
+            for file in files:
+                try:
+                    # Save uploaded file
+                    file_path = temp_path / file.filename
+                    with open(file_path, "wb") as buffer:
+                        shutil.copyfileobj(file.file, buffer)
+                    
+                    LOGGER.info(f"Processing {file.filename}")
+                    
+                    # Load image using sharp's IO to get focal length and handle rotation
+                    image, _, f_px = sharp_io.load_rgb(file_path)
+                    
+                    # Run prediction
+                    gaussians = predict_image(predictor, image, f_px, device)
+                    
+                    # Save PLY
+                    ply_filename = f"{file_path.stem}.ply"
+                    ply_path = temp_path / ply_filename
+                    
+                    height, width = image.shape[:2]
+                    save_ply(gaussians, f_px, (height, width), ply_path)
+                    
+                    # Add to zip
+                    zf.write(ply_path, ply_filename)
+                    
+                except Exception as e:
+                    LOGGER.error(f"Error processing {file.filename}: {e}")
+                    continue
+        
+        output_zip.seek(0)
+        return StreamingResponse(
+            output_zip, 
+            media_type="application/zip", 
+            headers={"Content-Disposition": "attachment; filename=gaussians.zip"}
+        )
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)

src/sharp/web/README.md

@@ -0,0 +1,4 @@
+fastapi
+uvicorn
+python-multipart
+jinja2