Added new web viewer after ply is generated;

Author: cjgaspari

src/sharp/web/app.py

@@ -5,9 +5,10 @@
 import tempfile
 import zipfile
 import io as python_io
+import base64
 
 from fastapi import FastAPI, Request, UploadFile, File
-from fastapi.responses import HTMLResponse, StreamingResponse
+from fastapi.responses import HTMLResponse, StreamingResponse, JSONResponse
 from fastapi.staticfiles import StaticFiles
 from fastapi.templating import Jinja2Templates
 import torch
@@ -76,6 +77,63 @@ async def read_root(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)
 
@@ -98,9 +156,6 @@ async def predict(files: list[UploadFile] = File(...)):
                     image, _, f_px = sharp_io.load_rgb(file_path)
 
                     # Run prediction
-                    # We need to convert numpy image to what predict_image expects if needed
-                    # predict_image expects numpy array (H, W, 3)
-                    
                     gaussians = predict_image(predictor, image, f_px, device)
 
                     # Save PLY
@@ -115,7 +170,6 @@ async def predict(files: list[UploadFile] = File(...)):
 
                 except Exception as e:
                     LOGGER.error(f"Error processing {file.filename}: {e}")
-                    # We could add an error log to the zip or just skip
                     continue
 
         output_zip.seek(0)