Update api_server.py

api_server.py

@@ -1,228 +1,3 @@
-# from fastapi import FastAPI, HTTPException, UploadFile, File, Form
-# from pydantic import BaseModel
-# import numpy as np
-# from PIL import Image
-# import io, uuid, os, shutil, timeit
-# from datetime import datetime
-# from fastapi.staticfiles import StaticFiles
-# from fastapi.middleware.cors import CORSMiddleware
-
-# # import your three wrappers
-# from app import predict_simple, predict_middle, predict_full
-
-# app = FastAPI()
-
-# # allow CORS if needed
-# app.add_middleware(
-#     CORSMiddleware,
-#     allow_origins=["*"],
-#     allow_methods=["*"],
-#     allow_headers=["*"],
-# )
-
-# BASE_URL = "https://snapanddtraceapp-988917236820.us-central1.run.app"
-# OUTPUT_DIR = os.path.abspath("./outputs")
-# os.makedirs(OUTPUT_DIR, exist_ok=True)
-# app.mount("/outputs", StaticFiles(directory=OUTPUT_DIR), name="outputs")
-
-# UPDATES_DIR = os.path.abspath("./updates")
-# os.makedirs(UPDATES_DIR, exist_ok=True)
-# app.mount("/updates", StaticFiles(directory=UPDATES_DIR), name="updates")
-
-
-# def save_and_build_urls(
-#     session_id: str,
-#     output_image: np.ndarray,
-#     outlines: np.ndarray,
-#     dxf_path: str,
-#     mask: np.ndarray
-# ):
-#     """Helper to save all four artifacts and return public URLs."""
-#     request_dir = os.path.join(OUTPUT_DIR, session_id)
-#     os.makedirs(request_dir, exist_ok=True)
-
-#     # filenames
-#     out_fn = "overlay.jpg"
-#     outlines_fn = "outlines.jpg"
-#     mask_fn = "mask.jpg"
-#     current_date = datetime.now().strftime("%d-%m-%Y")
-#     dxf_fn = f"out_{current_date}_{session_id}.dxf"
-
-#     # full paths
-#     out_path = os.path.join(request_dir, out_fn)
-#     outlines_path = os.path.join(request_dir, outlines_fn)
-#     mask_path = os.path.join(request_dir, mask_fn)
-#     new_dxf_path = os.path.join(request_dir, dxf_fn)
-
-#     # save images
-#     Image.fromarray(output_image).save(out_path)
-#     Image.fromarray(outlines).save(outlines_path)
-#     Image.fromarray(mask).save(mask_path)
-
-#     # copy dx file
-#     if os.path.exists(dxf_path):
-#         shutil.copy(dxf_path, new_dxf_path)
-#     else:
-#         # fallback if your DXF generator returns bytes or string
-#         with open(new_dxf_path, "wb") as f:
-#             if isinstance(dxf_path, (bytes, bytearray)):
-#                 f.write(dxf_path)
-#             else:
-#                 f.write(str(dxf_path).encode("utf-8"))
-
-#     # build URLs
-#     return {
-#         "output_image_url": f"{BASE_URL}/outputs/{session_id}/{out_fn}",
-#         "outlines_url":      f"{BASE_URL}/outputs/{session_id}/{outlines_fn}",
-#         "mask_url":          f"{BASE_URL}/outputs/{session_id}/{mask_fn}",
-#         "dxf_url":           f"{BASE_URL}/outputs/{session_id}/{dxf_fn}",
-#     }
-
-
-# @app.post("/predict1")
-# async def predict1_api(
-#     file: UploadFile = File(...)
-# ):
-#     """
-#     Simple predict: only image → overlay, outlines, mask, DXF
-#     """
-#     session_id = str(uuid.uuid4())
-#     try:
-#         img_bytes = await file.read()
-#         image = np.array(Image.open(io.BytesIO(img_bytes)).convert("RGB"))
-#     except Exception:
-#         raise HTTPException(400, "Invalid image upload")
-
-#     try:
-#         start = timeit.default_timer()
-#         out_img, outlines, dxf_path, mask = predict_simple(image)
-#         elapsed = timeit.default_timer() - start
-#         print(f"[{session_id}] predict1 in {elapsed:.2f}s")
-
-#         return save_and_build_urls(session_id, out_img, outlines, dxf_path, mask)
-
-#     except Exception as e:
-#         raise HTTPException(500, f"predict1 failed: {e}")
-#     except ReferenceBoxNotDetectedError:
-#         raise HTTPException(status_code=400, detail="Error detecting reference battery! Please try again with a clearer image.")
-#     except FingerCutOverlapError:
-#         raise HTTPException(status_code=400, detail="There was an overlap with fingercuts!s Please try again to generate dxf.")
-
-
-# @app.post("/predict2")
-# async def predict2_api(
-#     file: UploadFile = File(...),
-#     enable_fillet: str = Form(..., regex="^(On|Off)$"),
-#     fillet_value_mm: float = Form(...)
-# ):
-#     """
-#     Middle predict: image + fillet toggle + fillet value → overlay, outlines, mask, DXF
-#     """
-#     session_id = str(uuid.uuid4())
-#     try:
-#         img_bytes = await file.read()
-#         image = np.array(Image.open(io.BytesIO(img_bytes)).convert("RGB"))
-#     except Exception:
-#         raise HTTPException(400, "Invalid image upload")
-
-#     try:
-#         start = timeit.default_timer()
-#         out_img, outlines, dxf_path, mask = predict_middle(
-#             image, enable_fillet, fillet_value_mm
-#         )
-#         elapsed = timeit.default_timer() - start
-#         print(f"[{session_id}] predict2 in {elapsed:.2f}s")
-
-#         return save_and_build_urls(session_id, out_img, outlines, dxf_path, mask)
-
-#     except Exception as e:
-#         raise HTTPException(500, f"predict2 failed: {e}")
-#     except ReferenceBoxNotDetectedError:
-#         raise HTTPException(status_code=400, detail="Error detecting reference battery! Please try again with a clearer image.")
-#     except FingerCutOverlapError:
-#         raise HTTPException(status_code=400, detail="There was an overlap with fingercuts!s Please try again to generate dxf.")
-
-# @app.post("/predict3")
-# async def predict3_api(
-#     file: UploadFile = File(...),
-#     enable_fillet: str = Form(..., regex="^(On|Off)$"),
-#     fillet_value_mm: float = Form(...),
-#     enable_finger_cut: str = Form(..., regex="^(On|Off)$")
-# ):
-#     """
-#     Full predict: image + fillet toggle/value + finger-cut toggle → overlay, outlines, mask, DXF
-#     """
-#     session_id = str(uuid.uuid4())
-#     try:
-#         img_bytes = await file.read()
-#         image = np.array(Image.open(io.BytesIO(img_bytes)).convert("RGB"))
-#     except Exception:
-#         raise HTTPException(400, "Invalid image upload")
-
-#     try:
-#         start = timeit.default_timer()
-#         out_img, outlines, dxf_path, mask = predict_full(
-#             image, enable_fillet, fillet_value_mm, enable_finger_cut
-#         )
-#         elapsed = timeit.default_timer() - start
-#         print(f"[{session_id}] predict3 in {elapsed:.2f}s")
-
-#         return save_and_build_urls(session_id, out_img, outlines, dxf_path, mask)
-
-#     except Exception as e:
-#         raise HTTPException(500, f"predict3 failed: {e}")
-#     except ReferenceBoxNotDetectedError:
-#         raise HTTPException(status_code=400, detail="Error detecting reference battery! Please try again with a clearer image.")
-#     except FingerCutOverlapError:
-#         raise HTTPException(status_code=400, detail="There was an overlap with fingercuts!s Please try again to generate dxf.")
-
-# @app.post("/update")
-# async def update_files(
-#     output_image: UploadFile = File(...),
-#     outlines_image: UploadFile = File(...),
-#     mask_image: UploadFile = File(...),
-#     dxf_file: UploadFile = File(...)
-# ):
-#     session_id = str(uuid.uuid4())
-#     update_dir = os.path.join(UPDATES_DIR, session_id)
-#     os.makedirs(update_dir, exist_ok=True)
-
-#     try:
-#         upload_map = {
-#             "output_image":  output_image,
-#             "outlines_image": outlines_image,
-#             "mask_image":     mask_image,
-#             "dxf_file":       dxf_file,
-#         }
-#         urls = {}
-#         for key, up in upload_map.items():
-#             fn = up.filename
-#             path = os.path.join(update_dir, fn)
-#             with open(path, "wb") as f:
-#                 shutil.copyfileobj(up.file, f)
-#             urls[key] = f"{BASE_URL}/updates/{session_id}/{fn}"
-
-#         return {"session_id": session_id, "uploaded": urls}
-
-#     except Exception as e:
-#         raise HTTPException(500, f"Update failed: {e}")
-
-
-# if __name__ == "__main__":
-#     import uvicorn
-#     port = int(os.environ.get("PORT", 8082))
-#     print(f"Starting FastAPI server on 0.0.0.0:{port}...")
-#     uvicorn.run(app, host="0.0.0.0", port=port)
-
-
-
-
-
-
-
-
-
-
 from fastapi import FastAPI, HTTPException, UploadFile, File, Form
 from pydantic import BaseModel
 import numpy as np
@@ -233,19 +8,19 @@ from fastapi.staticfiles import StaticFiles
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import FileResponse
 
-# import your three wrappers
-from app import predict_simple, predict_middle, predict_full
-
+# Import your paper-based prediction function
 from app import (
-    predict_simple, predict_middle, predict_full,
+    predict_full_paper,
     ReferenceBoxNotDetectedError,
-    FingerCutOverlapError
+    FingerCutOverlapError,
+    MultipleObjectsError,
+    NoObjectDetectedError,
+    PaperNotDetectedError
 )
 
-
 app = FastAPI()
 
-# allow CORS if needed
+# Allow CORS if needed
 app.add_middleware(
     CORSMiddleware,
     allow_origins=["*"],
@@ -268,68 +43,75 @@ app.mount("/updates", StaticFiles(directory=UPDATES_DIR), name="updates")
 
 def save_and_build_urls(
     session_id: str,
-    output_image: np.ndarray,
-    outlines: np.ndarray,
     dxf_path: str,
-    mask: np.ndarray,
-    endpoint_type: str,
-    fillet_value: float = None,
-    finger_cut: str = None
+    output_image: np.ndarray = None,
+    outlines: np.ndarray = None,
+    mask: np.ndarray = None,
+    endpoint_type: str = "predict",
+    paper_size: str = None,
+    offset_value: float = None,
+    offset_unit: str = "mm",
+    finger_cut: str = "Off"
 ):
-    """Helper to save all four artifacts and return public URLs."""
+    """Helper to save all artifacts and return public URLs."""
     request_dir = os.path.join(OUTPUT_DIR, session_id)
     os.makedirs(request_dir, exist_ok=True)
 
-    # filenames
-    out_fn = "overlay.jpg"
-    outlines_fn = "outlines.jpg"
-    mask_fn = "mask.jpg"
-    
     # Get current date
     current_date = datetime.utcnow().strftime("%d-%m-%Y")
-
-
-    # Format fillet value with underscore instead of dot
-    fillet_str = f"{fillet_value:.2f}".replace(".", "_") if fillet_value is not None else None
     
-    # Determine DXF filename based on endpoint type
-    if endpoint_type == "predict1":
-        dxf_fn = f"DXF_{current_date}.dxf"
-    elif endpoint_type == "predict2":
-        dxf_fn = f"DXF_{current_date}.dxf"
-    elif endpoint_type == "predict3":
+    # Format offset value with underscore instead of dot
+    offset_str = f"{offset_value:.3f}".replace(".", "_") if offset_value is not None else "0_000"
+    
+    # Create descriptive DXF filename
+    if paper_size and offset_value is not None:
+        dxf_fn = f"DXF_{current_date}_{paper_size}_{offset_str}{offset_unit}"
+        if finger_cut == "On":
+            dxf_fn += "_fingercut"
+        dxf_fn += ".dxf"
+    else:
         dxf_fn = f"DXF_{current_date}.dxf"
 
-    # full paths
-    out_path = os.path.join(request_dir, out_fn)
-    outlines_path = os.path.join(request_dir, outlines_fn)
-    mask_path = os.path.join(request_dir, mask_fn)
+    # Full path for DXF
     new_dxf_path = os.path.join(request_dir, dxf_fn)
 
-    # save images
-    Image.fromarray(output_image).save(out_path)
-    Image.fromarray(outlines).save(outlines_path)
-    Image.fromarray(mask).save(mask_path)
-
-    # copy dxf file
+    # Copy DXF file
     if os.path.exists(dxf_path):
         shutil.copy(dxf_path, new_dxf_path)
     else:
-        # fallback if your DXF generator returns bytes or string
+        # Fallback if your DXF generator returns bytes or string
         with open(new_dxf_path, "wb") as f:
             if isinstance(dxf_path, (bytes, bytearray)):
                 f.write(dxf_path)
             else:
                 f.write(str(dxf_path).encode("utf-8"))
 
-    # build URLs with /download prefix for DXF
-    return {
-        "output_image_url": f"{BASE_URL}/outputs/{session_id}/{out_fn}",
-        "outlines_url":     f"{BASE_URL}/outputs/{session_id}/{outlines_fn}",
-        "mask_url":         f"{BASE_URL}/outputs/{session_id}/{mask_fn}",
-        "dxf_url":          f"{BASE_URL}/download/{session_id}/{dxf_fn}",  # Changed to use download endpoint
+    urls = {
+        "dxf_url": f"{BASE_URL}/download/{session_id}/{dxf_fn}",
     }
 
+    # Save optional images if provided
+    if output_image is not None:
+        out_fn = "annotated_image.jpg"
+        out_path = os.path.join(request_dir, out_fn)
+        Image.fromarray(output_image).save(out_path)
+        urls["output_image_url"] = f"{BASE_URL}/outputs/{session_id}/{out_fn}"
+
+    if outlines is not None:
+        outlines_fn = "outlines.jpg"
+        outlines_path = os.path.join(request_dir, outlines_fn)
+        Image.fromarray(outlines).save(outlines_path)
+        urls["outlines_url"] = f"{BASE_URL}/outputs/{session_id}/{outlines_fn}"
+
+    if mask is not None:
+        mask_fn = "mask.jpg"
+        mask_path = os.path.join(request_dir, mask_fn)
+        Image.fromarray(mask).save(mask_path)
+        urls["mask_url"] = f"{BASE_URL}/outputs/{session_id}/{mask_fn}"
+
+    return urls
+
+
 # Add new endpoint for downloading DXF files
 @app.get("/download/{session_id}/{filename}")
 async def download_file(session_id: str, filename: str):
@@ -345,13 +127,14 @@ async def download_file(session_id: str, filename: str):
     )
 
 
-@app.post("/predict1")
-async def predict1_api(
-    file: UploadFile = File(...)
+@app.post("/predict_paper_simple")
+async def predict_paper_simple_api(
+    file: UploadFile = File(...),
+    paper_size: str = Form(..., regex="^(A4|A3|US Letter)$"),
 ):
     """
-    Simple predict: only image → overlay, outlines, mask, DXF
-    DXF naming format: DXF_DD-MM-YYYY.dxf
+    Simple paper-based predict: image + paper size → DXF only
+    Default: 0mm offset, no finger cuts
     """
     session_id = str(uuid.uuid4())
     try:
@@ -362,37 +145,57 @@ async def predict1_api(
 
     try:
         start = timeit.default_timer()
-        out_img, outlines, dxf_path, mask = predict_simple(image)
+        
+        # Call predict_full_paper with default values
+        dxf_path, ann_img, outlines_img, mask_img, scale_info = predict_full_paper(
+            image=image,
+            paper_size=paper_size,
+            offset_value_mm=0.0,  # No offset
+            offset_unit="mm",
+            enable_finger_cut="Off",  # No finger cuts
+            selected_outputs=[]  # DXF only
+        )
+        
         elapsed = timeit.default_timer() - start
-        print(f"[{session_id}] predict1 in {elapsed:.2f}s")
+        print(f"[{session_id}] predict_paper_simple in {elapsed:.2f}s - {scale_info}")
 
-        return save_and_build_urls(
+        urls = save_and_build_urls(
             session_id=session_id,
-            output_image=out_img,
-            outlines=outlines,
             dxf_path=dxf_path,
-            mask=mask,
-            endpoint_type="predict1"
+            endpoint_type="predict_paper_simple",
+            paper_size=paper_size,
+            offset_value=0.0,
+            offset_unit="mm",
+            finger_cut="Off"
         )
-
-    except ReferenceBoxNotDetectedError:
-        raise HTTPException(status_code=400, detail="Error detecting reference battery! Please try again with a clearer image.")
+        
+        # Add scaling info to response
+        urls["scale_info"] = scale_info
+        return urls
+
+    except (ReferenceBoxNotDetectedError, PaperNotDetectedError):
+        raise HTTPException(status_code=400, detail="Error detecting paper! Please ensure the paper is clearly visible and try again.")
+    except (MultipleObjectsError):
+        raise HTTPException(status_code=400, detail="Multiple objects detected! Please place only a single object on the paper.")
+    except (NoObjectDetectedError):
+        raise HTTPException(status_code=400, detail="No object detected! Please ensure an object is placed on the paper.")
     except FingerCutOverlapError:
         raise HTTPException(status_code=400, detail="There was an overlap with fingercuts! Please try again to generate dxf.")
-    except HTTPException as e:
-        raise e
     except Exception as e:
-        raise HTTPException(status_code=500, detail="Error detecting reference battery! Please try again with a clearer image.")
+        print(f"Error in predict_paper_simple: {str(e)}")
+        raise HTTPException(status_code=500, detail="Error processing image! Please try again with a clearer image.")
 
-@app.post("/predict2")
-async def predict2_api(
+
+@app.post("/predict_paper_with_offset")
+async def predict_paper_with_offset_api(
     file: UploadFile = File(...),
-    enable_fillet: str = Form(..., regex="^(On|Off)$"),
-    fillet_value_mm: float = Form(...)
+    paper_size: str = Form(..., regex="^(A4|A3|US Letter)$"),
+    offset_value: float = Form(...),
+    offset_unit: str = Form(..., regex="^(mm|inches)$"),
+    include_images: bool = Form(False)  # Optional: include preview images
 ):
     """
-    Middle predict: image + fillet toggle + fillet value → overlay, outlines, mask, DXF
-    DXF naming format: DXF_DD-MM-YYYY_fillet-value_mm.dxf
+    Paper-based predict with offset: image + paper size + offset → DXF + optional images
     """
     session_id = str(uuid.uuid4())
     try:
@@ -401,44 +204,70 @@ async def predict2_api(
     except Exception:
         raise HTTPException(400, "Invalid image upload")
 
+    # Validate offset
+    if offset_value < 0:
+        raise HTTPException(400, "Offset value cannot be negative")
+    if offset_value > 50:  # Reasonable upper limit
+        raise HTTPException(400, "Offset value too large (max 50)")
+
     try:
         start = timeit.default_timer()
-        out_img, outlines, dxf_path, mask = predict_middle(
-            image, enable_fillet, fillet_value_mm
+        
+        # Determine which outputs to include
+        selected_outputs = ["Annotated Image", "Outlines", "Mask"] if include_images else []
+        
+        dxf_path, ann_img, outlines_img, mask_img, scale_info = predict_full_paper(
+            image=image,
+            paper_size=paper_size,
+            offset_value_mm=offset_value,
+            offset_unit=offset_unit,
+            enable_finger_cut="Off",  # No finger cuts
+            selected_outputs=selected_outputs
         )
+        
         elapsed = timeit.default_timer() - start
-        print(f"[{session_id}] predict2 in {elapsed:.2f}s")
+        print(f"[{session_id}] predict_paper_with_offset in {elapsed:.2f}s - {scale_info}")
 
-        return save_and_build_urls(
+        urls = save_and_build_urls(
             session_id=session_id,
-            output_image=out_img,
-            outlines=outlines,
             dxf_path=dxf_path,
-            mask=mask,
-            endpoint_type="predict2",
-            fillet_value=fillet_value_mm
+            output_image=ann_img if include_images else None,
+            outlines=outlines_img if include_images else None,
+            mask=mask_img if include_images else None,
+            endpoint_type="predict_paper_with_offset",
+            paper_size=paper_size,
+            offset_value=offset_value,
+            offset_unit=offset_unit,
+            finger_cut="Off"
         )
-
-    except ReferenceBoxNotDetectedError:
-        raise HTTPException(status_code=400, detail="Error detecting reference battery! Please try again with a clearer image.")
+        
+        urls["scale_info"] = scale_info
+        return urls
+
+    except (ReferenceBoxNotDetectedError, PaperNotDetectedError):
+        raise HTTPException(status_code=400, detail="Error detecting paper! Please ensure the paper is clearly visible and try again.")
+    except (MultipleObjectsError):
+        raise HTTPException(status_code=400, detail="Multiple objects detected! Please place only a single object on the paper.")
+    except (NoObjectDetectedError):
+        raise HTTPException(status_code=400, detail="No object detected! Please ensure an object is placed on the paper.")
     except FingerCutOverlapError:
         raise HTTPException(status_code=400, detail="There was an overlap with fingercuts! Please try again to generate dxf.")
-    except HTTPException as e:
-        raise e
     except Exception as e:
-        raise HTTPException(status_code=500, detail="Error detecting reference battery! Please try again with a clearer image.")
+        print(f"Error in predict_paper_with_offset: {str(e)}")
+        raise HTTPException(status_code=500, detail="Error processing image! Please try again with a clearer image.")
 
 
-@app.post("/predict3")
-async def predict3_api(
+@app.post("/predict_paper_full")
+async def predict_paper_full_api(
     file: UploadFile = File(...),
-    enable_fillet: str = Form(..., regex="^(On|Off)$"),
-    fillet_value_mm: float = Form(...),
-    enable_finger_cut: str = Form(..., regex="^(On|Off)$")
+    paper_size: str = Form(..., regex="^(A4|A3|US Letter)$"),
+    offset_value: float = Form(...),
+    offset_unit: str = Form(..., regex="^(mm|inches)$"),
+    enable_finger_cut: str = Form(..., regex="^(On|Off)$"),
+    include_images: bool = Form(False)  # Optional: include preview images
 ):
     """
-    Full predict: image + fillet toggle/value + finger-cut toggle → overlay, outlines, mask, DXF
-    DXF naming format: DXF_DD-MM-YYYY_fillet-value_mm_fingercut-On|Off.dxf
+    Full paper-based predict: image + paper size + offset + finger cuts → DXF + optional images
     """
     session_id = str(uuid.uuid4())
     try:
@@ -447,33 +276,112 @@ async def predict3_api(
     except Exception:
         raise HTTPException(400, "Invalid image upload")
 
+    # Validate offset
+    if offset_value < 0:
+        raise HTTPException(400, "Offset value cannot be negative")
+    if offset_value > 50:
+        raise HTTPException(400, "Offset value too large (max 50)")
+
     try:
         start = timeit.default_timer()
-        out_img, outlines, dxf_path, mask = predict_full(
-            image, enable_fillet, fillet_value_mm, enable_finger_cut
+        
+        # Determine which outputs to include
+        selected_outputs = ["Annotated Image", "Outlines", "Mask"] if include_images else []
+        
+        dxf_path, ann_img, outlines_img, mask_img, scale_info = predict_full_paper(
+            image=image,
+            paper_size=paper_size,
+            offset_value_mm=offset_value,
+            offset_unit=offset_unit,
+            enable_finger_cut=enable_finger_cut,
+            selected_outputs=selected_outputs
         )
+        
         elapsed = timeit.default_timer() - start
-        print(f"[{session_id}] predict3 in {elapsed:.2f}s")
+        print(f"[{session_id}] predict_paper_full in {elapsed:.2f}s - {scale_info}")
 
-        return save_and_build_urls(
+        urls = save_and_build_urls(
             session_id=session_id,
-            output_image=out_img,
-            outlines=outlines,
             dxf_path=dxf_path,
-            mask=mask,
-            endpoint_type="predict3",
-            fillet_value=fillet_value_mm,
+            output_image=ann_img if include_images else None,
+            outlines=outlines_img if include_images else None,
+            mask=mask_img if include_images else None,
+            endpoint_type="predict_paper_full",
+            paper_size=paper_size,
+            offset_value=offset_value,
+            offset_unit=offset_unit,
             finger_cut=enable_finger_cut
         )
-
-    except ReferenceBoxNotDetectedError:
-        raise HTTPException(status_code=400, detail="Error detecting reference battery! Please try again with a clearer image.")
+        
+        urls["scale_info"] = scale_info
+        return urls
+
+    except (ReferenceBoxNotDetectedError, PaperNotDetectedError):
+        raise HTTPException(status_code=400, detail="Error detecting paper! Please ensure the paper is clearly visible and try again.")
+    except (MultipleObjectsError):
+        raise HTTPException(status_code=400, detail="Multiple objects detected! Please place only a single object on the paper.")
+    except (NoObjectDetectedError):
+        raise HTTPException(status_code=400, detail="No object detected! Please ensure an object is placed on the paper.")
     except FingerCutOverlapError:
         raise HTTPException(status_code=400, detail="There was an overlap with fingercuts! Please try again to generate dxf.")
-    except HTTPException as e:
-        raise e
     except Exception as e:
-        raise HTTPException(status_code=500, detail="Error detecting reference battery! Please try again with a clearer image.")
+        print(f"Error in predict_paper_full: {str(e)}")
+        raise HTTPException(status_code=500, detail="Error processing image! Please try again with a clearer image.")
+
+
+# Keep the legacy endpoints for backward compatibility (optional)
+@app.post("/predict1")
+async def predict1_api(
+    file: UploadFile = File(...)
+):
+    """
+    Legacy endpoint - redirects to simple paper-based prediction with A4 default
+    """
+    return await predict_paper_simple_api(file=file, paper_size="A4")
+
+
+@app.post("/predict2")
+async def predict2_api(
+    file: UploadFile = File(...),
+    enable_fillet: str = Form(..., regex="^(On|Off)$"),
+    fillet_value_mm: float = Form(...)
+):
+    """
+    Legacy endpoint - redirects to paper-based prediction with offset
+    Note: Fillet functionality mapped to offset for compatibility
+    """
+    # Map fillet to offset (you might want to adjust this logic)
+    offset_value = fillet_value_mm if enable_fillet == "On" else 0.0
+    
+    return await predict_paper_with_offset_api(
+        file=file,
+        paper_size="A4",  # Default to A4
+        offset_value=offset_value,
+        offset_unit="mm",
+        include_images=True
+    )
+
+
+@app.post("/predict3")
+async def predict3_api(
+    file: UploadFile = File(...),
+    enable_fillet: str = Form(..., regex="^(On|Off)$"),
+    fillet_value_mm: float = Form(...),
+    enable_finger_cut: str = Form(..., regex="^(On|Off)$")
+):
+    """
+    Legacy endpoint - redirects to full paper-based prediction
+    """
+    offset_value = fillet_value_mm if enable_fillet == "On" else 0.0
+    
+    return await predict_paper_full_api(
+        file=file,
+        paper_size="A4",  # Default to A4
+        offset_value=offset_value,
+        offset_unit="mm",
+        enable_finger_cut=enable_finger_cut,
+        include_images=True
+    )
 
 
 @app.post("/update")
@@ -515,11 +423,23 @@ def health():
     return Response(content="OK", status_code=200)
 
 
+@app.get("/")
+def root():
+    return {
+        "message": "Paper-based DXF Generator API",
+        "endpoints": [
+            "/predict_paper_simple - Simple DXF generation with paper reference",
+            "/predict_paper_with_offset - DXF generation with contour offset",
+            "/predict_paper_full - Full DXF generation with all features",
+            "/predict1, /predict2, /predict3 - Legacy endpoints (backward compatibility)"
+        ],
+        "paper_sizes": ["A4", "A3", "US Letter"],
+        "units": ["mm", "inches"]
+    }
+
+
 if __name__ == "__main__":
     import uvicorn
     port = int(os.environ.get("PORT", 8080))
     print(f"Starting FastAPI server on 0.0.0.0:{port}...")
-    uvicorn.run(app, host="0.0.0.0", port=port)
-
-
-
+    uvicorn.run(app, host="0.0.0.0", port=port)