Add Pillow fallback when pyvips/libvips unavailable

pyvips fails on HF Spaces if libvips shared lib is missing.
Now tries pyvips at import time, falls back to Pillow+numpy
with the same concurrent fetching and vectorised normalisation.
Both paths share urllib3 connection pooling and ThreadPoolExecutor.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

app.py

@@ -1,6 +1,5 @@
 import torch
 import numpy as np
-import pyvips
 import gradio as gr
 from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
@@ -12,6 +11,17 @@ import urllib3
 import os
 import json
 import random
+from io import BytesIO
+
+# ── pyvips with Pillow fallback ─────────────────────────────────────────
+try:
+    import pyvips
+    USE_VIPS = True
+    print("Using pyvips for image processing.")
+except Exception:
+    USE_VIPS = False
+    from PIL import Image, ImageOps
+    print("pyvips not available, falling back to Pillow.")
 
 from model import MobileCLIPRanker
 
@@ -83,42 +93,87 @@ if DEVICE == "cuda" and hasattr(torch, "compile"):
         pass
 
 
-# ── Image processing (pyvips) ──────────────────────────────────────────
+# ── Image processing ────────────────────────────────────────────────────
+def _fetch_bytes(url: str):
+    """Fetch raw bytes from URL via connection-pooled HTTP."""
+    resp = http_pool.request("GET", url, preload_content=True)
+    if resp.status != 200:
+        return None
+    return resp.data
+
+
+def _preprocess_vips(data: bytes):
+    """pyvips path: shrink-on-load thumbnail + letterbox pad."""
+    img = pyvips.Image.thumbnail_buffer(data, IMG_SIZE, height=IMG_SIZE)
+    if img.bands == 4:
+        img = img.flatten(background=[128, 128, 128])
+    elif img.bands == 1:
+        img = img.colourspace("srgb")
+    if img.width != IMG_SIZE or img.height != IMG_SIZE:
+        img = img.gravity(
+            "centre", IMG_SIZE, IMG_SIZE,
+            extend="background", background=[128, 128, 128],
+        )
+    arr = np.ndarray(
+        buffer=img.write_to_memory(), dtype=np.uint8,
+        shape=(IMG_SIZE, IMG_SIZE, 3),
+    )
+    return arr
+
+
+def _preprocess_pil(data: bytes):
+    """Pillow path: thumbnail + letterbox pad."""
+    img = Image.open(BytesIO(data)).convert("RGB")
+    img.thumbnail((IMG_SIZE, IMG_SIZE), Image.Resampling.BICUBIC)
+    delta_w = IMG_SIZE - img.size[0]
+    delta_h = IMG_SIZE - img.size[1]
+    padding = (delta_w // 2, delta_h // 2,
+               delta_w - delta_w // 2, delta_h - delta_h // 2)
+    img = ImageOps.expand(img, padding, fill=(128, 128, 128))
+    return np.asarray(img, dtype=np.uint8)
+
+
+_preprocess = _preprocess_vips if USE_VIPS else _preprocess_pil
+
+
 def _fetch_and_preprocess(url: str):
     """Fetch one image, letterbox-resize, normalise -> CHW float32 numpy."""
     try:
         if url.startswith("http"):
-            resp = http_pool.request("GET", url, preload_content=True)
-            if resp.status != 200:
+            data = _fetch_bytes(url)
+            if data is None:
                 return None
-            # thumbnail_buffer uses shrink-on-load (fast JPEG decode)
-            img = pyvips.Image.thumbnail_buffer(
-                resp.data, IMG_SIZE, height=IMG_SIZE
-            )
+            arr = _preprocess(data)
         else:
-            img = pyvips.Image.thumbnail(url, IMG_SIZE, height=IMG_SIZE)
-
-        # Ensure 3-band sRGB
-        if img.bands == 4:
-            img = img.flatten(background=[128, 128, 128])
-        elif img.bands == 1:
-            img = img.colourspace("srgb")
-
-        # Letterbox pad to exact IMG_SIZE x IMG_SIZE
-        if img.width != IMG_SIZE or img.height != IMG_SIZE:
-            img = img.gravity(
-                "centre", IMG_SIZE, IMG_SIZE,
-                extend="background", background=[128, 128, 128],
-            )
+            if USE_VIPS:
+                img = pyvips.Image.thumbnail(url, IMG_SIZE, height=IMG_SIZE)
+                if img.bands == 4:
+                    img = img.flatten(background=[128, 128, 128])
+                elif img.bands == 1:
+                    img = img.colourspace("srgb")
+                if img.width != IMG_SIZE or img.height != IMG_SIZE:
+                    img = img.gravity(
+                        "centre", IMG_SIZE, IMG_SIZE,
+                        extend="background", background=[128, 128, 128],
+                    )
+                arr = np.ndarray(
+                    buffer=img.write_to_memory(), dtype=np.uint8,
+                    shape=(IMG_SIZE, IMG_SIZE, 3),
+                )
+            else:
+                img = Image.open(url).convert("RGB")
+                img.thumbnail((IMG_SIZE, IMG_SIZE), Image.Resampling.BICUBIC)
+                dw = IMG_SIZE - img.size[0]
+                dh = IMG_SIZE - img.size[1]
+                img = ImageOps.expand(
+                    img, (dw // 2, dh // 2, dw - dw // 2, dh - dh // 2),
+                    fill=(128, 128, 128),
+                )
+                arr = np.asarray(img, dtype=np.uint8)
 
-        # -> float32 CHW normalised numpy
-        arr = np.ndarray(
-            buffer=img.write_to_memory(),
-            dtype=np.uint8,
-            shape=(IMG_SIZE, IMG_SIZE, 3),
-        )
+        # float32 CHW normalised
         arr = (arr.astype(np.float32) * (1.0 / 255.0) - MEAN) * INV_STD
-        return arr.transpose(2, 0, 1)  # HWC -> CHW
+        return arr.transpose(2, 0, 1)
     except Exception:
         return None
 
@@ -127,19 +182,31 @@ def _fetch_display(url: str):
     """Fetch image for Gradio display -> numpy uint8 HWC."""
     try:
         if url.startswith("http"):
-            resp = http_pool.request("GET", url, preload_content=True)
-            img = pyvips.Image.new_from_buffer(resp.data, "")
+            data = _fetch_bytes(url)
+            if data is None:
+                return None
         else:
-            img = pyvips.Image.new_from_file(url, access="sequential")
-        if img.bands == 4:
-            img = img.flatten(background=[255, 255, 255])
-        elif img.bands == 1:
-            img = img.colourspace("srgb")
-        return np.ndarray(
-            buffer=img.write_to_memory(),
-            dtype=np.uint8,
-            shape=(img.height, img.width, 3),
-        )
+            data = None
+
+        if USE_VIPS:
+            if data is not None:
+                img = pyvips.Image.new_from_buffer(data, "")
+            else:
+                img = pyvips.Image.new_from_file(url, access="sequential")
+            if img.bands == 4:
+                img = img.flatten(background=[255, 255, 255])
+            elif img.bands == 1:
+                img = img.colourspace("srgb")
+            return np.ndarray(
+                buffer=img.write_to_memory(), dtype=np.uint8,
+                shape=(img.height, img.width, 3),
+            )
+        else:
+            if data is not None:
+                img = Image.open(BytesIO(data)).convert("RGB")
+            else:
+                img = Image.open(url).convert("RGB")
+            return np.asarray(img, dtype=np.uint8)
     except Exception:
         return None
 
@@ -224,7 +291,7 @@ def gradio_wrapper(text_input):
 
 with gr.Blocks() as demo:
     gr.Markdown("# Real Estate Image Ranker")
-    gr.Markdown("**MobileCLIP2-L14** fine-tuned ranker with pyvips acceleration.")
+    gr.Markdown("**MobileCLIP2-L14** fine-tuned ranker.")
     with gr.Row():
         with gr.Column(scale=1):
             gr.Markdown("### 1. Select Data")

packages.txt

@@ -1 +1,3 @@
+libvips
 libvips-dev
+libvips-tools

requirements.txt

@@ -8,5 +8,6 @@ pyyaml
 huggingface_hub
 timm
 open_clip_torch
+pillow
 pyvips
 urllib3