Create app.py

app.py

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+import os, io, shutil, zipfile, hashlib, json
+from pathlib import Path
+from typing import List, Dict, Tuple
+
+
+import gradio as gr
+import numpy as np
+from PIL import Image
+import cv2
+
+
+import torch
+from transformers import BlipProcessor, BlipForConditionalGeneration
+
+
+# -------- Settings --------
+DEFAULT_MODEL = "Salesforce/blip-image-captioning-base" # CPU friendly
+BIG_MODEL = "Salesforce/blip-image-captioning-large" # better on T4/A10G
+
+
+# Blur threshold: lower => more tolerant (keep blurrier images)
+BLUR_VAR_THRESHOLD = 100.0
+
+
+# Work dirs inside the Space container
+ROOT = Path("workspace")
+IMAGES_DIR = ROOT / "images"
+EXPORT_DIR = ROOT / "export"
+ROOT.mkdir(parents=True, exist_ok=True)
+IMAGES_DIR.mkdir(parents=True, exist_ok=True)
+EXPORT_DIR.mkdir(parents=True, exist_ok=True)
+
+
+# -------- Utilities --------
+
+
+def clear_workspace():
+if IMAGES_DIR.exists():
+shutil.rmtree(IMAGES_DIR)
+if EXPORT_DIR.exists():
+shutil.rmtree(EXPORT_DIR)
+IMAGES_DIR.mkdir(parents=True, exist_ok=True)
+EXPORT_DIR.mkdir(parents=True, exist_ok=True)
+
+
+
+
+def is_image(fname: str) -> bool:
+ext = str(fname).lower().split(".")[-1]
+return ext in ["jpg", "jpeg", "png", "bmp", "webp"]
+
+
+
+
+def laplacian_var_blur(pil_img: Image.Image) -> float:
+arr = np.array(pil_img.convert("L"))
+fm = cv2.Laplacian(arr, cv2.CV_64F).var()
+return float(fm)
+
+
+
+
+def dhash(pil_img: Image.Image, hash_size: int = 8) -> str:
+# Perceptual dHash for near-duplicate filtering
+img = pil_img.convert("L").resize((hash_size + 1, hash_size), Image.LANCZOS)
+diff = np.array(img)[:, 1:] > np.array(img)[:, :-1]
+return ''.join('1' if v else '0' for v in diff.flatten())
+
+
+
+
+def save_uploaded_files(files: List[gr.File]) -> List[str]:
+saved = []
+for f in files:
+if f is None:
+continue
+name = os.path.basename(f.name)
+dst = IMAGES_DIR / name
+shutil.copy(f.name, dst)
+saved.append(str(dst))
+return saved
+
+
+
+
+def unzip_to_images(zbytes: bytes) -> List[str]:
+saved = []
+with zipfile.ZipFile(io.BytesIO(zbytes)) as zf:
+demo.launch()