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app.py

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+#!/usr/bin/env python
+# ──────────────────────────────────────────────────────────────
+#  BubbleAI Image-Safety Detector – Hugging Face Space
+#  -------------------------------------------------------------
+#  Gradio app that classifies uploaded images as “Safe” or
+#  “Unsafe” using a fine-tuned ResNet-50.
+#
+#  Coder: Amir Mehdi Memari  (2025-08-06)
+#  Description:
+#     • Loads the checkpoint `resnet_safety_classifier.pth`
+#       (must be in the same repo directory; tracked with Git-LFS).
+#     • Applies standard ImageNet preprocessing.
+#     • Returns class probabilities via a simple Gradio UI.
+#  Usage:
+#     The HF Space builder executes `python app.py` automatically.
+# ──────────────────────────────────────────────────────────────
+
+from __future__ import annotations
+import pathlib, typing as t
+
+import torch
+import torchvision
+from torchvision import transforms
+from PIL import Image
+import gradio as gr
+
+
+# ── 1. Locate checkpoint ──────────────────────────────────────
+REPO_DIR    = pathlib.Path(__file__).parent
+CKPT_PATH   = REPO_DIR / "resnet_safety_classifier.pth"
+DEVICE      = "cuda" if torch.cuda.is_available() else "cpu"
+
+
+# ── 2. Define the model architecture exactly as trained ───────
+class SafetyResNet(torch.nn.Module):
+    """
+    ResNet-50 backbone with a 2-unit classifier head.
+    """
+    def __init__(self) -> None:
+        super().__init__()
+        base = torchvision.models.resnet50(weights=None)
+        base.fc = torch.nn.Linear(base.fc.in_features, 2)   # Safe / Unsafe
+        self.model = base
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.model(x)
+
+
+# ── 3. Instantiate & load weights ─────────────────────────────
+model = SafetyResNet().to(DEVICE)
+state = torch.load(CKPT_PATH, map_location=DEVICE)
+model.load_state_dict(state, strict=True)
+model.eval()                                           # inference mode
+CLASSES = ["Safe", "Unsafe"]
+
+
+# ── 4. Pre-processing pipeline (ImageNet stats) ───────────────
+preprocess = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                         std =[0.229, 0.224, 0.225]),
+])
+
+
+# ── 5. Inference helper ───────────────────────────────────────
+@torch.inference_mode()
+def predict(img: Image.Image) -> t.Dict[str, float]:
+    """
+    Returns {class_name: probability} for a single PIL image.
+    """
+    tensor = preprocess(img).unsqueeze(0).to(DEVICE)
+    probs  = torch.softmax(model(tensor)[0], dim=0).cpu().tolist()
+    return {CLASSES[i]: float(probs[i]) for i in range(2)}
+
+
+# ── 6. Build Gradio Interface ─────────────────────────────────
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Upload an image"),
+    outputs=gr.Label(num_top_classes=2, label="Prediction"),
+    title="BubbleAI Image-Safety Detector",
+    description=(
+        "This demo classifies images as **Safe** or **Unsafe** (NSFW) "
+        "using a fine-tuned ResNet-50. Probabilities are shown for both "
+        "classes. Model weights © 2025 Amir Mehdi Memari."
+    ),
+    cache_examples=False,           # disable weighty example cache rebuilds
+)
+
+# ── 7. Launch (HF Spaces auto-calls this in production) ───────
+if __name__ == "__main__":
+    demo.launch()

clip_safety_classifier.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bd1c0a11caa8a6ebeda4bc1258f1d09be2cc8a15f19fab8b27eda0ffb1da4183
+size 605757795

requirements.txt

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+# ——— Core DL stack ———
+torch>=2.2          # runtime will auto-select the CPU build
+torchvision>=0.17   # provides ResNet + transforms
+
+# ——— App / UI ———
+gradio>=4.26        # Space UI
+pillow              # PIL image handling (pulled by torchvision, listed for clarity)

resnet_safety_classifier.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ce42fb248e341f8cbf133d1164b9453e88c088ba2fcc291f3dd8e65746598b18
+size 98558659