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app_py.py

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+import os
+import gradio as gr
+import numpy as np
+from PIL import Image
+from ultralytics import YOLO
+from huggingface_hub import hf_hub_download
+
+# ── Model ──────────────────────────────────────────────────────────────────────
+MODEL_REPO = "DannyLuna/recaptcha-classification-57k"
+MODEL_FILE = "recaptcha_classification_57k.onnx"
+MODEL_PATH = os.path.join("models", MODEL_FILE)
+
+os.makedirs("models", exist_ok=True)
+
+if not os.path.exists(MODEL_PATH):
+    print(f"[INFO] Downloading {MODEL_FILE} from {MODEL_REPO}...")
+    hf_hub_download(
+        repo_id=MODEL_REPO,
+        filename=MODEL_FILE,
+        local_dir="models",
+    )
+    print("[INFO] Download complete.")
+
+model = YOLO(MODEL_PATH, task="classify")
+print(f"[INFO] Model loaded. Classes: {list(model.names.values())}")
+
+# Classes that the solver considers "real" matches (not background)
+TARGET_CLASSES = {
+    "bicycle", "bridge", "bus", "car", "chimney", "crosswalk",
+    "fire hydrant", "motorcycle", "mountain", "palm tree",
+    "stairs", "tractor", "traffic light",
+}
+
+# ── Inference ──────────────────────────────────────────────────────────────────
+def predict(image: Image.Image):
+    if image is None:
+        return {}, "⚠️ No image provided."
+
+    results = model(image, verbose=False)
+    probs   = results[0].probs
+
+    # Build full confidence dict for the label widget
+    conf_dict = {
+        model.names[i]: float(probs.data[i])
+        for i in range(len(model.names))
+    }
+
+    top1_name = model.names[int(probs.top1)]
+    top1_conf = float(probs.top1conf)
+    is_target = top1_name in TARGET_CLASSES
+
+    label_emoji = "✅" if is_target else "🚫"
+    status = (
+        f"{label_emoji} **{top1_name.upper()}** — {top1_conf:.1%} confidence"
+        + ("" if is_target else "\n*(classified as `other` / background)*")
+    )
+
+    return conf_dict, status
+
+
+# ── UI ─────────────────────────────────────────────────────────────────────────
+CLASSES_MD = ", ".join(f"`{c}`" for c in sorted(TARGET_CLASSES))
+
+with gr.Blocks(title="reCAPTCHA Classifier", theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        f"""
+# 🔍 reCAPTCHA Image Classifier
+YOLO-based classification model fine-tuned on **57k reCAPTCHA tiles**.  
+Upload a tile image and the model will predict which class it belongs to.
+
+**Target classes:** {CLASSES_MD} + `other`
+"""
+    )
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            img_input = gr.Image(type="pil", label="Upload tile image")
+            run_btn   = gr.Button("🚀 Classify", variant="primary")
+
+        with gr.Column(scale=1):
+            status_out = gr.Markdown(label="Top prediction")
+            label_out  = gr.Label(
+                num_top_classes=5,
+                label="Top-5 class probabilities",
+            )
+
+    run_btn.click(fn=predict, inputs=img_input, outputs=[label_out, status_out])
+    img_input.change(fn=predict, inputs=img_input, outputs=[label_out, status_out])
+
+    gr.Examples(
+        examples=[],   # drop your own example images here
+        inputs=img_input,
+    )
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)

dockerfile (4).txt

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+# ── Base ───────────────────────────────────────────────────────────────────────
+# slim image keeps the layer small; HF free tier is CPU-only
+FROM python:3.11-slim
+
+# ── System deps ────────────────────────────────────────────────────────────────
+# libgl1 + libglib2.0-0 are required by OpenCV (pulled in by ultralytics)
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        libgl1 \
+        libglib2.0-0 \
+    && rm -rf /var/lib/apt/lists/*
+
+# ── HF Spaces user (uid 1000) ──────────────────────────────────────────────────
+# Spaces runs as a non-root user; pre-create it so chown works
+RUN useradd -m -u 1000 user
+USER user
+
+ENV HOME=/home/user \
+    PATH="/home/user/.local/bin:$PATH" \
+    PYTHONUNBUFFERED=1 \
+    # tell HF hub to cache inside the writable home dir
+    HF_HOME=/home/user/.cache/huggingface \
+    # disable YOLO's attempt to write analytics / config to /root
+    YOLO_CONFIG_DIR=/home/user/.config/ultralytics
+
+WORKDIR /home/user/app
+
+# ── Python deps ────────────────────────────────────────────────────────────────
+# Copy requirements first so Docker can cache this layer
+COPY --chown=user requirements.txt .
+RUN pip install --no-cache-dir --upgrade pip \
+ && pip install --no-cache-dir -r requirements.txt
+
+# ── App code ───────────────────────────────────────────────────────────────────
+COPY --chown=user . .
+
+# ── Runtime ────────────────────────────────────────────────────────────────────
+EXPOSE 7860
+
+CMD ["python", "app.py"]

requirements.txt

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+# YOLO inference (CPU) — pulls in torch, torchvision, opencv-python-headless, etc.
+ultralytics>=8.3.0
+
+# ONNX runtime for CPU inference (lighter than the full pytorch weights)
+onnxruntime>=1.18.0
+
+# HF model download
+huggingface_hub>=0.23.0
+
+# UI
+gradio>=4.44.0