Upload folder using huggingface_hub (part 22)

Dockerfile

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+# Tri-Netra AI AI - HuggingFace Spaces (Docker SDK) image.
+#
+# Build target: a small public demo of the layered brain-MRI tumor pipeline.
+# Inference is ONNX-only (~430 MB total for v3 UNet + T1c specialist + 3
+# classifiers), so CPU on free Spaces is fast enough (~30-40 ms/image).
+# The LLM explanation defaults to HuggingFace Inference Providers (open-weight
+# Llama 3.3 70B + Gemma 3 27B IT via $HF_TOKEN Space secret); if no token is
+# set the dashboard falls back to the deterministic radiology report which is
+# rich on its own and zero-hallucination by construction.
+#
+# Model weights are NOT bundled into this image (the HF Space free-tier 1 GB
+# repo cap is too small). dashboard.py downloads them from a separate HF
+# Model repo (default: anannyavyas1/Tri-Netra-AI-Models) on first boot via
+# huggingface_hub. See scripts/upload_models_to_hf.py for how to populate
+# that Model repo from your local .pt -> .onnx exports.
+
+FROM python:3.11-slim
+
+# System deps: libgl/libglib for OpenCV (cv2), libgomp for ONNXruntime
+# parallelism, curl for the Spaces health probe.
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    libgl1 \
+    libglib2.0-0 \
+    libgomp1 \
+    curl \
+ && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+# Install Python deps first so Docker layer caching speeds up rebuilds when
+# code changes but requirements don't.
+COPY requirements-spaces.txt /app/requirements-spaces.txt
+RUN pip install --no-cache-dir --upgrade pip \
+ && pip install --no-cache-dir -r requirements-spaces.txt
+
+# Copy application code. We intentionally do NOT copy datasets, training
+# scripts, or any model weights; only what the dashboard actually needs at
+# request time. ONNX weights are fetched from anannyavyas1/Tri-Netra-AI-Models on
+# first boot - see _ensure_onnx_models_downloaded() in dashboard.py.
+COPY dashboard.py /app/dashboard.py
+COPY src /app/src
+COPY web_dashboard /app/web_dashboard
+# Empty per-model directories so .pt/.onnx downloads land in predictable
+# paths (find_weights_path searches these). Also drop in any small
+# metrics .json that's present (for /metrics) - missing is fine.
+COPY real_eval_current /app/real_eval_current
+COPY segmentation_artifacts /app/segmentation_artifacts
+
+# Spaces convention: PORT=7860 and bind 0.0.0.0. The dashboard reads both from
+# environment so no CLI flag is needed.
+ENV PORT=7860 \
+    HOST=0.0.0.0 \
+    PYTHONUNBUFFERED=1 \
+    PYTHONIOENCODING=utf-8 \
+    LOG_LEVEL=INFO
+
+# Prefer ONNX over PyTorch on the inference path. Override with ONNX_DISABLE=1
+# to debug-fall-back to the PyTorch path.
+ENV ONNX_DISABLE=
+
+# LLM defaults for the public demo. The deployer adds HF_TOKEN as a Space
+# secret to enable the layered LLM pipeline; without it the dashboard returns
+# the deterministic radiology report (still very rich + zero hallucinations).
+ENV HF_MODEL_TEXT="meta-llama/Llama-3.3-70B-Instruct" \
+    HF_MODEL_VISION="google/gemma-3-27b-it"
+
+# Where to fetch ONNX weights from on first boot. Point at your own Model
+# repo if you forked.
+ENV HF_MODELS_REPO="anannyavyas1/Tri-Netra-AI-Models"
+
+EXPOSE 7860
+
+# HF Spaces health probes /health (we expose it explicitly in dashboard.py).
+HEALTHCHECK --interval=30s --timeout=5s --start-period=20s --retries=3 \
+    CMD curl -fs http://localhost:${PORT}/health || exit 1
+
+CMD ["python", "dashboard.py"]

Documentation/NeuroLens AI A Comparative Deep Learning Framework.pdf

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