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Dockerfile

@@ -1,86 +1,43 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
+# AI Content Detector — OpenEnv Environment
+# HuggingFace Spaces deployment (port 7860)
 #
-# This source code is licensed under the BSD-style license found in the
-# LICENSE file in the root directory of this source tree.
+# Build:  docker build -t ai-content-detector .
+# Run:    docker run -p 7860:7860 \
+#           -e API_BASE_URL=https://api-inference.huggingface.co/v1 \
+#           -e MODEL_NAME=meta-llama/Llama-3-8b-Instruct \
+#           -e HF_TOKEN=hf_... \
+#           ai-content-detector
 
-# Multi-stage build using openenv-base
-# This Dockerfile is flexible and works for both:
-# - In-repo environments (with local OpenEnv sources)
-# - Standalone environments (with openenv from PyPI/Git)
-# The build script (openenv build) handles context detection and sets appropriate build args.
-
-ARG BASE_IMAGE=ghcr.io/meta-pytorch/openenv-base:latest
-FROM ${BASE_IMAGE} AS builder
+FROM python:3.11-slim
 
 WORKDIR /app
 
-# Ensure git is available (required for installing dependencies from VCS)
-RUN apt-get update && \
-    apt-get install -y --no-install-recommends git curl ffmpeg && \
-    rm -rf /var/lib/apt/lists/*
-
-# Build argument to control whether we're building standalone or in-repo
-ARG BUILD_MODE=in-repo
-ARG ENV_NAME=ai_content_detector_env
-
-# Copy environment code (always at root of build context)
-COPY . /app/env
+# ffmpeg is required by imageio[ffmpeg] for video support
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    curl ca-certificates ffmpeg \
+    && rm -rf /var/lib/apt/lists/*
 
-# For in-repo builds, openenv is already vendored in the build context
-# For standalone builds, openenv will be installed via pyproject.toml
-WORKDIR /app/env
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
 
-# Ensure uv is available (for local builds where base image lacks it)
-RUN if ! command -v uv >/dev/null 2>&1; then \
-        curl -LsSf https://astral.sh/uv/install.sh | sh && \
-        mv /root/.local/bin/uv /usr/local/bin/uv && \
-        mv /root/.local/bin/uvx /usr/local/bin/uvx; \
-    fi
-    
-# Install dependencies using uv sync
-# If uv.lock exists, use it; otherwise resolve on the fly
-RUN --mount=type=cache,target=/root/.cache/uv \
-    if [ -f uv.lock ]; then \
-        uv sync --frozen --no-install-project --no-editable; \
-    else \
-        uv sync --no-install-project --no-editable; \
-    fi
+COPY . .
 
-RUN --mount=type=cache,target=/root/.cache/uv \
-    if [ -f uv.lock ]; then \
-        uv sync --frozen --no-editable; \
-    else \
-        uv sync --no-editable; \
-    fi
-
-# Final runtime stage
-FROM ${BASE_IMAGE}
-
-WORKDIR /app
+# Pre-download HC3 dataset at build time for faster cold start
+# (comment out to keep image smaller; dataset downloads on first request)
+RUN python -c "from dataset import load_samples; \
+    load_samples('easy'); load_samples('medium'); load_samples('hard')" \
+    || echo 'Dataset pre-load skipped (no internet at build time)'
 
-# Copy the virtual environment from builder
-COPY --from=builder /app/env/.venv /app/.venv
-
-# Copy the environment code
-COPY --from=builder /app/env /app/env
-
-# Set PATH to use the virtual environment
-ENV PATH="/app/.venv/bin:$PATH"
-
-# Set PYTHONPATH so imports work correctly
-ENV PYTHONPATH="/app/env:$PYTHONPATH"
-
-# Health check
 # Run as non-root user
 RUN useradd -m appuser && chown -R appuser /app
 USER appuser
 
+EXPOSE 7860
+
 HEALTHCHECK --interval=30s --timeout=10s --start-period=90s --retries=3 \
     CMD curl -f http://localhost:7860/health || exit 1
 
 ENV PRELOAD=1
 
-# Run the FastAPI server on port 7860 (HuggingFace Spaces standard)
 ENV ENABLE_WEB_INTERFACE=true
-CMD ["sh", "-c", "cd /app/env && uvicorn server.app:app --host 0.0.0.0 --port 7860"]
+CMD ["uvicorn", "server.app:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -5,10 +5,10 @@ colorFrom: blue
 colorTo: blue
 sdk: docker
 pinned: false
-app_port: 8000
-base_path: /web
+app_port: 7860
 tags:
   - openenv
+base_path: /web
 ---
 
 # Ai Content Detector Env Environment
@@ -163,7 +163,7 @@ The client supports context manager usage for automatic connection management:
 from ai_content_detector_env import AiContentDetectorAction, AiContentDetectorEnv
 
 # Connect with context manager (auto-connects and closes)
-with AiContentDetectorEnv(base_url="http://localhost:8000") as env:
+with AiContentDetectorEnv(base_url="http://localhost:7860") as env:
     result = env.reset()
     print(f"Reset: {result.observation.echoed_message}")
     # Multiple steps with low latency
@@ -199,7 +199,7 @@ from ai_content_detector_env import AiContentDetectorAction, AiContentDetectorEn
 from concurrent.futures import ThreadPoolExecutor
 
 def run_episode(client_id: int):
-    with AiContentDetectorEnv(base_url="http://localhost:8000") as env:
+    with AiContentDetectorEnv(base_url="http://localhost:7860") as env:
         result = env.reset()
         for i in range(10):
             result = env.step(AiContentDetectorAction(message=f"Client {client_id}, step {i}"))

requirements.txt

@@ -0,0 +1,11 @@
+openenv-core[core]>=0.2.2
+fastapi>=0.115.0
+uvicorn[standard]>=0.29.0
+pydantic>=2.0.0
+websockets>=12.0
+python-multipart>=0.0.9
+datasets>=2.18.0
+openai>=1.20.0
+Pillow>=10.0.0
+imageio>=2.33.0
+imageio[ffmpeg]>=2.33.0

server/Dockerfile

@@ -1,42 +1,85 @@
-# AI Content Detector — OpenEnv Environment
-# HuggingFace Spaces deployment (port 7860)
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
 #
-# Build:  docker build -t ai-content-detector .
-# Run:    docker run -p 7860:7860 \
-#           -e API_BASE_URL=https://api-inference.huggingface.co/v1 \
-#           -e MODEL_NAME=meta-llama/Llama-3-8b-Instruct \
-#           -e HF_TOKEN=hf_... \
-#           ai-content-detector
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
 
-FROM python:3.11-slim
+# Multi-stage build using openenv-base
+# This Dockerfile is flexible and works for both:
+# - In-repo environments (with local OpenEnv sources)
+# - Standalone environments (with openenv from PyPI/Git)
+# The build script (openenv build) handles context detection and sets appropriate build args.
+
+ARG BASE_IMAGE=ghcr.io/meta-pytorch/openenv-base:latest
+FROM ${BASE_IMAGE} AS builder
 
 WORKDIR /app
 
-# ffmpeg is required by imageio[ffmpeg] for video support
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    curl ca-certificates ffmpeg \
-    && rm -rf /var/lib/apt/lists/*
+# Ensure git is available (required for installing dependencies from VCS)
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends git curl ffmpeg && \
+    rm -rf /var/lib/apt/lists/*
+
+# Build argument to control whether we're building standalone or in-repo
+ARG BUILD_MODE=in-repo
+ARG ENV_NAME=ai_content_detector_env
+
+# Copy environment code (always at root of build context)
+COPY . /app/env
 
-COPY requirements.txt .
-RUN pip install --no-cache-dir -r requirements.txt
+# For in-repo builds, openenv is already vendored in the build context
+# For standalone builds, openenv will be installed via pyproject.toml
+WORKDIR /app/env
 
-COPY . .
+# Ensure uv is available (for local builds where base image lacks it)
+RUN if ! command -v uv >/dev/null 2>&1; then \
+        curl -LsSf https://astral.sh/uv/install.sh | sh && \
+        mv /root/.local/bin/uv /usr/local/bin/uv && \
+        mv /root/.local/bin/uvx /usr/local/bin/uvx; \
+    fi
+    
+# Install dependencies using uv sync
+# If uv.lock exists, use it; otherwise resolve on the fly
+RUN --mount=type=cache,target=/root/.cache/uv \
+    if [ -f uv.lock ]; then \
+        uv sync --frozen --no-install-project --no-editable; \
+    else \
+        uv sync --no-install-project --no-editable; \
+    fi
 
-# Pre-download HC3 dataset at build time for faster cold start
-# (comment out to keep image smaller; dataset downloads on first request)
-RUN python -c "from dataset import load_samples; \
-    load_samples('easy'); load_samples('medium'); load_samples('hard')" \
-    || echo 'Dataset pre-load skipped (no internet at build time)'
+RUN --mount=type=cache,target=/root/.cache/uv \
+    if [ -f uv.lock ]; then \
+        uv sync --frozen --no-editable; \
+    else \
+        uv sync --no-editable; \
+    fi
+
+# Final runtime stage
+FROM ${BASE_IMAGE}
+
+WORKDIR /app
 
+# Copy the virtual environment from builder
+COPY --from=builder /app/env/.venv /app/.venv
+
+# Copy the environment code
+COPY --from=builder /app/env /app/env
+
+# Set PATH to use the virtual environment
+ENV PATH="/app/.venv/bin:$PATH"
+
+# Set PYTHONPATH so imports work correctly
+ENV PYTHONPATH="/app/env:$PYTHONPATH"
+
+# Health check
 # Run as non-root user
 RUN useradd -m appuser && chown -R appuser /app
 USER appuser
 
-EXPOSE 7860
-
 HEALTHCHECK --interval=30s --timeout=10s --start-period=90s --retries=3 \
     CMD curl -f http://localhost:7860/health || exit 1
 
 ENV PRELOAD=1
 
-CMD ["uvicorn", "server.app:app", "--host", "0.0.0.0", "--port", "7860"]
+# Run the FastAPI server on port 7860 (HuggingFace Spaces standard)
+CMD ["sh", "-c", "cd /app/env && uvicorn server.app:app --host 0.0.0.0 --port 7860"]

server/requirements.txt

@@ -1,4 +1,4 @@
-openenv[core]>=0.2.0
+openenv-core[core]>=0.2.2
 fastapi>=0.115.0
 uvicorn[standard]>=0.29.0
 pydantic>=2.0.0