Use custom Dockerfile for TensorFlow 2.15 compatibility

Dockerfile

@@ -0,0 +1,35 @@
+FROM python:3.10-slim
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV TF_CPP_MIN_LOG_LEVEL=2
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential \
+    && rm -rf /var/lib/apt/lists/*
+
+# Create user for HuggingFace Spaces
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user
+ENV PATH=/home/user/.local/bin:$PATH
+
+WORKDIR /home/user/app
+
+# Copy requirements first for better caching
+COPY --chown=user:user requirements.txt .
+
+# Install Python dependencies
+RUN pip install --no-cache-dir --upgrade pip && \
+    pip install --no-cache-dir -r requirements.txt
+
+# Copy application code
+COPY --chown=user:user . .
+
+# Expose port
+EXPOSE 7860
+
+# Run the application
+CMD ["python", "app.py"]

README.md

@@ -3,9 +3,7 @@ title: CRISPR Array Detection
 emoji: 🧬
 colorFrom: blue
 colorTo: green
-sdk: gradio
-sdk_version: 4.44.0
-app_file: app.py
+sdk: docker
 pinned: false
 license: mit
 ---

app.py

@@ -259,4 +259,4 @@ if __name__ == "__main__":
     model = get_model()
     warmup_model(model)
     print(f"Model ready! GPU: {get_gpu_status()}")
-    demo.launch()
+    demo.launch(server_name="0.0.0.0", server_port=7860)