changed docker to make it memory friendly

S2FApp/Dockerfile

@@ -15,11 +15,9 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
 # Copy requirements first for better caching
 COPY requirements.txt .
 
-# Install Python dependencies (exclude heavy training deps for smaller image)
-RUN pip install --no-cache-dir \
-    torch torchvision \
-    numpy opencv-python streamlit matplotlib Pillow plotly \
-    huggingface_hub
+# Install Python dependencies - CPU-only PyTorch to fit Space memory limits (avoids OOM)
+RUN pip install --no-cache-dir torch torchvision --index-url https://download.pytorch.org/whl/cpu && \
+    pip install --no-cache-dir numpy opencv-python streamlit matplotlib Pillow plotly huggingface_hub
 
 # Copy app code (chown for HF Spaces permissions)
 COPY --chown=user:user app.py predictor.py download_ckp.py ./

S2FApp/app.py

@@ -18,7 +18,6 @@ S2F_ROOT = os.path.dirname(os.path.abspath(__file__))
 if S2F_ROOT not in sys.path:
     sys.path.insert(0, S2F_ROOT)
 
-from predictor import S2FPredictor
 from utils.substrate_settings import list_substrates
 
 st.set_page_config(page_title="Shape2Force (S2F)", page_icon="🔬", layout="centered")
@@ -139,6 +138,7 @@ has_image = img is not None
 if run and checkpoint and has_image:
     with st.spinner("Loading model and predicting..."):
         try:
+            from predictor import S2FPredictor
             predictor = S2FPredictor(
                 model_type=model_type,
                 checkpoint_path=checkpoint,