Update dockerfile, path to cudnn

Dockerfile

@@ -73,11 +73,11 @@ RUN if [ -f "/app/FILM.py" ]; then \
     # Add try-except around model call\
     sed -i '/def __call__/a\        try:' /app/FILM.py && \
     sed -i '/result = self._model/i\            # Try with GPU' /app/FILM.py && \
-    sed -i '/result = self._model/a\        except Exception as e:\n            print(f"GPU inference failed: {e}, falling back to CPU")\n            # Force CPU execution\n            with tf.device("/cpu:0"):\n                result = self._model(inputs, training=False)' /app/FILM.py; \
+    sed -i '/result = self._model/a\        except Exception as e:\n            print(f"GPU inference failed: {e}, falling back to CPU")\n            with tf.device("/cpu:0"):\n                result = self._model(inputs, training=False)' /app/FILM.py; \
 fi
 
 # Set environment variables for GPU compatibility
-ENV LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/lib/x86_64-linux-gnu:${LD_LIBRARY_PATH}
+ENV LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu:/usr/local/cuda/lib64
 ENV PATH=/usr/local/cuda/bin:${PATH}
 ENV TF_FORCE_GPU_ALLOW_GROWTH=true
 
@@ -89,7 +89,7 @@ EXPOSE 8501
 # Create a startup script that ensures proper execution
 RUN echo '#!/bin/bash\n\
 # Ensure library paths are set correctly\n\
-export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH\n\
+export LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu:/usr/local/cuda/lib64\n\
 # Run the app\n\
 exec streamlit run app.py --server.port=8501 --server.address=0.0.0.0\n\
 ' > /app/start.sh && chmod +x /app/start.sh