trying out a fix

Dockerfile

@@ -2,25 +2,23 @@ FROM nvcr.io/nvidia/tensorflow:20.12-tf1-py3
 
 USER root
 
-# Fix GPG Keys and install system libs
+# Fix GPG Keys and system dependencies
 RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/3bf863cc.pub \
-    && apt-get update && apt-get install -y \
-    libgl1-mesa-glx \
-    libglib2.0-0 \
+    && apt-get update && apt-get install -y libgl1-mesa-glx libglib2.0-0 \
     && rm -rf /var/lib/apt/lists/*
 
-# Set up user
+# Create user and app directory
 RUN useradd -m -u 1000 user
 RUN mkdir -p /home/user/app && chown -R user:user /home/user/app
 
-# Force the environment to use the NVIDIA-optimized site-packages
-# In 20.12-tf1-py3, NVIDIA puts the TF packages here:
-ENV PYTHONPATH=/usr/local/lib/python3.6/dist-packages:/usr/local/lib/python3.7/dist-packages:$PYTHONPATH \
-    HF_HOME=/home/user/app/.cache
+# CRITICAL: Force the path to prioritize NVIDIA's Python 3.7 site-packages over system 3.8
+ENV PYTHONPATH=/usr/local/lib/python3.6/dist-packages:/usr/local/lib/python3.7/dist-packages:/home/user/app
+ENV HF_HOME=/home/user/app/.cache
+ENV PATH=/home/user/.local/bin:$PATH
 
 WORKDIR /home/user/app
 
-# Use the specific python3 binary for all pip installs
+# We use /usr/bin/python3 because in this image it is linked to the TF1.15 build
 COPY requirements.txt .
 RUN /usr/bin/python3 -m pip install --no-cache-dir --upgrade "pip<23.0"
 RUN /usr/bin/python3 -m pip install --no-cache-dir -r requirements.txt
@@ -29,6 +27,5 @@ COPY --chown=user:user . .
 
 USER user
 
-# Use the absolute path to python3 to run uvicorn
-# This ensures it doesn't accidentally jump into Python 3.8
+# Final Command: Absolute path to ensure we don't hit Python 3.8
 CMD ["/usr/bin/python3", "-m", "uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

@@ -5,17 +5,24 @@ import numpy as np
 import tensorflow as tf
 import sys 
 
+import tensorflow as tf
+# Force TF 1.15 behavior if not already set
+if hasattr(tf, 'compat') and hasattr(tf.compat, 'v1'):
+    tf.compat.v1.disable_v2_behavior()
+
 try:
-    import tensorflow as tf
-    # For TF 1.15 compatibility
-    from tensorflow import keras
-    import tensorflow.python.keras.engine as KE
-    
-    # Inject into sys.modules so 'import keras' works for mrcnn
-    sys.modules['keras'] = keras
-    sys.modules['keras.engine'] = KE
+    from tensorflow.python.keras import engine as KE
 except ImportError:
-    import keras
+    # Some TF 1.15 versions hide it here
+    import tensorflow.keras.backend as K
+    from tensorflow.python.keras.api._v1.keras import engine as KE
+
+# Inject these into sys.modules so 'import keras.engine' works
+import tensorflow.python.keras as keras
+sys.modules['keras'] = keras
+sys.modules['keras.engine'] = KE
+sys.modules['keras.layers'] = keras.layers
+sys.modules['keras.models'] = keras.models
     
 from fastapi import FastAPI, File, UploadFile
 from fastapi.middleware.cors import CORSMiddleware