some changes

Dockerfile

@@ -1,22 +1,32 @@
-# Use a slim Python base image. For GPU, you'd need a CUDA-enabled base.
+# Use a slim Python base image.
 FROM python:3.9-slim
 
 # Set the working directory in the container
 WORKDIR /app
 
-# Install git (useful for some Hugging Face model/tokenizer downloads that might use it)
-# Also install common build tools often needed for Python packages
+# Set Hugging Face cache directory and Gradio temp/flagging dir
+# These will be within /app or /tmp, which we can make writable.
+ENV HF_HOME=/app/.cache/huggingface
+ENV GRADIO_TEMP_DIR=/tmp/gradio_tmp # For Gradio's own temp files
+ENV GRADIO_FLAGGING_DIR=/tmp/gradio_flags # For Gradio flagging data
+
+# Install git and build-essential (good practice for some pip installs)
 RUN apt-get update && apt-get install -y \
     git \
     build-essential \
     && rm -rf /var/lib/apt/lists/*
 
+# Create the cache and temp directories and make them writable by any user.
+# The user running the app inside the container (often root by default in simple Dockerfiles,
+# or a non-root user in managed environments like Spaces) needs to write here.
+RUN mkdir -p $HF_HOME $GRADIO_TEMP_DIR $GRADIO_FLAGGING_DIR && \
+    chmod -R 777 $HF_HOME $GRADIO_TEMP_DIR $GRADIO_FLAGGING_DIR
+
 # Copy the requirements file first to leverage Docker layer caching
 COPY requirements.txt requirements.txt
 
 # Install Python dependencies
 # --no-cache-dir reduces image size
-# --prefer-binary can speed up builds for packages with binary distributions
 RUN pip install --no-cache-dir --prefer-binary -r requirements.txt
 
 # Copy the application code into the container

app.py

@@ -90,6 +90,24 @@ This Space uses the `lusxvr/nanoVLM-222M` model.
 # Example image from COCO dataset
 example_image_url = "http://images.cocodataset.org/val2017/000000039769.jpg" # A cat and a remote
 
+# iface = gr.Interface(
+#     fn=generate_text_for_image,
+#     inputs=[
+#         gr.Image(type="pil", label="Upload Image"),
+#         gr.Textbox(label="Your Prompt/Question", info="e.g., 'What is this a picture of?', 'Describe the main subject.', 'How many animals are there?'")
+#     ],
+#     outputs=gr.Textbox(label="Generated Text", show_copy_button=True),
+#     title="Interactive nanoVLM-222M Demo",
+#     description=description,
+#     examples=[
+#         [example_image_url, "a photo of a"],
+#         [example_image_url, "Describe the image in detail."],
+#         [example_image_url, "What objects are on the sofa?"],
+#     ],
+#     cache_examples=True # Cache results for examples to load faster
+# )
+# ... (other parts of your app.py)
+
 iface = gr.Interface(
     fn=generate_text_for_image,
     inputs=[
@@ -104,9 +122,14 @@ iface = gr.Interface(
         [example_image_url, "Describe the image in detail."],
         [example_image_url, "What objects are on the sofa?"],
     ],
-    cache_examples=True # Cache results for examples to load faster
+    cache_examples=True,
+    allow_flagging="never"  # Add this line to disable flagging
+    # OR, if you want flagging, configure its directory (ensure GRADIO_FLAGGING_DIR is set in Dockerfile):
+    # import os
+    # flagging_dir=os.environ.get("GRADIO_FLAGGING_DIR"),
 )
 
+
 if __name__ == "__main__":
     # For Hugging Face Spaces, it's common to launch with server_name="0.0.0.0"
     # The Space infrastructure handles the public URL and port mapping.