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Dockerfile

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+FROM python:3.12
+COPY --from=ghcr.io/astral-sh/uv:0.4.20 /uv /bin/uv
+
+RUN useradd -m -u 1000 user
+ENV PATH="/home/user/.local/bin:$PATH"
+ENV UV_SYSTEM_PYTHON=1
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN uv pip install -r requirements.txt
+
+COPY --chown=user . /app
+USER user
+
+CMD ["marimo", "run", "app.py", "--host", "0.0.0.0", "--port", "7860"]

README.md

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+---
+title: marimo chatbot template
+emoji: 🤖
+colorFrom: red
+colorTo: blue
+sdk: docker
+pinned: true
+license: mit
+short_description: Template for deploying a marimo chatbot to HF
+---
+
+Check out marimo at <https://github.com/marimo-team/marimo>
+Check out the configuration reference at <https://huggingface.co/docs/hub/spaces-config-reference>

app.py

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+import marimo
+
+__generated_with = "0.9.14"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def __():
+    import marimo as mo
+    import os
+    from huggingface_hub import InferenceClient
+    return InferenceClient, mo, os
+
+
+@app.cell
+def __():
+    MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta"
+    return (MODEL_NAME,)
+
+
+@app.cell(hide_code=True)
+def __(MODEL_NAME, mo):
+    mo.md(f"""
+    # Chat with **{MODEL_NAME}**
+    """)
+    return
+
+
+@app.cell
+def __(max_tokens, mo, system_message, temperature, top_p):
+    mo.hstack(
+        [
+            system_message,
+            mo.vstack([temperature, top_p, max_tokens], align="end"),
+        ],
+    )
+    return
+
+
+@app.cell
+def __(mo, respond):
+    chat = mo.ui.chat(
+        model=respond,
+        prompts=["Tell me a joke.", "What is the square root of {{number}}?"],
+    )
+    chat
+    return (chat,)
+
+
+@app.cell
+def __(InferenceClient, MODEL_NAME, os):
+    """
+    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.26.2/en/guides/inference
+    """
+
+    hf_token = os.environ.get("HF_TOKEN")
+    if not hf_token:
+        print("HF_TOKEN not set, may have limited access.")
+
+    client = InferenceClient(
+        MODEL_NAME,
+        token=hf_token,
+    )
+    return client, hf_token
+
+
+@app.cell
+def __(client, mo):
+    # Create UI controls
+    system_message = mo.ui.text_area(
+        value="You are a friendly Chatbot.",
+        label="System message",
+    )
+    max_tokens = mo.ui.slider(
+        start=1,
+        stop=2048,
+        value=512,
+        step=1,
+        label="Max new tokens",
+        show_value=True,
+    )
+    temperature = mo.ui.slider(
+        start=0.1,
+        stop=4.0,
+        value=0.7,
+        step=0.1,
+        label="Temperature",
+        show_value=True,
+    )
+    top_p = mo.ui.slider(
+        start=0.1,
+        stop=1.0,
+        value=0.95,
+        step=0.05,
+        label="Top-p (nucleus sampling)",
+        show_value=True,
+    )
+
+    # Add more configuration options if needed.
+
+
+    # Create chat callback
+    def respond(messages: list[mo.ai.ChatMessage], config):
+        chat_messages = [{"role": "system", "content": system_message.value}]
+
+        for message in messages:
+            parts = []
+            # Add text
+            parts.append({"type": "text", "text": message.content})
+
+            # Add attachments
+            if message.attachments:
+                for attachment in message.attachments:
+                    content_type = attachment.content_type or ""
+                    # This example only supports image attachments
+                    if content_type.startswith("image"):
+                        parts.append(
+                            {
+                                "type": "image_url",
+                                "image_url": {"url": attachment.url},
+                            }
+                        )
+                    else:
+                        raise ValueError(
+                            f"Unsupported content type {content_type}"
+                        )
+
+            chat_messages.append({"role": message.role, "content": parts})
+
+        response = client.chat_completion(
+            chat_messages,
+            max_tokens=max_tokens.value,
+            temperature=temperature.value,
+            top_p=top_p.value,
+            stream=False,
+        )
+
+        # You can return strings, markdown, charts, tables, dataframes, and more.
+        return response.choices[0].message.content
+    return max_tokens, respond, system_message, temperature, top_p
+
+
+@app.cell
+def __():
+    # If you need to do anything _reactively_ to the chat messages,
+    # you can access the chat messages using the `chat.value` attribute.
+    # chat.value
+    return
+
+
+if __name__ == "__main__":
+    app.run()

development.md

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+# Development
+
+## Testing your Dockerfile locally
+
+```bash
+docker build -t marimo-app .
+docker run -it --rm -p 7860:7860 marimo-app
+```

requirements.txt

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+huggingface-hub==0.26.2
+marimo
+# Or a specific version
+# marimo>=0.9.0
+
+# Add other dependencies as needed