Initial commit of llm app

Dockerfile

@@ -0,0 +1,42 @@
+# Get a distribution that has uv already installed
+FROM ghcr.io/astral-sh/uv:python3.13-bookworm-slim
+
+# Add Rust compiler installation
+USER root
+RUN apt-get update && apt-get install -y \
+    curl \
+    build-essential \
+    && rm -rf /var/lib/apt/lists/*
+RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
+ENV PATH="/root/.cargo/bin:${PATH}"
+
+# Add user - this is the user that will run the app
+# If you do not set user, the app will run as root (undesirable)
+RUN useradd -m -u 1000 user
+USER user
+
+# Set up Rust for the user
+RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
+ENV PATH="/home/user/.cargo/bin:${PATH}"
+
+# Set the home directory and path
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH        
+
+ENV UVICORN_WS_PROTOCOL=websockets
+
+# Set the working directory
+WORKDIR $HOME/app
+
+# Copy the app to the container
+COPY --chown=user . $HOME/app
+
+# Install the dependencies
+# RUN uv sync --frozen
+RUN uv sync
+
+# Expose the port
+EXPOSE 7860
+
+# Run the app
+CMD ["uv", "run", "chainlit", "run", "app.py", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -1,11 +1 @@
----
-title: Llm App
-emoji: 🌍
-colorFrom: gray
-colorTo: pink
-sdk: docker
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+LLM Challenge Makerspace 

app.py

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+# You can find this code for Chainlit python streaming here (https://docs.chainlit.io/concepts/streaming/python)
+
+# OpenAI Chat completion
+import os
+from openai import AsyncOpenAI  # importing openai for API usage
+import chainlit as cl  # importing chainlit for our app
+from chainlit.prompt import Prompt, PromptMessage  # importing prompt tools
+from chainlit.playground.providers import ChatOpenAI  # importing ChatOpenAI tools
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# ChatOpenAI Templates
+system_template = """You are a helpful assistant who always speaks in a pleasant tone!
+"""
+
+user_template = """{input}
+Think through your response step by step.
+"""
+
+
+@cl.on_chat_start  # marks a function that will be executed at the start of a user session
+async def start_chat():
+    settings = {
+        "model": "gpt-3.5-turbo",
+        "temperature": 0,
+        "max_tokens": 500,
+        "top_p": 1,
+        "frequency_penalty": 0,
+        "presence_penalty": 0,
+    }
+
+    cl.user_session.set("settings", settings)
+
+
+@cl.on_message  # marks a function that should be run each time the chatbot receives a message from a user
+async def main(message: cl.Message):
+    settings = cl.user_session.get("settings")
+
+    client = AsyncOpenAI()
+
+    print(message.content)
+
+    prompt = Prompt(
+        provider=ChatOpenAI.id,
+        messages=[
+            PromptMessage(
+                role="system",
+                template=system_template,
+                formatted=system_template,
+            ),
+            PromptMessage(
+                role="user",
+                template=user_template,
+                formatted=user_template.format(input=message.content),
+            ),
+        ],
+        inputs={"input": message.content},
+        settings=settings,
+    )
+
+    print([m.to_openai() for m in prompt.messages])
+
+    msg = cl.Message(content="")
+
+    # Call OpenAI
+    async for stream_resp in await client.chat.completions.create(
+        messages=[m.to_openai() for m in prompt.messages], stream=True, **settings
+    ):
+        token = stream_resp.choices[0].delta.content
+        if not token:
+            token = ""
+        await msg.stream_token(token)
+
+    # Update the prompt object with the completion
+    prompt.completion = msg.content
+    msg.prompt = prompt
+
+    # Send and close the message stream
+    await msg.send()

app/hello_world.ipynb

@@ -0,0 +1,59 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "np.random.seed(0)\n",
+    "values = np.random.randn(100)\n",
+    "pd.Series(values).plot(kind='hist', title='Test Histogram')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

app/hello_world.py

@@ -0,0 +1 @@
+print("hello world! let's do some ml ops!")

chainlit.md

@@ -0,0 +1,3 @@
+# Beyond ChatGPT 
+
+This Chainlit app was created following instructions from [this repository!](https://github.com/AI-Maker-Space/Beyond-ChatGPT)

pyproject.toml

@@ -0,0 +1,14 @@
+[project]
+name = "beyond-chatgpt"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.13"
+dependencies = [
+    "chainlit==0.7.700",
+    "cohere==4.37",
+    "openai==1.3.5",
+    "pydantic==2.10.1",
+    "python-dotenv==1.0.0",
+    "tiktoken==0.5.1",
+]