init commit to huggine face

.idea/workspace.xml

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+    "last_opened_file_path": "/Users/jiefeng/Dropbox/Apps/llm-app",
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+    "node.js.selected.package.eslint": "(autodetect)",
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Dockerfile

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+FROM python:3.9
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+WORKDIR $HOME/app
+COPY --chown=user . $HOME/app
+COPY ./requirements.txt ~/app/requirements.txt
+RUN pip install -r requirements.txt
+COPY . .
+CMD ["chainlit", "run", "app.py", "--port", "7860"]

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 openai #importing openai for API usage
+import chainlit as cl #importing chainlit for our app
+
+# You only need the api key inserted here if it's not in your .env file
+#openai.api_key = "YOUR_API_KEY"
+
+# We select our model. If you do not have access to GPT-4, please use GPT-3.5T (gpt-3.5-turbo)
+
+model_name = "gpt-3.5-turbo"
+# model_name = "gpt-4"
+settings = {
+    "temperature": 0.7, # higher value increases output diveresity/randomness
+    "max_tokens": 500, # maximum length of output response
+    "top_p": 1, # choose only the top x% of possible words to return
+    "frequency_penalty": 0, # higher value will result in the model being more conservative in its use of repeated tokens.
+    "presence_penalty": 0, # higher value will result in the model being more likely to generate tokens that have not yet been included in the generated text
+}
+
+@cl.on_chat_start # marks a function that will be executed at the start of a user session
+def start_chat():
+    cl.user_session.set(
+        "message_history",
+        [{"role": "system", "content": "You are a helpful assistant."}],
+    )
+
+
+@cl.on_message # marks a function that should be run each time the chatbot receives a message from a user
+async def main(message: str):
+    message_history = cl.user_session.get("message_history")
+    message_history.append({"role": "user", "content": message})
+
+    msg = cl.Message(content="")
+
+    async for stream_resp in await openai.ChatCompletion.acreate(
+        model=model_name, messages=message_history, stream=True, **settings
+    ):
+        token = stream_resp.choices[0]["delta"].get("content", "")
+        await msg.stream_token(token)
+
+    message_history.append({"role": "assistant", "content": msg.content})
+    await msg.send()

chainlit.md

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+# Welcome to Chainlit! 🚀🤖
+
+Hi there, Developer! 👋 We're excited to have you on board. Chainlit is a powerful tool designed to help you prototype, debug and share applications built on top of LLMs.
+
+## Useful Links 🔗
+
+- **Documentation:** Get started with our comprehensive [Chainlit Documentation](https://docs.chainlit.io) 📚
+- **Discord Community:** Join our friendly [Chainlit Discord](https://discord.gg/k73SQ3FyUh) to ask questions, share your projects, and connect with other developers! 💬
+
+We can't wait to see what you create with Chainlit! Happy coding! 💻😊
+
+## Welcome screen
+
+To modify the welcome screen, edit the `chainlit.md` file at the root of your project. If you do not want a welcome screen, just leave this file empty.

requirements.txt

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+chainlit
+openai