hugging face space deployment fixes

README.md

@@ -1,11 +1,21 @@
-# ChatXBT Web3 AI Assitant Service
+---
+title: ChatXBT
+emoji: 💬
+colorFrom: purple
+colorTo: pink
+sdk: docker
+sdk_version: "latest"
+app_file: chatxbt-assistant.py
+pinned: false
+---
 
-AI-powered unification and execution protocol for web3 applications
+# ChatXBT Web3 AI Assistant Service
 
+AI-powered unification and execution protocol for web3 applications
 
 ## Requirements
 
-The list below contains all the requirements needed to have a complete developer environment to sart working on this project.
+The list below contains all the requirements needed to have a complete developer environment to start working on this project.
 Follow the links to install or update these tools on your machine.
 
 1. [Python 3.12+](https://www.python.org/downloads/release/python-3120/)
@@ -13,17 +23,15 @@ Follow the links to install or update these tools on your machine.
 3. [Pip Installer](https://pip.pypa.io/en/stable/installation/)
 4. [Python Venv](https://docs.python.org/3/library/venv.html)
 
-
 ## Installation
 
 1. Clone the repository: `git clone https://github.com/ChatXBT/chatxbt-web3-ai-assistant.git`
-2. Get a copy of the `\`.env`\` file from the project manager`
+2. Get a copy of the `.env` file from the project manager
 3. Create a virtual environment: `python -m venv env`
 4. Activate the virtual environment: `source env/bin/activate`
-4. Install the dependencies: `pip install -r requirements.txt`
-5. Run the application: `chainlit run chatxbt-assistant.py -w`
-6. Open the application in your browser: `http://localhost:8000`
-
+5. Install the dependencies: `pip install -r requirements.txt`
+6. Run the application: `chainlit run chatxbt-assistant.py -w`
+7. Open the application in your browser: `http://localhost:8000`
 
 ## Project Overview
 
@@ -36,17 +44,15 @@ The project is structured as follows:
 5. src/search_services: `a list of search services that can be used to retrieve data for the AI assistant.`
 6. src/tools: `a list of tools that can be used to extend the functionality of the AI assistant.`
 
-
 ## Guides
 
 1. Follow the development, structure and documentation patterns of the `src/tools/coin_data_toolkit.py` to build custom toolkits
 2. Follow the development, structure and documentation patterns of the `src/tools/coin_data_toolkit.py` to build classes 
-3. Pay attention to the `constants` variables and `caching` decorators to optimize perfomance of class functions/methods across the project
+3. Pay attention to the `constants` variables and `caching` decorators to optimize performance of class functions/methods across the project
 4. Create new classes in the appropriate folders to maintain an organized codebase and project
 5. After installing any new package run `` to update the dependency requirements list
 6. Expose RPC functions for SDKs that do not support python runtime. use [DeepKit](https://deepkit.io/documentation/rpc) and [BunJS](https://bun.sh/) if possible.
 
-
 ## Git Workflow
 
 For this project, we will be using a very simple version of the Gitflow workflow. This workflow is designed to manage the development, release, and maintenance of software projects. It provides a clear and structured approach to managing branches, releases, and feature development.
@@ -68,38 +74,19 @@ For this project, we will be using a very simple version of the Gitflow workflow
 3. Push your changes to the remote repository: `git push origin bugfix/my-bugfix`
 4. Open a pull request on GitHub and request a review from a team member.
 5. Deploy the bugfix branch to a staging environment for testing.
-6. Once testing is finished, merge the pull  request into the main branch.
+6. Once testing is finished, merge the pull request into the main branch.
 7. Once the pull request is approved, merge it into the main branch.
 8. Delete the bugfix branch: `git branch -d bugfix/my-bugfix`
 9. Update your local main branch: `git checkout main && git pull origin main`
 
-
-## Deplopyment to producton
+## Deployment to production
 
 To be finalised and added
 
-
 ## TODO
 
 1. Add deployment instructions
 2. Setup CI/CD pipelines for automatic deployment
 3. Setup and integrate `LiteLLM` instance for LLM service high availability
-4. Add RPC class for remmote method calls to other chatxbt services written in other launguages such as `JS`, `TS` & `GO`
-5. Write enough unit test to ensure class and function input and output correctness
-
-## HUGGING FACE DEPLOYMENT
-
----
-title: ChatXBT
-emoji: 💬
-colorFrom: purple
-colorTo: pink
-sdk: docker
-sdk_version: "latest"
-app_file: chatxbt-assistant.py
-pinned: false
----
-
-# ChatXBT
-
-Welcome to ChatXBT! This project is a Chainlit app deployed on Hugging Face spaces.
+4. Add RPC class for remote method calls to other ChatXBT services written in other languages such as `JS`, `TS` & `GO`
+5. Write enough unit tests to ensure class and function input and output correctness