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AelfGPT

AelfGPT is a powerful tool designed to enhance the development experience for Aelf blockchain developers. This application integrates advanced AI technologies to offer intelligent code debugging, code generation, and blockchain exploration functionalities.

Demo:

aelf-gpt-demo.mp4

Key Features

  1. Smart Contract Debugger: Analyze and debug Aelf smart contract code written in C#. Get detailed insights into potential issues and suggestions for code improvements.

  2. Smart Contract Generator: Generate and enhance Aelf smart contract code based on user input. Receive AI-driven suggestions for code enhancements.

  3. Chat with Aelf Documentation: Interact with the Aelf documentation through an AI-powered chat interface. Ask questions and get responses based on the Aelf documentation.

  4. Aelf Block Explorer: Explore and retrieve Aelf blockchain data, including:

    • Chain Status: View the current status of the blockchain.
    • Block Information: Retrieve block information by block hash or block height.
    • Transaction Details: Get details of specific transactions.
    • Transaction Pool Status: Check the status of transactions in the pool.

Setup and Deployment (Local)

1. Fork and Clone the Repository

  1. Fork this repository on GitHub to your own account.

  2. Clone your forked repository:

    git clone https://github.com/your-username/aelfgpt.git
cd aelfgpt

2. Create and Configure the .env File

Create a .env file in the root directory of your project using the provided example:

  1. Copy the example .env file:

    cp .env.example .env

  2. Fill in the environment variables:

    • MONGODB_CONN_URI: Your MongoDB connection URI.
    • EDEN_AI_API_KEY: Your Eden AI API key. Obtain it from Eden AI.
    • AI_ML_API_KEY: Your GPT-4 API key from AIML API.
    • GOOGLE_API_KEY: Your Google API key from Google Cloud.

3. Set Up Dependencies

Install the required dependencies:

pip install -r requirements.txt

4. Create Embeddings and Configure MongoDB Atlas

  1. Run the Jupyter notebook provided to create and store embeddings into MongoDB.

  2. Configure MongoDB Atlas:

    • Create a Vector Search:

      • Go to MongoDB Atlas and navigate to the search section.

      • Create a new vector search with the provided JSON configuration:

        {
  "fields": [
    {
      "numDimensions": 1536,
      "path": "embedding",
      "similarity": "cosine",
      "type": "vector"
    }
  ]
}

    • Create Atlas Search:

      • Select the docs collection and create an Atlas Search.
      • This process may take 1-2 minutes. Once done, your vector search should have status 'Active' and its ready for use.

5. Run the Application Locally

Run the Streamlit application:

streamlit run Home.py

Deployment Guide for Streamlit Application

This guide provides step-by-step instructions to deploy your Streamlit application on Google Cloud Platform (GCP) using Cloud Run.

Prerequisites

  1. Google Cloud Account: If you don't have a Google Cloud account, create one here.

Getting Started

  1. Create a Google Cloud Project:

    • Sign in to the Google Cloud Console.
    • Create a new project or select an existing one.
    • Enable the billing account (free trial recommended to avoid unintended costs).

  2. Clone the Repository:

    git clone https://github.com/codingis4noobs2/aelfGPT.git
cd aelfGPT

  3. Authenticate with Google Cloud:

    gcloud auth login

  4. Initialize Your Project:

    gcloud init

Cloud Run Deployment

Create Required Files

  1. Dockerfile: Create a Dockerfile with the following content:

    # Use an official Python runtime as a parent image
FROM python:3.10-slim

# Set the working directory in the container
WORKDIR /app

# Copy the requirements file into the container
COPY requirements.txt /app/

# Install the required dependencies
RUN pip install --no-cache-dir -r requirements.txt

# Copy the rest of the application code into the container
COPY . /app

# Expose the port that the app runs on
EXPOSE 80

# Command to run the app
CMD ["streamlit", "run", "Home.py", "--server.enableCORS", "false", "--browser.serverAddress", "0.0.0.0", "--browser.gatherUsageStats", "false", "--server.port", "80"]

  2. app.yaml: Create an app.yaml file with the following content:

    runtime: python310

entrypoint: streamlit run Home.py --server.enableCORS false --browser.serverAddress 0.0.0.0 --browser.gatherUsageStats false --server.port $PORT

Build and Deploy

  1. Build the Container:

    gcloud builds submit --tag gcr.io/PROJECT-ID/streamlit-app

    Replace PROJECT-ID with your actual project ID, which can be found in the top left dropdown in the Google Cloud Console. image

  2. Deploy the Container:

    gcloud run deploy --image gcr.io/PROJECT-ID/streamlit-app --platform managed --allow-unauthenticated

Note

If you encounter the error Cloud Run – Failed to start and then listen on the port defined by the PORT:

  1. Execute the following command: 
    docker buildx build --platform linux/amd64 -t {project-name} .
  2. Then, redeploy the container.

Alternative Deployment Method: Cloud Run UI

You can also deploy the container via the Cloud Run UI in the Google Cloud Console:

  1. Navigate to Cloud Run
  2. Click on create "+ Create Service" Button.
  3. In the Cloud Run UI, select the latest container image and enter a service name for the application. image
  4. Click on the radio button "Allow unauthenticated invocations" so that others can visit your app.
  5. Select the appropriate machine configuration based on your estimated traffic handling and click "Create."

You will receive a link to your application in a few minutes.

Contributing

Contributions are welcome! Please open an issue or submit a pull request if you have suggestions or improvements.