m-c-frank / apimesh Goto Github PK

for now start with this:

#!/bin/bash

# Step 1: Create Flask app
cat > app.py <<EOF
from flask import Flask, request, jsonify

app = Flask(__name__)

@app.route('/', methods=['POST'])
def echo():
    return jsonify(request.json)

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8000)
EOF

# Step 2: Create Dockerfile
cat > Dockerfile <<EOF
FROM python:3.9-slim
WORKDIR /app
COPY app.py /app
RUN pip install Flask
CMD ["python", "/app/app.py"]
EOF

# Step 3: Build and Run Docker Container
docker build -t flask-app .
docker run -d -p 8000:8000 --name flask-app flask-app

# Optional Step 4: Test the setup using curl
echo "To test, run: curl -X POST -d '{\"key\":\"value\"}' -H 'Content-Type: application/json' http://localhost:8000/"

write a gitraw-server that is running on my local machine which takes in a github username, github reponame and path/filename to a file and returns a plain message line {"content":the actual file content} it should just use curl and listen for requests and then curl raw. i guess i just think github workflows are too slow therefore i need my llms to have access to simple microservices and the gitraw can be used to index whats available and to get like little readmes to understand how the tools work

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on the github repo settings

right now im using chatgpt to propose commands to run but i need to document them here or something. perhaps have a .bashhistory file in the repository itself and on every commit with a "working as intended" tag it could check and prune the bash history and make a script for just that task

there are so many, i need to find a way to convey the full meaning and potential of a cli in a single line including an example on how to use it. so i can let an llm decide what to do