# Aider chat conversation:
USER: add previ
ASSISTANT:
^C KeyboardInterrupt
USER: add `frontend/src/components/Preview.tsx`
ASSISTANT: Sure, I can help with that. Could you please provide more details about the changes you want to make in `frontend/src/components/Preview.tsx`?
USER: add a button to download the code as index.html
ASSISTANT: To add a download button, we need to modify the `frontend/src/components/Preview.tsx` file. We will add a new button that, when clicked, will trigger a download of the generated code as an `index.html` file.
Here is the step-by-step plan:
1. Import the `useState` hook from React.
2. Create a new state variable `blobUrl` and a function `createBlobUrl` to update it.
3. In the `createBlobUrl` function, create a new Blob from the `code` prop, create a URL for the Blob, and set `blobUrl` to this URL.
4. Add a new button that triggers the `createBlobUrl` function when clicked.
5. Set the `href` attribute of the button to `blobUrl` and the `download` attribute to `index.html`.
Here is the *edit block* for these changes:
```jsx
frontend/src/components/Preview.tsx
<<<<<<< HEAD
interface Props {
code: string;
}
function Preview({ code }: Props) {
return (
<div className="w-[704px]">
<iframe
title="Iframe Example"
srcDoc={code}
className="border-[5px] border-black rounded-[33px] p-4 shadow-lg
transform scale-[0.8] origin-top-left w-[1280px] h-[832px]"
></iframe>
</div>
);
}
export default Preview;
=======
import { useState } from 'react';
interface Props {
code: string;
}
function Preview({ code }: Props) {
const [blobUrl, setBlobUrl] = useState('');
const createBlobUrl = () => {
const blob = new Blob([code], { type: 'text/html' });
const url = URL.createObjectURL(blob);
setBlobUrl(url);
};
return (
<div className="w-[704px]">
<iframe
title="Iframe Example"
srcDoc={code}
className="border-[5px] border-black rounded-[33px] p-4 shadow-lg
transform scale-[0.8] origin-top-left w-[1280px] h-[832px]"
></iframe>
<button onClick={createBlobUrl} href={blobUrl} download="index.html">
Download code
</button>
</div>
);
}
export default Preview;
>>>>>>> updated
```
# Aider chat conversation:
ASSISTANT: To achieve this, we need to modify the `backend/main.py` file. We will change the file writing operation to write into a new file in the `run_logs` directory for each run. We will use the current timestamp to create a unique filename for each run.
Here is the step-by-step plan:
1. Import the `datetime` and `os` modules.
2. Create a `run_logs` directory if it doesn't exist.
3. Generate a unique filename using the current timestamp.
4. Change the file writing operation to write into the new file in the `run_logs` directory.
Here is the *edit block* for these changes:
```python
backend/main.py
<<<<<<< HEAD
# Load environment variables first
from dotenv import load_dotenv
from prompts import assemble_prompt
load_dotenv()
from fastapi import FastAPI, WebSocket
from llm import stream_openai_response
app = FastAPI()
@app.websocket("/generate-code")
async def stream_code_test(websocket: WebSocket):
await websocket.accept()
result = await websocket.receive_json()
async def process_chunk(content):
await websocket.send_json({"type": "chunk", "value": content})
messages = assemble_prompt("")
print(messages)
# Write the messages dict into a file for debugging
with open("messages.json", "w") as f:
f.write(str(messages))
await stream_openai_response(
messages,
lambda x: process_chunk(x),
)
=======
# Load environment variables first
from dotenv import load_dotenv
import os
from datetime import datetime
from prompts import assemble_prompt
load_dotenv()
from fastapi import FastAPI, WebSocket
from llm import stream_openai_response
app = FastAPI()
@app.websocket("/generate-code")
async def stream_code_test(websocket: WebSocket):
await websocket.accept()
result = await websocket.receive_json()
async def process_chunk(content):
await websocket.send_json({"type": "chunk", "value": content})
messages = assemble_prompt("")
print(messages)
# Create run_logs directory if it doesn't exist
if not os.path.exists('run_logs'):
os.makedirs('run_logs')
# Generate a unique filename using the current timestamp
filename = datetime.now().strftime('run_logs/messages_%Y%m%d_%H%M%S.json')
# Write the messages dict into a new file for each run
with open(filename, "w") as f:
f.write(str(messages))
await stream_openai_response(
messages,
lambda x: process_chunk(x),
)
>>>>>>> updated
```
