86 lines
2.7 KiB
Python
86 lines
2.7 KiB
Python
# Load environment variables first
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from dotenv import load_dotenv
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load_dotenv()
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import json
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import os
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import traceback
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from datetime import datetime
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from fastapi import FastAPI, WebSocket
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from llm import stream_openai_response
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from mock import MOCK_HTML, mock_completion
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from image_generation import generate_images
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from prompts import assemble_prompt
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app = FastAPI()
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# Useful for debugging purposes when you don't want to waste GPT4-Vision credits
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# Setting to True will stream a mock response instead of calling the OpenAI API
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SHOULD_MOCK_AI_RESPONSE = False
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def write_logs(prompt_messages, completion):
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# Create run_logs directory if it doesn't exist
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if not os.path.exists("run_logs"):
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os.makedirs("run_logs")
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# Generate a unique filename using the current timestamp
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filename = datetime.now().strftime("run_logs/messages_%Y%m%d_%H%M%S.json")
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# Write the messages dict into a new file for each run
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with open(filename, "w") as f:
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f.write(json.dumps({"prompt": prompt_messages, "completion": completion}))
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@app.websocket("/generate-code")
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async def stream_code_test(websocket: WebSocket):
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await websocket.accept()
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params = await websocket.receive_json()
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await websocket.send_json({"type": "status", "value": "Generating code..."})
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async def process_chunk(content):
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await websocket.send_json({"type": "chunk", "value": content})
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prompt_messages = assemble_prompt(params["image"])
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if params["generationType"] == "update":
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# Transform into message format
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for index, text in enumerate(params["history"]):
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prompt_messages += [
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{"role": "assistant" if index % 2 == 0 else "user", "content": text}
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]
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if SHOULD_MOCK_AI_RESPONSE:
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completion = await mock_completion(process_chunk)
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else:
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completion = await stream_openai_response(
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prompt_messages,
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lambda x: process_chunk(x),
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)
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# Write the messages dict into a log so that we can debug later
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write_logs(prompt_messages, completion)
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# Generate images
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await websocket.send_json({"type": "status", "value": "Generating images..."})
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try:
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updated_html = await generate_images(completion)
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await websocket.send_json({"type": "setCode", "value": updated_html})
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await websocket.send_json(
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{"type": "status", "value": "Code generation complete."}
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)
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except Exception as e:
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traceback.print_exc()
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print("Image generation failed", e)
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await websocket.send_json(
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{"type": "status", "value": "Image generation failed but code is complete."}
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)
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finally:
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await websocket.close()
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