421 lines
17 KiB
Python
421 lines
17 KiB
Python
import os
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import asyncio
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import traceback
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from fastapi import APIRouter, WebSocket
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import openai
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from codegen.utils import extract_html_content
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from config import ANTHROPIC_API_KEY, IS_PROD, SHOULD_MOCK_AI_RESPONSE
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from custom_types import InputMode
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from llm import (
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Llm,
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convert_frontend_str_to_llm,
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stream_claude_response,
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stream_claude_response_native,
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stream_openai_response,
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)
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from openai.types.chat import ChatCompletionMessageParam
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from mock_llm import mock_completion
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from typing import Any, Callable, Coroutine, Dict, List, Literal, Union, cast, get_args
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from image_generation import create_alt_url_mapping, generate_images
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from prompts import assemble_imported_code_prompt, assemble_prompt
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from datetime import datetime
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import json
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from prompts.claude_prompts import VIDEO_PROMPT
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from prompts.types import Stack
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# from utils import pprint_prompt
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from video.utils import assemble_claude_prompt_video
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from ws.constants import APP_ERROR_WEB_SOCKET_CODE # type: ignore
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router = APIRouter()
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def write_logs(prompt_messages: List[ChatCompletionMessageParam], completion: str):
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# Get the logs path from environment, default to the current working directory
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logs_path = os.environ.get("LOGS_PATH", os.getcwd())
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# Create run_logs directory if it doesn't exist within the specified logs path
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logs_directory = os.path.join(logs_path, "run_logs")
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if not os.path.exists(logs_directory):
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os.makedirs(logs_directory)
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print("Writing to logs directory:", logs_directory)
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# Generate a unique filename using the current timestamp within the logs directory
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filename = datetime.now().strftime(f"{logs_directory}/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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# Generate images and return updated completions
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async def process_completion(
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completion: str,
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index: int,
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should_generate_images: bool,
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openai_api_key: str | None,
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openai_base_url: str | None,
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image_cache: dict[str, str],
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send_message: Callable[
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[Literal["chunk", "status", "setCode", "error"], str, int],
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Coroutine[Any, Any, None],
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],
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):
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try:
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if should_generate_images and openai_api_key:
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await send_message("status", "Generating images...", index)
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updated_html = await generate_images(
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completion,
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api_key=openai_api_key,
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base_url=openai_base_url,
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image_cache=image_cache,
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)
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else:
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updated_html = completion
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await send_message("setCode", updated_html, index)
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await send_message("status", "Code generation complete.", index)
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except Exception as e:
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traceback.print_exc()
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print(f"Image generation failed for variant {index}", e)
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await send_message("setCode", completion, index)
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await send_message(
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"status", "Image generation failed but code is complete.", index
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)
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@router.websocket("/generate-code")
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async def stream_code(websocket: WebSocket):
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await websocket.accept()
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print("Incoming websocket connection...")
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async def throw_error(
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message: str,
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):
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await websocket.send_json({"type": "error", "value": message})
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await websocket.close(APP_ERROR_WEB_SOCKET_CODE)
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async def send_message(
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type: Literal["chunk", "status", "setCode", "error"],
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value: str,
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variantIndex: int,
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):
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await websocket.send_json(
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{"type": type, "value": value, "variantIndex": variantIndex}
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)
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# TODO: Are the values always strings?
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params: Dict[str, str] = await websocket.receive_json()
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print("Received params")
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# Read the code config settings (stack) from the request. Fall back to default if not provided.
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generated_code_config = ""
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if "generatedCodeConfig" in params and params["generatedCodeConfig"]:
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generated_code_config = params["generatedCodeConfig"]
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if not generated_code_config in get_args(Stack):
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await throw_error(f"Invalid generated code config: {generated_code_config}")
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return
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# Cast the variable to the Stack type
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valid_stack = cast(Stack, generated_code_config)
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# Validate the input mode
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input_mode = params.get("inputMode")
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if not input_mode in get_args(InputMode):
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await throw_error(f"Invalid input mode: {input_mode}")
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raise Exception(f"Invalid input mode: {input_mode}")
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# Cast the variable to the right type
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validated_input_mode = cast(InputMode, input_mode)
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# Read the model from the request. Fall back to default if not provided.
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code_generation_model_str = params.get(
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"codeGenerationModel", Llm.GPT_4O_2024_05_13.value
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)
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try:
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code_generation_model = convert_frontend_str_to_llm(code_generation_model_str)
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except:
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await throw_error(f"Invalid model: {code_generation_model_str}")
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raise Exception(f"Invalid model: {code_generation_model_str}")
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# Auto-upgrade usage of older models
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if code_generation_model in {Llm.GPT_4_VISION, Llm.GPT_4_TURBO_2024_04_09}:
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print(
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f"Initial deprecated model: {code_generation_model}. Auto-updating code generation model to GPT-4O-2024-05-13"
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)
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code_generation_model = Llm.GPT_4O_2024_05_13
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elif code_generation_model == Llm.CLAUDE_3_SONNET:
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print(
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f"Initial deprecated model: {code_generation_model}. Auto-updating code generation model to CLAUDE-3.5-SONNET-2024-06-20"
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)
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code_generation_model = Llm.CLAUDE_3_5_SONNET_2024_06_20
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print(
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f"Generating {generated_code_config} code for uploaded {input_mode} using {code_generation_model} model..."
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)
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# Get the OpenAI API key from the request. Fall back to environment variable if not provided.
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# If neither is provided, we throw an error.
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openai_api_key = None
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if params["openAiApiKey"]:
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openai_api_key = params["openAiApiKey"]
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print("Using OpenAI API key from client-side settings dialog")
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else:
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openai_api_key = os.environ.get("OPENAI_API_KEY")
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if openai_api_key:
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print("Using OpenAI API key from environment variable")
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if not openai_api_key and (
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code_generation_model == Llm.GPT_4_VISION
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or code_generation_model == Llm.GPT_4_TURBO_2024_04_09
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or code_generation_model == Llm.GPT_4O_2024_05_13
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):
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print("OpenAI API key not found")
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await throw_error(
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"No OpenAI API key found. Please add your API key in the settings dialog or add it to backend/.env file. If you add it to .env, make sure to restart the backend server."
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)
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return
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# Get the Anthropic API key from the request. Fall back to environment variable if not provided.
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# If neither is provided, we throw an error later only if Claude is used.
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anthropic_api_key = None
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if "anthropicApiKey" in params and params["anthropicApiKey"]:
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anthropic_api_key = params["anthropicApiKey"]
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print("Using Anthropic API key from client-side settings dialog")
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else:
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anthropic_api_key = ANTHROPIC_API_KEY
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if anthropic_api_key:
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print("Using Anthropic API key from environment variable")
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# Get the OpenAI Base URL from the request. Fall back to environment variable if not provided.
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openai_base_url: Union[str, None] = None
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# Disable user-specified OpenAI Base URL in prod
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if not os.environ.get("IS_PROD"):
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if "openAiBaseURL" in params and params["openAiBaseURL"]:
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openai_base_url = params["openAiBaseURL"]
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print("Using OpenAI Base URL from client-side settings dialog")
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else:
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openai_base_url = os.environ.get("OPENAI_BASE_URL")
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if openai_base_url:
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print("Using OpenAI Base URL from environment variable")
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if not openai_base_url:
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print("Using official OpenAI URL")
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# Get the image generation flag from the request. Fall back to True if not provided.
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should_generate_images = bool(params.get("isImageGenerationEnabled", True))
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print("generating code...")
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await send_message("status", "Generating code...", 0)
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await send_message("status", "Generating code...", 1)
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async def process_chunk(content: str, variantIndex: int):
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await send_message("chunk", content, variantIndex)
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# Image cache for updates so that we don't have to regenerate images
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image_cache: Dict[str, str] = {}
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# If this generation started off with imported code, we need to assemble the prompt differently
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if params.get("isImportedFromCode") and params["isImportedFromCode"]:
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original_imported_code = params["history"][0]
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prompt_messages = assemble_imported_code_prompt(
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original_imported_code, valid_stack, code_generation_model
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)
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for index, text in enumerate(params["history"][1:]):
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if index % 2 == 0:
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message: ChatCompletionMessageParam = {
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"role": "user",
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"content": text,
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}
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else:
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message: ChatCompletionMessageParam = {
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"role": "assistant",
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"content": text,
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}
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prompt_messages.append(message)
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else:
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# Assemble the prompt
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try:
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if params.get("resultImage") and params["resultImage"]:
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prompt_messages = assemble_prompt(
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params["image"], valid_stack, params["resultImage"]
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)
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else:
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prompt_messages = assemble_prompt(params["image"], valid_stack)
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except:
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# TODO: This should use variantIndex
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await websocket.send_json(
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{
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"type": "error",
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"value": "Error assembling prompt. Contact support at support@picoapps.xyz",
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}
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)
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await websocket.close()
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return
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if params["generationType"] == "update":
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# Transform the history tree into message format
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# TODO: Move this to frontend
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for index, text in enumerate(params["history"]):
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if index % 2 == 0:
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message: ChatCompletionMessageParam = {
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"role": "assistant",
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"content": text,
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}
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else:
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message: ChatCompletionMessageParam = {
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"role": "user",
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"content": text,
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}
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prompt_messages.append(message)
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image_cache = create_alt_url_mapping(params["history"][-2])
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if validated_input_mode == "video":
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video_data_url = params["image"]
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prompt_messages = await assemble_claude_prompt_video(video_data_url)
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# pprint_prompt(prompt_messages) # type: ignore
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if SHOULD_MOCK_AI_RESPONSE:
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completions = [
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await mock_completion(process_chunk, input_mode=validated_input_mode)
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]
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else:
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try:
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if validated_input_mode == "video":
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if not anthropic_api_key:
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await throw_error(
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"Video only works with Anthropic models. No Anthropic API key found. Please add the environment variable ANTHROPIC_API_KEY to backend/.env or in the settings dialog"
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)
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raise Exception("No Anthropic key")
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completions = [
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await stream_claude_response_native(
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system_prompt=VIDEO_PROMPT,
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messages=prompt_messages, # type: ignore
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api_key=anthropic_api_key,
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callback=lambda x: process_chunk(x, 0),
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model=Llm.CLAUDE_3_OPUS,
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include_thinking=True,
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)
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]
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else:
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# Depending on the presence and absence of various keys,
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# we decide which models to run
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variant_models = []
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if openai_api_key and anthropic_api_key:
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variant_models = ["openai", "anthropic"]
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elif openai_api_key:
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variant_models = ["openai", "openai"]
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elif anthropic_api_key:
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variant_models = ["anthropic", "anthropic"]
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else:
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await throw_error(
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"No OpenAI or Anthropic API key found. Please add the environment variable OPENAI_API_KEY or ANTHROPIC_API_KEY to backend/.env or in the settings dialog"
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)
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raise Exception("No OpenAI or Anthropic key")
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tasks: List[Coroutine[Any, Any, str]] = []
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for index, model in enumerate(variant_models):
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if model == "openai":
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if openai_api_key is None:
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await throw_error("OpenAI API key is missing.")
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raise Exception("OpenAI API key is missing.")
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tasks.append(
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stream_openai_response(
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prompt_messages,
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api_key=openai_api_key,
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base_url=openai_base_url,
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callback=lambda x, i=index: process_chunk(x, i),
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model=Llm.GPT_4O_2024_05_13,
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)
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)
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elif model == "anthropic":
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if anthropic_api_key is None:
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await throw_error("Anthropic API key is missing.")
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raise Exception("Anthropic API key is missing.")
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tasks.append(
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stream_claude_response(
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prompt_messages,
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api_key=anthropic_api_key,
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callback=lambda x, i=index: process_chunk(x, i),
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model=Llm.CLAUDE_3_5_SONNET_2024_06_20,
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)
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)
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completions = await asyncio.gather(*tasks)
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print("Models used for generation: ", variant_models)
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except openai.AuthenticationError as e:
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print("[GENERATE_CODE] Authentication failed", e)
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error_message = (
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"Incorrect OpenAI key. Please make sure your OpenAI API key is correct, or create a new OpenAI API key on your OpenAI dashboard."
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+ (
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" Alternatively, you can purchase code generation credits directly on this website."
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if IS_PROD
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else ""
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)
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)
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return await throw_error(error_message)
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except openai.NotFoundError as e:
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print("[GENERATE_CODE] Model not found", e)
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error_message = (
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e.message
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+ ". Please make sure you have followed the instructions correctly to obtain an OpenAI key with GPT vision access: https://github.com/abi/screenshot-to-code/blob/main/Troubleshooting.md"
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+ (
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" Alternatively, you can purchase code generation credits directly on this website."
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if IS_PROD
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else ""
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)
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)
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return await throw_error(error_message)
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except openai.RateLimitError as e:
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print("[GENERATE_CODE] Rate limit exceeded", e)
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error_message = (
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"OpenAI error - 'You exceeded your current quota, please check your plan and billing details.'"
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+ (
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" Alternatively, you can purchase code generation credits directly on this website."
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if IS_PROD
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else ""
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)
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)
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return await throw_error(error_message)
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# if validated_input_mode == "video":
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# completion = extract_tag_content("html", completions[0])
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# Strip the completion of everything except the HTML content
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completions = [extract_html_content(completion) for completion in completions]
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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, completions[0]) # type: ignore
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try:
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image_generation_tasks = [
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process_completion(
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completion,
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index,
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should_generate_images,
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openai_api_key,
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openai_base_url,
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image_cache,
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send_message,
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)
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for index, completion in enumerate(completions)
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]
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await asyncio.gather(*image_generation_tasks)
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except Exception as e:
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traceback.print_exc()
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print("An error occurred during image generation and processing", e)
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await send_message(
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"status", "An error occurred during image generation and processing.", 0
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)
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await websocket.close()
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