Github/screenshot-to-code
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Merge 0b3b492c98 into 392b9849a2

Browse Source
This commit is contained in:
xia ning 2024-06-11 09:43:09 +08:00 committed by GitHub
commit f8f2910157
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
4 changed files with 264 additions and 39 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

5
backend/config.py
View File

@ -5,6 +5,11 @@ import os
ANTHROPIC_API_KEY = os.environ.get("ANTHROPIC_API_KEY", None)
# AWS
AWS_ACCESS_KEY = os.environ.get("AWS_ACCESS_KEY", None)
AWS_SECRET_ACCESS_KEY = os.environ.get("AWS_SECRET_ACCESS_KEY", None)
AWS_REGION_NAME = os.environ.get("AWS_REGION_NAME", "us-west-2")
# Debugging-related
SHOULD_MOCK_AI_RESPONSE = bool(os.environ.get("MOCK", False))

32
backend/evals/core.py
View File

@ -1,7 +1,11 @@
import os
from config import AWS_REGION_NAME
from config import AWS_ACCESS_KEY
from config import AWS_SECRET_ACCESS_KEY
from config import ANTHROPIC_API_KEY
from llm import Llm, stream_claude_response, stream_openai_response
from llm import Llm, stream_claude_response, stream_openai_response, stream_claude_response_aws_bedrock
from prompts import assemble_prompt
from prompts.types import Stack
@ -10,20 +14,32 @@ async def generate_code_core(image_url: str, stack: Stack, model: Llm) -> str:
prompt_messages = assemble_prompt(image_url, stack)
openai_api_key = os.environ.get("OPENAI_API_KEY")
anthropic_api_key = ANTHROPIC_API_KEY
aws_access_key = AWS_ACCESS_KEY
aws_secret_access_key = AWS_SECRET_ACCESS_KEY
aws_region_name = AWS_REGION_NAME,
openai_base_url = None
async def process_chunk(content: str):
pass
if model == Llm.CLAUDE_3_SONNET:
if not anthropic_api_key:
raise Exception("Anthropic API key not found")
if not anthropic_api_key and not aws_access_key and not aws_secret_access_key:
raise Exception("Anthropic API key or AWS Access Key not found")
completion = await stream_claude_response(
prompt_messages,
api_key=anthropic_api_key,
callback=lambda x: process_chunk(x),
)
if anthropic_api_key:
completion = await stream_claude_response(
prompt_messages,
api_key=anthropic_api_key,
callback=lambda x: process_chunk(x),
)
else:
completion = await stream_claude_response_aws_bedrock(
prompt_messages,
access_key=aws_access_key,
secret_access_key=aws_secret_access_key,
aws_region_name=aws_region_name,
callback=lambda x: process_chunk(x),
)
else:
if not openai_api_key:
raise Exception("OpenAI API key not found")

187
backend/llm.py
View File

@ -5,7 +5,10 @@ from openai import AsyncOpenAI
from openai.types.chat import ChatCompletionMessageParam, ChatCompletionChunk
from config import IS_DEBUG_ENABLED
from debug.DebugFileWriter import DebugFileWriter
import json
import boto3
from typing import List
from botocore.exceptions import ClientError
from utils import pprint_prompt
@ -15,6 +18,7 @@ class Llm(Enum):
GPT_4_TURBO_2024_04_09 = "gpt-4-turbo-2024-04-09"
GPT_4O_2024_05_13 = "gpt-4o-2024-05-13"
CLAUDE_3_SONNET = "claude-3-sonnet-20240229"
CLAUDE_3_SONNET_BEDROCK = "anthropic.claude-3-sonnet-20240229-v1:0"
CLAUDE_3_OPUS = "claude-3-opus-20240229"
CLAUDE_3_HAIKU = "claude-3-haiku-20240307"
@ -128,6 +132,128 @@ async def stream_claude_response(
return response.content[0].text
def initialize_bedrock_client(access_key: str, secret_access_key: str, aws_region_name: str):
try:
# Initialize the Bedrock Runtime client
bedrock_runtime = boto3.client(
service_name='bedrock-runtime',
aws_access_key_id=access_key,
aws_secret_access_key=secret_access_key,
region_name=aws_region_name,
)
return bedrock_runtime
except ClientError as err:
message = err.response["Error"]["Message"]
print(f"A client error occurred: {message}")
except Exception as err:
print("An error occurred!")
raise err
async def stream_bedrock_response(
bedrock_runtime,
messages: List[dict],
system_prompt: str,
model_id: str,
max_tokens: int,
content_type: str,
accept: str,
temperature: float,
callback: Callable[[str], Awaitable[None]],
) -> str:
try:
# Prepare the request body
body = json.dumps({
"anthropic_version": "bedrock-2023-05-31",
"max_tokens": max_tokens,
"messages": messages,
"system": system_prompt,
"temperature": temperature
})
# Invoke the Bedrock Runtime API with response stream
response = bedrock_runtime.invoke_model_with_response_stream(
body=body,
modelId=model_id,
accept=accept,
contentType=content_type,
)
stream = response.get("body")
# Stream the response
final_message = ""
if stream:
for event in stream:
chunk = event.get("chunk")
if chunk:
data = chunk.get("bytes").decode()
chunk_obj = json.loads(data)
if chunk_obj["type"] == "content_block_delta":
text = chunk_obj["delta"]["text"]
await callback(text)
final_message += text
return final_message
except ClientError as err:
message = err.response["Error"]["Message"]
print(f"A client error occurred: {message}")
except Exception as err:
print("An error occurred!")
raise err
async def stream_claude_response_aws_bedrock(
messages: List[dict],
access_key: str,
secret_access_key: str,
aws_region_name: str,
callback: Callable[[str], Awaitable[None]],
) -> str:
bedrock_runtime = initialize_bedrock_client(access_key, secret_access_key, aws_region_name)
# Set model parameters
model_id = Llm.CLAUDE_3_SONNET_BEDROCK.value
max_tokens = 4096
content_type = 'application/json'
accept = '*/*'
temperature = 0.0
# Translate OpenAI messages to Claude messages
system_prompt = cast(str, messages[0].get("content"))
claude_messages = [dict(message) for message in messages[1:]]
for message in claude_messages:
if not isinstance(message["content"], list):
continue
for content in message["content"]: # type: ignore
if content["type"] == "image_url":
content["type"] = "image"
# Extract base64 data and media type from data URL
# Example base64 data URL: data:image/png;base64,iVBOR...
image_data_url = cast(str, content["image_url"]["url"])
media_type = image_data_url.split(";")[0].split(":")[1]
base64_data = image_data_url.split(",")[1]
# Remove OpenAI parameter
del content["image_url"]
content["source"] = {
"type": "base64",
"media_type": media_type,
"data": base64_data,
}
return await stream_bedrock_response(
bedrock_runtime,
claude_messages,
system_prompt,
model_id,
max_tokens,
content_type,
accept,
temperature,
callback,
)
async def stream_claude_response_native(
system_prompt: str,
@ -216,3 +342,62 @@ async def stream_claude_response_native(
raise Exception("No HTML response found in AI response")
else:
return response.content[0].text
async def stream_claude_response_native_aws_bedrock(
system_prompt: str,
messages: list[Any],
access_key: str,
secret_access_key: str,
aws_region_name: str,
callback: Callable[[str], Awaitable[None]],
include_thinking: bool = False,
model: Llm = Llm.CLAUDE_3_SONNET_BEDROCK,
) -> str:
bedrock_runtime = initialize_bedrock_client(access_key, secret_access_key, aws_region_name)
# Set model parameters
model_id = Llm.CLAUDE_3_SONNET_BEDROCK.value
max_tokens = 4096
content_type = 'application/json'
accept = '*/*'
temperature = 0.0
# Multi-pass flow
current_pass_num = 1
max_passes = 2
prefix = "<thinking>"
response = None
while current_pass_num <= max_passes:
current_pass_num += 1
# Set up message depending on whether we have a <thinking> prefix
messages_to_send = (
messages + [{"role": "assistant", "content": prefix}]
if include_thinking
else messages
)
response_text = await stream_bedrock_response(
bedrock_runtime,
messages_to_send,
system_prompt,
model_id,
max_tokens,
content_type,
accept,
temperature,
callback,
)
# Set up messages array for next pass
messages += [
{"role": "assistant", "content": str(prefix) + response_text},
{
"role": "user",
"content": "You've done a good job with a first draft. Improve this further based on the original instructions so that the app is fully functional and looks like the original video of the app we're trying to replicate.",
},
]
return response_text

79
backend/routes/generate_code.py
View File

@ -2,14 +2,14 @@ import os
import traceback
from fastapi import APIRouter, WebSocket
import openai
from config import ANTHROPIC_API_KEY, IS_PROD, SHOULD_MOCK_AI_RESPONSE
from config import ANTHROPIC_API_KEY, IS_PROD, SHOULD_MOCK_AI_RESPONSE, AWS_ACCESS_KEY, AWS_SECRET_ACCESS_KEY, AWS_REGION_NAME
from custom_types import InputMode
from llm import (
Llm,
convert_frontend_str_to_llm,
stream_claude_response,
stream_claude_response_native,
stream_openai_response,
stream_openai_response, stream_claude_response_aws_bedrock, stream_claude_response_native_aws_bedrock,
)
from openai.types.chat import ChatCompletionMessageParam
from mock_llm import mock_completion
@ -25,7 +25,6 @@ from prompts.types import Stack
from video.utils import extract_tag_content, assemble_claude_prompt_video
from ws.constants import APP_ERROR_WEB_SOCKET_CODE # type: ignore
router = APIRouter()
@ -55,7 +54,7 @@ async def stream_code(websocket: WebSocket):
print("Incoming websocket connection...")
async def throw_error(
message: str,
message: str,
):
await websocket.send_json({"type": "error", "value": message})
await websocket.close(APP_ERROR_WEB_SOCKET_CODE)
@ -230,33 +229,53 @@ async def stream_code(websocket: WebSocket):
else:
try:
if validated_input_mode == "video":
if not anthropic_api_key:
await throw_error(
"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"
)
raise Exception("No Anthropic key")
if not anthropic_api_key and not AWS_ACCESS_KEY and not AWS_SECRET_ACCESS_KEY:
await throw_error(
"Video only works with Anthropic models. Neither Anthropic API key or AWS Access Key found. Please add the environment variable ANTHROPIC_API_KEY or AWS_ACCESS_KEY/AWS_SECRET_ACCESS_KEY to backend/.env or in the settings dialog"
)
raise Exception("No Anthropic key")
completion = await stream_claude_response_native(
system_prompt=VIDEO_PROMPT,
messages=prompt_messages, # type: ignore
api_key=anthropic_api_key,
callback=lambda x: process_chunk(x),
model=Llm.CLAUDE_3_OPUS,
include_thinking=True,
)
exact_llm_version = Llm.CLAUDE_3_OPUS
elif code_generation_model == Llm.CLAUDE_3_SONNET:
if not anthropic_api_key:
await throw_error(
"No Anthropic API key found. Please add the environment variable ANTHROPIC_API_KEY to backend/.env or in the settings dialog"
)
raise Exception("No Anthropic key")
completion = await stream_claude_response(
prompt_messages, # type: ignore
api_key=anthropic_api_key,
callback=lambda x: process_chunk(x),
)
if anthropic_api_key:
completion = await stream_claude_response_native(
system_prompt=VIDEO_PROMPT,
messages=prompt_messages, # type: ignore
api_key=anthropic_api_key,
callback=lambda x: process_chunk(x),
model=Llm.CLAUDE_3_OPUS,
include_thinking=True,
)
else:
completion = await stream_claude_response_native_aws_bedrock(
system_prompt=VIDEO_PROMPT,
messages=prompt_messages, # type: ignore
access_key=AWS_ACCESS_KEY,
secret_access_key=AWS_SECRET_ACCESS_KEY,
aws_region_name=AWS_REGION_NAME,
callback=lambda x: process_chunk(x),
model=Llm.CLAUDE_3_OPUS,
include_thinking=True,
)
exact_llm_version = Llm.CLAUDE_3_OPUS
elif code_generation_model == Llm.CLAUDE_3_SONNET:
if not anthropic_api_key and not AWS_ACCESS_KEY and not AWS_SECRET_ACCESS_KEY:
await throw_error(
"No Anthropic API key or AWS Access Key found. Please add the environment variable ANTHROPIC_API_KEY or AWS_ACCESS_KEY/AWS_SECRET_ACCESS_KEY to backend/.env or in the settings dialog"
)
raise Exception("No Anthropic key")
if anthropic_api_key:
completion = await stream_claude_response(
prompt_messages, # type: ignore
api_key=anthropic_api_key,
callback=lambda x: process_chunk(x),
)
else:
completion = await stream_claude_response_aws_bedrock(
prompt_messages, # type: ignore
access_key=AWS_ACCESS_KEY,
secret_access_key=AWS_SECRET_ACCESS_KEY,
aws_region_name=AWS_REGION_NAME,
callback=lambda x: process_chunk(x),
)
exact_llm_version = code_generation_model
else:
completion = await stream_openai_response(