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sync from upstream

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This commit is contained in:
xie river 2024-06-25 11:19:26 +00:00
parent e9c6756c2e
commit 101d1738b6
3 changed files with 8 additions and 107 deletions
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105
backend/llm.py
View File

@ -62,7 +62,7 @@ async def stream_openai_response(
model: Llm,
) -> str:
client = AsyncOpenAI(api_key=api_key, base_url=base_url)
print(f"--stream_openai_response--{model}")
# Base parameters
params = {
"model": model.value,
@ -105,9 +105,9 @@ async def stream_claude_response(
callback: Callable[[str], Awaitable[None]],
model: Llm,
) -> str:
print(f"--stream_openai_response--{model}")
# client = AsyncAnthropic(api_key=api_key)
modelId = BEDROCK_LLM_MODELID_LIST[model]
# Base parameters
max_tokens = 4096
temperature = 0.0
@ -256,17 +256,6 @@ async def stream_claude_response_native(
response_text += chunk_obj['delta']['text']
await callback(chunk_obj['delta']['text'])
# if chunk_obj['type'] == 'content_block_delta':
# print(f"\nStop reason: {chunk_obj['delta']['stop_reason']}")
# print(f"Stop sequence: {chunk_obj['delta']['stop_sequence']}")
# print(f"Output tokens: {chunk_obj['usage']['output_tokens']}")
# output_tokens = chunk_obj['usage']['output_tokens']
# # input_tokens = chunk_obj['usage']['input_tokens']
# # input_tokens = chunk_obj['amazon-bedrock-invocationMetrics']['inputTokenCount']
# if chunk_obj['type'] == 'text_delta':
# print(chunk_obj['delta']['text'])
# response_text += chunk_obj['delta']['text']
# await callback(chunk_obj['delta']['text'])
print(response_text)
# Write each pass's code to .html file and thinking to .txt file
@ -304,91 +293,3 @@ async def stream_claude_response_native(
raise Exception("No HTML response found in AI response")
else:
return response_text
async def stream_claude_response_native_bak(
system_prompt: str,
messages: list[Any],
api_key: str,
callback: Callable[[str], Awaitable[None]],
include_thinking: bool = False,
model: Llm = Llm.CLAUDE_3_OPUS,
) -> str:
client = AsyncAnthropic(api_key=api_key)
# Base model parameters
max_tokens = 4096
temperature = 0.0
# Multi-pass flow
current_pass_num = 1
max_passes = 2
prefix = "<thinking>"
response = None
# For debugging
full_stream = ""
debug_file_writer = DebugFileWriter()
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
)
pprint_prompt(messages_to_send)
async with client.messages.stream(
model=model.value,
max_tokens=max_tokens,
temperature=temperature,
system=system_prompt,
messages=messages_to_send, # type: ignore
) as stream:
async for text in stream.text_stream:
print(text, end="", flush=True)
full_stream += text
await callback(text)
response = await stream.get_final_message()
response_text = response.content[0].text
# Write each pass's code to .html file and thinking to .txt file
if IS_DEBUG_ENABLED:
debug_file_writer.write_to_file(
f"pass_{current_pass_num - 1}.html",
debug_file_writer.extract_html_content(response_text),
)
debug_file_writer.write_to_file(
f"thinking_pass_{current_pass_num - 1}.txt",
response_text.split("</thinking>")[0],
)
# Set up messages array for next pass
messages += [
{"role": "assistant", "content": str(prefix) + response.content[0].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.",
},
]
print(
f"Token usage: Input Tokens: {response.usage.input_tokens}, Output Tokens: {response.usage.output_tokens}"
)
# Close the Anthropic client
await client.close()
if IS_DEBUG_ENABLED:
debug_file_writer.write_to_file("full_stream.txt", full_stream)
if not response:
raise Exception("No HTML response found in AI response")
else:
return response.content[0].text

2
frontend/src/App.tsx
View File

@ -441,7 +441,7 @@ function App() {
2x faster. Give it a try!
</p>
</div>
)} */}
)}
{appState !== AppState.CODE_READY && <TipLink />}

8
frontend/src/lib/models.ts
View File

@ -1,10 +1,10 @@
// Keep in sync with backend (llm.py)
// Order here matches dropdown order
export enum CodeGenerationModel {
CLAUDE_3_5_SONNET = "claude_3_5_sonnet",
CLAUDE_3_5_SONNET_2024_06_20 = "claude-3-5-sonnet-20240620",
// CLAUDE_3_5_SONNET = "claude_3_5_sonnet",
CLAUDE_3_SONNET = "claude_3_sonnet",
GPT_4O_2024_05_13 = "gpt-4o-2024-05-13",
CLAUDE_3_5_SONNET_2024_06_20 = "claude-3-5-sonnet-20240620",
GPT_4_TURBO_2024_04_09 = "gpt-4-turbo-2024-04-09",
GPT_4_VISION = "gpt_4_vision",
}
@ -13,10 +13,10 @@ export enum CodeGenerationModel {
export const CODE_GENERATION_MODEL_DESCRIPTIONS: {
[key in CodeGenerationModel]: { name: string; inBeta: boolean };
} = {
"gpt-4o-2024-05-13": { name: "GPT-4o 🌟", inBeta: false },
"claude-3-5-sonnet-20240620": { name: "Claude 3.5 Sonnet 🌟", inBeta: false },
"gpt-4o-2024-05-13": { name: "GPT-4o 🌟", inBeta: false },
"gpt-4-turbo-2024-04-09": { name: "GPT-4 Turbo (Apr 2024)", inBeta: false },
gpt_4_vision: { name: "GPT-4 Vision (Nov 2023)", inBeta: false },
claude_3_sonnet: { name: "Claude 3 Sonnet", inBeta: false },
claude_3_5_sonnet: { name: "Claude 3.5 Sonnet", inBeta: false },
// claude_3_5_sonnet: { name: "Claude 3.5 Sonnet", inBeta: false },
};