This commit is contained in:
Abi Raja 2024-07-31 14:40:52 -04:00
commit 710f2987a4
9 changed files with 650 additions and 15 deletions

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@ -6,6 +6,9 @@ import os
OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY", None) OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY", None)
ANTHROPIC_API_KEY = os.environ.get("ANTHROPIC_API_KEY", None) ANTHROPIC_API_KEY = os.environ.get("ANTHROPIC_API_KEY", None)
# Image generation (optional)
REPLICATE_API_KEY = os.environ.get("REPLICATE_API_KEY", None)
# Debugging-related # Debugging-related
SHOULD_MOCK_AI_RESPONSE = bool(os.environ.get("MOCK", False)) SHOULD_MOCK_AI_RESPONSE = bool(os.environ.get("MOCK", False))

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@ -1,13 +1,29 @@
import asyncio import asyncio
import re import re
from typing import Dict, List, Union from typing import Dict, List, Literal, Union
from openai import AsyncOpenAI from openai import AsyncOpenAI
from bs4 import BeautifulSoup from bs4 import BeautifulSoup
from image_generation.replicate import call_replicate
async def process_tasks(prompts: List[str], api_key: str, base_url: str | None):
tasks = [generate_image(prompt, api_key, base_url) for prompt in prompts] async def process_tasks(
prompts: List[str],
api_key: str,
base_url: str | None,
model: Literal["dalle3", "sdxl-lightning"],
):
import time
start_time = time.time()
if model == "dalle3":
tasks = [generate_image_dalle(prompt, api_key, base_url) for prompt in prompts]
else:
tasks = [generate_image_replicate(prompt, api_key) for prompt in prompts]
results = await asyncio.gather(*tasks, return_exceptions=True) results = await asyncio.gather(*tasks, return_exceptions=True)
end_time = time.time()
generation_time = end_time - start_time
print(f"Image generation time: {generation_time:.2f} seconds")
processed_results: List[Union[str, None]] = [] processed_results: List[Union[str, None]] = []
for result in results: for result in results:
@ -20,7 +36,7 @@ async def process_tasks(prompts: List[str], api_key: str, base_url: str | None):
return processed_results return processed_results
async def generate_image( async def generate_image_dalle(
prompt: str, api_key: str, base_url: str | None prompt: str, api_key: str, base_url: str | None
) -> Union[str, None]: ) -> Union[str, None]:
client = AsyncOpenAI(api_key=api_key, base_url=base_url) client = AsyncOpenAI(api_key=api_key, base_url=base_url)
@ -36,6 +52,25 @@ async def generate_image(
return res.data[0].url return res.data[0].url
async def generate_image_replicate(prompt: str, api_key: str) -> str:
# We use SDXL Lightning
return await call_replicate(
"5f24084160c9089501c1b3545d9be3c27883ae2239b6f412990e82d4a6210f8f",
{
"width": 1024,
"height": 1024,
"prompt": prompt,
"scheduler": "K_EULER",
"num_outputs": 1,
"guidance_scale": 0,
"negative_prompt": "worst quality, low quality",
"num_inference_steps": 4,
},
api_key,
)
def extract_dimensions(url: str): def extract_dimensions(url: str):
# Regular expression to match numbers in the format '300x200' # Regular expression to match numbers in the format '300x200'
matches = re.findall(r"(\d+)x(\d+)", url) matches = re.findall(r"(\d+)x(\d+)", url)
@ -63,7 +98,11 @@ def create_alt_url_mapping(code: str) -> Dict[str, str]:
async def generate_images( async def generate_images(
code: str, api_key: str, base_url: Union[str, None], image_cache: Dict[str, str] code: str,
api_key: str,
base_url: Union[str, None],
image_cache: Dict[str, str],
model: Literal["dalle3", "sdxl-lightning"] = "dalle3",
) -> str: ) -> str:
# Find all images # Find all images
soup = BeautifulSoup(code, "html.parser") soup = BeautifulSoup(code, "html.parser")
@ -91,7 +130,7 @@ async def generate_images(
return code return code
# Generate images # Generate images
results = await process_tasks(prompts, api_key, base_url) results = await process_tasks(prompts, api_key, base_url, model)
# Create a dict mapping alt text to image URL # Create a dict mapping alt text to image URL
mapped_image_urls = dict(zip(prompts, results)) mapped_image_urls = dict(zip(prompts, results))

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@ -0,0 +1,62 @@
import asyncio
import httpx
async def call_replicate(
replicate_model_version: str, input: dict[str, str | int], api_token: str
) -> str:
url = "https://api.replicate.com/v1/predictions"
headers = {
"Authorization": f"Bearer {api_token}",
"Content-Type": "application/json",
}
data = {"version": replicate_model_version, "input": input}
async with httpx.AsyncClient() as client:
try:
response = await client.post(url, headers=headers, json=data)
response.raise_for_status()
response_json = response.json()
# Extract the id from the response
prediction_id = response_json.get("id")
if not prediction_id:
raise ValueError("Prediction ID not found in initial response.")
# Polling every 1 second until the status is succeeded or error
num_polls = 0
max_polls = 100
while num_polls < max_polls:
num_polls += 1
await asyncio.sleep(0.2)
# Check the status
status_check_url = f"{url}/{prediction_id}"
status_response = await client.get(status_check_url, headers=headers)
status_response.raise_for_status()
status_response_json = status_response.json()
status = status_response_json.get("status")
# If status is succeeded or if there's an error, break out of the loop
if status == "succeeded":
return status_response_json["output"][0]
elif status == "error":
raise ValueError(
f"Inference errored out: {status_response_json.get('error', 'Unknown error')}"
)
elif status == "failed":
raise ValueError("Inference failed")
# If we've reached here, it means we've exceeded the max number of polls
raise TimeoutError("Inference timed out")
except httpx.HTTPStatusError as e:
raise ValueError(f"HTTP error occurred: {e}")
except httpx.RequestError as e:
raise ValueError(f"An error occurred while requesting: {e}")
except asyncio.TimeoutError:
raise TimeoutError("Request timed out")
except Exception as e:
raise ValueError(f"An unexpected error occurred: {e}")

430
backend/poetry.lock generated
View File

@ -1,5 +1,115 @@
# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. # This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
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optional = false
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[package.dependencies]
idna = ">=2.0"
multidict = ">=4.0"
[metadata] [metadata]
lock-version = "2.0" lock-version = "2.0"
python-versions = "^3.10" python-versions = "^3.10"
content-hash = "5c21a836f7db5b25b6d0aa971bc04c9a935444be57b3fbf83b8316c46f261e24" content-hash = "9cec287b530dcda39515e7c76eed76d88eb0c725f37148af5ef93083cfb46ad8"

View File

@ -2,7 +2,7 @@ from typing import Union
from openai.types.chat import ChatCompletionMessageParam, ChatCompletionContentPartParam from openai.types.chat import ChatCompletionMessageParam, ChatCompletionContentPartParam
from custom_types import InputMode from custom_types import InputMode
from image_generation import create_alt_url_mapping from image_generation.core import create_alt_url_mapping
from llm import Llm from llm import Llm
from prompts.imported_code_prompts import IMPORTED_CODE_SYSTEM_PROMPTS from prompts.imported_code_prompts import IMPORTED_CODE_SYSTEM_PROMPTS

View File

@ -19,6 +19,7 @@ anthropic = "^0.18.0"
moviepy = "^1.0.3" moviepy = "^1.0.3"
pillow = "^10.3.0" pillow = "^10.3.0"
types-pillow = "^10.2.0.20240520" types-pillow = "^10.2.0.20240520"
aiohttp = "^3.9.5"
[tool.poetry.group.dev.dependencies] [tool.poetry.group.dev.dependencies]
pytest = "^7.4.3" pytest = "^7.4.3"

View File

@ -1,10 +1,15 @@
import os import os
import asyncio import asyncio
import traceback
from fastapi import APIRouter, WebSocket from fastapi import APIRouter, WebSocket
import openai import openai
from codegen.utils import extract_html_content from codegen.utils import extract_html_content
from config import ANTHROPIC_API_KEY, IS_PROD, OPENAI_API_KEY, SHOULD_MOCK_AI_RESPONSE from config import (
ANTHROPIC_API_KEY,
IS_PROD,
OPENAI_API_KEY,
REPLICATE_API_KEY,
SHOULD_MOCK_AI_RESPONSE,
)
from custom_types import InputMode from custom_types import InputMode
from llm import ( from llm import (
Llm, Llm,
@ -17,7 +22,7 @@ from llm import (
from fs_logging.core import write_logs from fs_logging.core import write_logs
from mock_llm import mock_completion from mock_llm import mock_completion
from typing import Any, Coroutine, Dict, List, Literal, Union, cast, get_args from typing import Any, Coroutine, Dict, List, Literal, Union, cast, get_args
from image_generation import generate_images from image_generation.core import generate_images
from prompts import create_prompt from prompts import create_prompt
from prompts.claude_prompts import VIDEO_PROMPT from prompts.claude_prompts import VIDEO_PROMPT
from prompts.types import Stack from prompts.types import Stack
@ -51,18 +56,30 @@ async def perform_image_generation(
openai_base_url: str | None, openai_base_url: str | None,
image_cache: dict[str, str], image_cache: dict[str, str],
): ):
replicate_api_key = REPLICATE_API_KEY
if not should_generate_images: if not should_generate_images:
return completion return completion
if not openai_api_key: if replicate_api_key:
print("No OpenAI API key found. Skipping image generation.") image_generation_model = "sdxl-lightning"
return completion api_key = replicate_api_key
else:
if not openai_api_key:
print(
"No OpenAI API key and Replicate key found. Skipping image generation."
)
return completion
image_generation_model = "dalle3"
api_key = openai_api_key
print("Generating images with model: ", image_generation_model)
return await generate_images( return await generate_images(
completion, completion,
api_key=openai_api_key, api_key=api_key,
base_url=openai_base_url, base_url=openai_base_url,
image_cache=image_cache, image_cache=image_cache,
model=image_generation_model,
) )

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@ -0,0 +1,85 @@
import asyncio
import os
from typing import List, Optional, Literal
from dotenv import load_dotenv
import aiohttp
from image_generation.core import process_tasks
EVALS = [
"Romantic Background",
"Company logo: A stylized green sprout emerging from a circle",
"Placeholder image of a PDF cover with abstract design",
"A complex bubble diagram showing various interconnected features and aspects of FestivalPro, with a large central bubble surrounded by smaller bubbles of different colors representing different categories and functionalities",
"A vibrant, abstract visualization of the RhythmRise experience ecosystem, featuring interconnected neon elements representing music, technology, and human connection",
"Banner with text 'LiblibAI学院 课程入口'",
"Profile picture of Pierre-Louis Labonne",
"Two hands holding iPhone 14 models with colorful displays",
"Portrait of a woman with long dark hair smiling at the camera",
"Threadless logo on a gradient background from light pink to coral",
"Jordan Schlansky Shows Conan His Favorite Nose Hair Trimmer",
"Team Coco",
"Intro to Large Language Models",
"Andrej Karpathy",
"He built a $200 million toy company",
"CNBC International",
"What will happen in year three of the war?",
"Channel",
"This is it",
"How ASML Dominates Chip Machines",
]
# Load environment variables
load_dotenv()
# Get API keys from environment variables
OPENAI_API_KEY: Optional[str] = os.getenv("OPENAI_API_KEY")
REPLICATE_API_TOKEN: Optional[str] = os.getenv("REPLICATE_API_TOKEN")
# Directory to save generated images
OUTPUT_DIR: str = "generated_images"
async def generate_and_save_images(
prompts: List[str],
model: Literal["dalle3", "sdxl-lightning"],
api_key: Optional[str],
) -> None:
# Ensure the output directory exists
os.makedirs(OUTPUT_DIR, exist_ok=True)
if api_key is None:
raise ValueError(f"API key for {model} is not set in the environment variables")
# Generate images
results: List[Optional[str]] = await process_tasks(
prompts, api_key, None, model=model
)
# Save images to disk
async with aiohttp.ClientSession() as session:
for i, image_url in enumerate(results):
if image_url:
# Get the image data
async with session.get(image_url) as response:
image_data: bytes = await response.read()
# Save the image with a filename based on the input eval
prefix = "replicate_" if model == "sdxl-lightning" else "dalle3_"
filename: str = (
f"{prefix}{prompts[i][:50].replace(' ', '_').replace(':', '')}.png"
)
filepath: str = os.path.join(OUTPUT_DIR, filename)
with open(filepath, "wb") as f:
f.write(image_data)
print(f"Saved {model} image: {filepath}")
else:
print(f"Failed to generate {model} image for prompt: {prompts[i]}")
async def main() -> None:
# await generate_and_save_images(EVALS, "dalle3", OPENAI_API_KEY)
await generate_and_save_images(EVALS, "sdxl-lightning", REPLICATE_API_TOKEN)
if __name__ == "__main__":
asyncio.run(main())