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# api/utils.py
from datetime import datetime
import json
import uuid
import asyncio
import random
import string
from typing import Any, Dict, Optional, AsyncGenerator
import httpx
from fastapi import HTTPException
from api.config import (
MODELS,
MODEL_ALIASES,
DEFAULT_MODEL,
API_ENDPOINT,
get_headers_api_chat,
BASE_URL,
MODEL_PREFIXES,
MODEL_REFERERS
)
from api.models import ChatRequest, Message
from api.logger import setup_logger
logger = setup_logger(__name__)
# Helper function to create a random alphanumeric chat ID
def generate_chat_id(length: int = 7) -> str:
characters = string.ascii_letters + string.digits
return ''.join(random.choices(characters, k=length))
# Helper function to create a chat completion data chunk
def create_chat_completion_data(
content: str, model: str, timestamp: int, finish_reason: Optional[str] = None
) -> Dict[str, Any]:
return {
"id": f"chatcmpl-{uuid.uuid4()}",
"object": "chat.completion.chunk",
"created": timestamp,
"model": model,
"choices": [
{
"index": 0,
"delta": {"content": content, "role": "assistant"},
"finish_reason": finish_reason,
}
],
"usage": None,
}
# Function to convert message to dictionary format, ensuring base64 data and optional model prefix
def message_to_dict(message: Message, model_prefix: Optional[str] = None):
content = message.content if isinstance(message.content, str) else message.content[0]["text"]
if model_prefix:
content = f"{model_prefix} {content}"
if isinstance(message.content, list) and len(message.content) == 2 and "image_url" in message.content[1]:
# Ensure base64 images are always included for all models
return {
"role": message.role,
"content": content,
"data": {
"imageBase64": message.content[1]["image_url"]["url"],
"fileText": "",
"title": "snapshot",
},
}
return {"role": message.role, "content": content}
# Function to strip model prefix from content if present
def strip_model_prefix(content: str, model_prefix: Optional[str] = None) -> str:
"""Remove the model prefix from the response content if present."""
if model_prefix and content.startswith(model_prefix):
logger.debug(f"Stripping prefix '{model_prefix}' from content.")
return content[len(model_prefix):].strip()
return content
# Function to get the correct referer URL for logging
def get_referer_url(chat_id: str, model: str) -> str:
"""Generate the referer URL based on specific models listed in MODEL_REFERERS."""
if model in MODEL_REFERERS:
return f"{BASE_URL}/chat/{chat_id}?model={model}"
return BASE_URL
# Helper function to format messages
def format_messages(messages: list[Message]) -> str:
# Assuming messages need to be concatenated in some way
return "\n".join([msg.content if isinstance(msg.content, str) else msg.content[0]["text"] for msg in messages])
# Process streaming response
async def process_streaming_response(request: ChatRequest) -> AsyncGenerator[str, None]:
chat_id = generate_chat_id()
referer_url = get_referer_url(chat_id, request.model)
logger.info(f"Generated Chat ID: {chat_id} - Model: {request.model} - URL: {referer_url}")
model = request.model if request.model in MODELS else MODEL_ALIASES.get(request.model, DEFAULT_MODEL)
model_prefix = MODEL_PREFIXES.get(model, "")
headers_api_chat = get_headers_api_chat(referer_url)
# Prepare data based on model type
if model in ['flux1', 'sdxl', 'sd', 'sd35']: # Image models
prompt = request.messages[-1].content if isinstance(request.messages[-1].content, str) else request.messages[-1].content[0]["text"]
data = {
"model": model,
"input": {
"width": "1024",
"height": "1024",
"steps": 4,
"output_format": "webp",
"batch_size": 1,
"mode": "plan",
"prompt": prompt
}
}
else: # Chat models
data = {
"model": model,
"input": {
"messages": [
{
"type": "human",
"content": f"{model_prefix} {format_messages(request.messages)}" if model_prefix else format_messages(request.messages)
}
],
"mode": "plan"
},
"noStream": False # Assuming streaming
}
async with httpx.AsyncClient() as client:
try:
async with client.post(
API_ENDPOINT,
headers=headers_api_chat,
json=data,
timeout=100
) as response:
response.raise_for_status()
# Assuming the API returns a streaming response
async for line in response.aiter_lines():
timestamp = int(datetime.now().timestamp())
if line:
content = line
# Depending on GizAI's response format, adjust parsing
# Placeholder for content processing
# Assuming content contains the message
cleaned_content = strip_model_prefix(content, model_prefix)
yield f"data: {json.dumps(create_chat_completion_data(cleaned_content, model, timestamp))}\n\n"
# Indicate end of stream
yield f"data: {json.dumps(create_chat_completion_data('', model, int(datetime.now().timestamp()), 'stop'))}\n\n"
yield "data: [DONE]\n\n"
except httpx.HTTPStatusError as e:
logger.error(f"HTTP error occurred for Chat ID {chat_id}: {e}")
raise HTTPException(status_code=e.response.status_code, detail=str(e))
except httpx.RequestError as e:
logger.error(f"Error occurred during request for Chat ID {chat_id}: {e}")
raise HTTPException(status_code=500, detail=str(e))
# Process non-streaming response
async def process_non_streaming_response(request: ChatRequest) -> Dict[str, Any]:
chat_id = generate_chat_id()
referer_url = get_referer_url(chat_id, request.model)
logger.info(f"Generated Chat ID: {chat_id} - Model: {request.model} - URL: {referer_url}")
model = request.model if request.model in MODELS else MODEL_ALIASES.get(request.model, DEFAULT_MODEL)
model_prefix = MODEL_PREFIXES.get(model, "")
headers_api_chat = get_headers_api_chat(referer_url)
# Prepare data based on model type
if model in ['flux1', 'sdxl', 'sd', 'sd35']: # Image models
prompt = request.messages[-1].content if isinstance(request.messages[-1].content, str) else request.messages[-1].content[0]["text"]
data = {
"model": model,
"input": {
"width": "1024",
"height": "1024",
"steps": 4,
"output_format": "webp",
"batch_size": 1,
"mode": "plan",
"prompt": prompt
}
}
else: # Chat models
data = {
"model": model,
"input": {
"messages": [
{
"type": "human",
"content": f"{model_prefix} {format_messages(request.messages)}" if model_prefix else format_messages(request.messages)
}
],
"mode": "plan"
},
"noStream": True # Non-streaming
}
async with httpx.AsyncClient() as client:
try:
response = await client.post(
API_ENDPOINT,
headers=headers_api_chat,
json=data,
timeout=100
)
response.raise_for_status()
response_data = response.json()
# Process response based on GizAI's API response structure
# Placeholder: assuming 'output' contains the generated content
if model in ['flux1', 'sdxl', 'sd', 'sd35']: # Image models
if response_data.get('status') == 'completed' and response_data.get('output'):
images = response_data['output']
# Assuming images is a list of URLs
# For non-streaming, return all images at once
# Adjust according to actual response
return {
"id": f"chatcmpl-{uuid.uuid4()}",
"object": "chat.completion",
"created": int(datetime.now().timestamp()),
"model": model,
"choices": [
{
"index": 0,
"message": {"role": "assistant", "content": "", "images": images},
"finish_reason": "stop",
}
],
"usage": None,
}
else: # Chat models
# Assuming response_data contains the full response
content = response_data.get('output', '')
cleaned_content = strip_model_prefix(content, model_prefix)
return {
"id": f"chatcmpl-{uuid.uuid4()}",
"object": "chat.completion",
"created": int(datetime.now().timestamp()),
"model": model,
"choices": [
{
"index": 0,
"message": {"role": "assistant", "content": cleaned_content},
"finish_reason": "stop",
}
],
"usage": None,
}
except httpx.HTTPStatusError as e:
logger.error(f"HTTP error occurred for Chat ID {chat_id}: {e}")
raise HTTPException(status_code=e.response.status_code, detail=str(e))
except httpx.RequestError as e:
logger.error(f"Error occurred during request for Chat ID {chat_id}: {e}")
raise HTTPException(status_code=500, detail=str(e))
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