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import base64
import json
import os
import secrets
import string
import time
from typing import List, Optional, Union, Any
import httpx
from dotenv import load_dotenv
from fastapi import FastAPI
from fastapi.responses import JSONResponse, StreamingResponse
from pydantic import BaseModel
# --- Configuration ---
load_dotenv()
# Env variables for external services
IMAGE_API_URL = os.environ.get("IMAGE_API_URL", "https://image.api.example.com")
SNAPZION_UPLOAD_URL = "https://upload.snapzion.com/api/public-upload"
SNAPZION_API_KEY = os.environ.get("SNAP", "")
# --- Dummy Model Definitions ---
# In a real application, these would be defined properly.
AVAILABLE_MODELS = [
{"id": "gpt-4-turbo", "object": "model", "created": int(time.time()), "owned_by": "system"},
{"id": "gpt-4o", "object": "model", "created": int(time.time()), "owned_by": "system"},
{"id": "gpt-3.5-turbo", "object": "model", "created": int(time.time()), "owned_by": "system"},
{"id": "dall-e-3", "object": "model", "created": int(time.time()), "owned_by": "system"},
{"id": "text-moderation-stable", "object": "model", "created": int(time.time()), "owned_by": "system"},
]
MODEL_ALIASES = {}
# --- FastAPI Application ---
app = FastAPI(
title="OpenAI Compatible API",
description="An adapter for various services to be compatible with the OpenAI API specification.",
version="1.0.0"
)
# --- Helper Function for Random ID Generation ---
def generate_random_id(prefix: str, length: int = 29) -> str:
"""
Generates a cryptographically secure, random alphanumeric ID.
"""
population = string.ascii_letters + string.digits
random_part = "".join(secrets.choice(population) for _ in range(length))
return f"{prefix}{random_part}"
# === API Endpoints ===
@app.get("/v1/models")
async def list_models():
"""Lists the available models."""
return {"object": "list", "data": AVAILABLE_MODELS}
# === Chat Completion ===
class Message(BaseModel):
role: str
content: str
class ChatRequest(BaseModel):
messages: List[Message]
model: str
stream: Optional[bool] = False
tools: Optional[Any] = None
@app.post("/v1/chat/completions")
async def chat_completion(request: ChatRequest):
"""
Handles chat completion requests, supporting both streaming and non-streaming responses.
"""
model_id = MODEL_ALIASES.get(request.model, request.model)
chat_id = generate_random_id("chatcmpl-")
headers = {
'accept': 'text/event-stream',
'content-type': 'application/json',
'origin': 'https://www.chatwithmono.xyz',
'referer': 'https://www.chatwithmono.xyz/',
'user-agent': 'Mozilla/5.0',
}
if request.tools:
# Handle tool by giving in system prompt.
# Tool call must be encoded in <tool_call><tool_call> XML tag.
tool_prompt = f"""You have access to the following tools . To call a tool, please respond with JSON for a tool call within <tool_call><tool_call> XML tag. Respond in the format {{"name": tool name, "parameters": dictionary of argument name and its value}}. Do not use variables.
Tools:
{";".join(f"<tool>{tool}</tool>" for tool in request.tools)}
Response Format for tool call:
For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
<tool_call>
{{"name": <function-name>, "arguments": <args-json-object>}}
</tool_call>
Example of tool calling:
<tool_call>
{{"name": "get_weather", "parameters": {{"city": "New York"}}}}
</tool_call>
Using tools is recommended.
"""
if request.messages[0].role == "system":
request.messages[0].content += "\n\n" + tool_prompt
else:
request.messages.insert(0, {"role": "system", "content": tool_prompt})
request_data = request.model_dump(exclude_unset=True)
payload = {
"messages": request_data["messages"],
"model": model_id
}
if request.stream:
async def event_stream():
created = int(time.time())
is_first_chunk = True
usage_info = None
is_tool_call = False
chunks_buffer = []
max_initial_chunks = 4 # Number of initial chunks to buffer
try:
async with httpx.AsyncClient(timeout=120) as client:
async with client.stream("POST", "https://www.chatwithmono.xyz/api/chat", headers=headers, json=payload) as response:
response.raise_for_status()
async for line in response.aiter_lines():
if not line: continue
if line.startswith("0:"):
try:
content_piece = json.loads(line[2:])
print(content_piece)
# Buffer the first few chunks
if len(chunks_buffer) < max_initial_chunks:
chunks_buffer.append(content_piece)
continue
# Process the buffered chunks if we haven't already
if chunks_buffer and not is_tool_call:
full_buffer = ''.join(chunks_buffer)
if "<tool_call>" in full_buffer:
print("Tool call detected")
is_tool_call = True
# Process the current chunk
if is_tool_call:
chunks_buffer.append(content_piece)
full_buffer = ''.join(chunks_buffer)
if "</tool_call>" in full_buffer:
print("Tool call End detected")
# Process tool call in the current chunk
tool_call_str = full_buffer.split("<tool_call>")[1].split("</tool_call>")[0]
tool_call_json = json.loads(tool_call_str.strip())
delta = {
"content": None,
"tool_calls": [{
"index": 0,
"id": generate_random_id("call_"),
"type": "function",
"function": {
"name": tool_call_json["name"],
"arguments": json.dumps(tool_call_json["parameters"])
}
}]
}
chunk_data = {
"id": chat_id, "object": "chat.completion.chunk", "created": created,
"model": model_id,
"choices": [{"index": 0, "delta": delta, "finish_reason": None}],
"usage": None
}
yield f"data: {json.dumps(chunk_data)}\n\n"
else:
continue
else:
# Regular content
if is_first_chunk:
delta = {"content": "".join(chunks_buffer), "tool_calls": None}
delta["role"] = "assistant"
is_first_chunk = False
chunk_data = {
"id": chat_id, "object": "chat.completion.chunk", "created": created,
"model": model_id,
"choices": [{"index": 0, "delta": delta, "finish_reason": None}],
"usage": None
}
yield f"data: {json.dumps(chunk_data)}\n\n"
delta = {"content": content_piece, "tool_calls": None}
chunk_data = {
"id": chat_id, "object": "chat.completion.chunk", "created": created,
"model": model_id,
"choices": [{"index": 0, "delta": delta, "finish_reason": None}],
"usage": None
}
yield f"data: {json.dumps(chunk_data)}\n\n"
except json.JSONDecodeError: continue
elif line.startswith(("e:", "d:")):
try:
usage_info = json.loads(line[2:]).get("usage")
except (json.JSONDecodeError, AttributeError): pass
break
final_usage = None
if usage_info:
prompt_tokens = usage_info.get("promptTokens", 0)
completion_tokens = usage_info.get("completionTokens", 0)
final_usage = {
"prompt_tokens": prompt_tokens, "completion_tokens": completion_tokens,
"total_tokens": prompt_tokens + completion_tokens,
}
done_chunk = {
"id": chat_id, "object": "chat.completion.chunk", "created": created, "model": model_id,
"choices": [{
"index": 0,
"delta": {"role": "assistant", "content": None, "function_call": None, "tool_calls": None},
"finish_reason": "stop"
}],
"usage": final_usage
}
yield f"data: {json.dumps(done_chunk)}\n\n"
except httpx.HTTPStatusError as e:
error_content = {
"error": {
"message": f"Upstream API error: {e.response.status_code}. Details: {e.response.text}",
"type": "upstream_error", "code": str(e.response.status_code)
}
}
yield f"data: {json.dumps(error_content)}\n\n"
finally:
yield "data: [DONE]\n\n"
return StreamingResponse(event_stream(), media_type="text/event-stream")
else: # Non-streaming
assistant_response, usage_info = "", {}
tool_call_json = None
try:
async with httpx.AsyncClient(timeout=120) as client:
async with client.stream("POST", "https://www.chatwithmono.xyz/api/chat", headers=headers, json=payload) as response:
response.raise_for_status()
async for chunk in response.aiter_lines():
if chunk.startswith("0:"):
try: assistant_response += json.loads(chunk[2:])
except: continue
elif chunk.startswith(("e:", "d:")):
try: usage_info = json.loads(chunk[2:]).get("usage", {})
except: continue
if "<tool_call>" in assistant_response and "</tool_call>" in assistant_response:
tool_call_str = assistant_response.split("<tool_call>")[1].split("</tool_call>")[0]
tool_call = json.loads(tool_call_str.strip())
tool_call_json = [{"id": generate_random_id("call_"),"function": {"name": tool_call["name"], "arguments": json.dumps(tool_call["parameters"])}}]
return JSONResponse(content={
"id": chat_id, "object": "chat.completion", "created": int(time.time()), "model": model_id,
"choices": [{"index": 0, "message": {"role": "assistant", "content": assistant_response if tool_call_json is None else None, "tool_calls": tool_call_json}, "finish_reason": "stop"}],
"usage": {
"prompt_tokens": usage_info.get("promptTokens", 0),
"completion_tokens": usage_info.get("completionTokens", 0),
"total_tokens": usage_info.get("promptTokens", 0) + usage_info.get("completionTokens", 0),
}
})
except httpx.HTTPStatusError as e:
return JSONResponse(status_code=e.response.status_code, content={"error": {"message": f"Upstream API error. Details: {e.response.text}", "type": "upstream_error"}})
# === Image Generation ===
class ImageGenerationRequest(BaseModel):
prompt: str
aspect_ratio: Optional[str] = "1:1"
n: Optional[int] = 1
user: Optional[str] = None
model: Optional[str] = "default"
@app.post("/v1/images/generations")
async def generate_images(request: ImageGenerationRequest):
"""Handles image generation requests."""
results = []
try:
async with httpx.AsyncClient(timeout=120) as client:
for _ in range(request.n):
model = request.model or "default"
if model in ["gpt-image-1", "dall-e-3", "dall-e-2", "nextlm-image-1"]:
headers = {'Content-Type': 'application/json', 'User-Agent': 'Mozilla/5.0', 'Referer': 'https://www.chatwithmono.xyz/'}
payload = {"prompt": request.prompt, "model": model}
resp = await client.post("https://www.chatwithmono.xyz/api/image", headers=headers, json=payload)
resp.raise_for_status()
data = resp.json()
b64_image = data.get("image")
if not b64_image: return JSONResponse(status_code=502, content={"error": "Missing base64 image in response"})
if SNAPZION_API_KEY:
upload_headers = {"Authorization": SNAPZION_API_KEY}
upload_files = {'file': ('image.png', base64.b64decode(b64_image), 'image/png')}
upload_resp = await client.post(SNAPZION_UPLOAD_URL, headers=upload_headers, files=upload_files)
upload_resp.raise_for_status()
upload_data = upload_resp.json()
image_url = upload_data.get("url")
else:
image_url = f"data:image/png;base64,{b64_image}"
results.append({"url": image_url, "b64_json": b64_image, "revised_prompt": data.get("revised_prompt")})
else:
params = {"prompt": request.prompt, "aspect_ratio": request.aspect_ratio, "link": "typegpt.net"}
resp = await client.get(IMAGE_API_URL, params=params)
resp.raise_for_status()
data = resp.json()
results.append({"url": data.get("image_link"), "b64_json": data.get("base64_output")})
except httpx.HTTPStatusError as e:
return JSONResponse(status_code=502, content={"error": f"Image generation failed. Upstream error: {e.response.status_code}", "details": e.response.text})
except Exception as e:
return JSONResponse(status_code=500, content={"error": "An internal error occurred.", "details": str(e)})
return {"created": int(time.time()), "data": results}
# === Moderation Endpoint ===
class ModerationRequest(BaseModel):
input: Union[str, List[str]]
model: Optional[str] = "text-moderation-stable"
@app.post("/v1/moderations")
async def create_moderation(request: ModerationRequest):
"""
Handles moderation requests, conforming to the OpenAI API specification.
Includes a custom 'reason' field in the result if provided by the upstream API.
"""
input_texts = [request.input] if isinstance(request.input, str) else request.input
if not input_texts:
return JSONResponse(status_code=400, content={"error": {"message": "Request must have at least one input string.", "type": "invalid_request_error"}})
moderation_url = "https://www.chatwithmono.xyz/api/moderation"
headers = {
'Content-Type': 'application/json',
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/138.0.0.0 Safari/537.36',
'Referer': 'https://www.chatwithmono.xyz/',
}
results = []
try:
async with httpx.AsyncClient(timeout=30) as client:
for text_input in input_texts:
payload = {"text": text_input}
resp = await client.post(moderation_url, headers=headers, json=payload)
resp.raise_for_status()
upstream_data = resp.json()
# --- Transform upstream response to OpenAI format ---
upstream_categories = upstream_data.get("categories", {})
openai_categories = {
"hate": upstream_categories.get("hate", False), "hate/threatening": False,
"harassment": False, "harassment/threatening": False,
"self-harm": upstream_categories.get("self-harm", False), "self-harm/intent": False, "self-harm/instructions": False,
"sexual": upstream_categories.get("sexual", False), "sexual/minors": False,
"violence": upstream_categories.get("violence", False), "violence/graphic": False,
}
category_scores = {k: 1.0 if v else 0.0 for k, v in openai_categories.items()}
flagged = upstream_data.get("overall_sentiment") == "flagged"
result_item = {
"flagged": flagged,
"categories": openai_categories,
"category_scores": category_scores,
}
# --- NEW: Conditionally add the 'reason' field ---
# This is a custom extension to the OpenAI spec to provide more detail.
reason = upstream_data.get("reason")
if reason:
result_item["reason"] = reason
results.append(result_item)
except httpx.HTTPStatusError as e:
return JSONResponse(
status_code=502, # Bad Gateway
content={"error": {"message": f"Moderation failed. Upstream error: {e.response.status_code}", "type": "upstream_error", "details": e.response.text}}
)
except Exception as e:
return JSONResponse(status_code=500, content={"error": {"message": "An internal error occurred during moderation.", "type": "internal_error", "details": str(e)}})
# Build the final OpenAI-compatible response
final_response = {
"id": generate_random_id("modr-"),
"model": request.model,
"results": results,
}
return JSONResponse(content=final_response)
# --- Main Execution ---
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000)
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