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import datetime
from flask import Flask, request, Response, json
import requests
from uuid import uuid4
import time
app = Flask(__name__)
MODEL_MAPPING = {
"deepseek": "deepseek/deepseek-chat",
"gpt-4o-mini": "openai/gpt-4o-mini",
"gemini-flash-1.5": "google/gemini-flash-1.5",
"deepseek-reasoner": "deepseek-reasoner",
"minimax-01": "minimax/minimax-01"
}
def make_heck_request(question, session_id, messages, actual_model):
previous_question = previous_answer = None
if len(messages) >= 2:
for i in range(len(messages)-2, -1, -1):
if messages[i]["role"] == "user":
previous_question = messages[i]["content"]
if i+1 < len(messages) and messages[i+1]["role"] == "assistant":
previous_answer = messages[i+1]["content"]
break
payload = {
"model": actual_model,
"question": question,
"language": "Chinese",
"sessionId": session_id,
"previousQuestion": previous_question,
"previousAnswer": previous_answer
}
headers = {
"Content-Type": "application/json",
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
}
return requests.post(
"https://gateway.aiapilab.com/api/ha/v1/chat",
json=payload,
headers=headers,
stream=True
)
def stream_response(question, session_id, messages, request_model, actual_model):
resp = make_heck_request(question, session_id, messages, actual_model)
is_answering = False
for line in resp.iter_lines():
if line:
line = line.decode('utf-8')
if not line.startswith('data: '):
continue
content = line[6:].strip()
if content == "[ANSWER_START]":
is_answering = True
chunk = {
"id": session_id,
"object": "chat.completion.chunk",
"created": int(time.time()),
"model": request_model,
"choices": [{
"index": 0,
"delta": {"role": "assistant"},
}]
}
yield f"data: {json.dumps(chunk, ensure_ascii=False)}\n\n"
continue
if content == "[ANSWER_DONE]":
chunk = {
"id": session_id,
"object": "chat.completion.chunk",
"created": int(time.time()),
"model": request_model,
"choices": [{
"index": 0,
"delta": {},
"finish_reason": "stop"
}]
}
yield f"data: {json.dumps(chunk, ensure_ascii=False)}\n\n"
break
if is_answering and content and not content.startswith("[RELATE_Q"):
chunk = {
"id": session_id,
"object": "chat.completion.chunk",
"created": int(time.time()),
"model": request_model,
"choices": [{
"index": 0,
"delta": {"content": content},
}]
}
yield f"data: {json.dumps(chunk, ensure_ascii=False)}\n\n"
def normal_response(question, session_id, messages, request_model, actual_model):
resp = make_heck_request(question, session_id, messages, actual_model)
full_content = []
is_answering = False
for line in resp.iter_lines():
if line:
line = line.decode('utf-8')
if line.startswith('data: '):
content = line[6:].strip()
if content == "[ANSWER_START]":
is_answering = True
elif content == "[ANSWER_DONE]":
break
elif is_answering:
full_content.append(content)
response = {
"id": session_id,
"object": "chat.completion",
"created": int(time.time()),
"model": request_model,
"choices": [{
"index": 0,
"message": {
"role": "assistant",
"content": "".join(full_content)
},
"finish_reason": "stop"
}]
}
return response
@app.get("/")
async def health_check():
return {
"health": "ok",
"timestamp": datetime.datetime.now().isoformat(),
"service": "AI API Forwarding Service",
"version": "1.0"
}
@app.route("/v1/chat/completions", methods=["POST"])
def chat_completions():
data = request.json
model = MODEL_MAPPING.get(data["model"])
if not model:
return {"error": "Unsupported Model"}, 400
question = next((msg["content"] for msg in reversed(data["messages"])
if msg["role"] == "user"), None)
session_id = str(uuid4())
if data.get("stream"):
return Response(
stream_response(question, session_id, data["messages"],
data["model"], model),
mimetype="text/event-stream"
)
else:
return normal_response(question, session_id, data["messages"],
data["model"], model)
# if __name__ == "__main__":
# app.run(host='0.0.0.0', port=8801) |