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import re
from typing import Dict, Optional
from fastapi import FastAPI
from pydantic import BaseModel
from mlx_lm import load, generate
from mlx_lm.sample_utils import make_sampler
base_model_id = "qwen2.5-0.5b-instruct-sumobot"
lora_adapter_or_id = "qwen2.5-0.5b-instruct-sumobot"
model, tokenizer = load(base_model_id, adapter_path=lora_adapter_or_id)
def parse_action(output: str):
# Mapping of shorthand to full action names
action_map = {
"SK": "Skill",
"DS": "Dash",
"FWD": "Accelerate",
"TL": "TurnLeft",
"TR": "TurnRight",
}
actions: Dict[str, Optional[float]] = {}
for part in [p.strip() for p in output.split(",")]:
name = part
duration = None
direct_match = re.match(r"^([A-Za-z]+)\s*([\d.]+)$", part)
if direct_match:
name = direct_match.group(1).strip()
duration = float(direct_match.group(2))
# Normalize shorthand to full name
for short, full in action_map.items():
if name.upper().startswith(short):
name = full
break
actions[name] = duration
return {"action": actions}
def get_finetuned_action(query_state):
# Inference with chat template
messages = [
{"role": "system", "content": "You are a Sumobot assistant that decides actions based on game state."},
{"role": "user", "content": f"Given this game state: {query_state}"},
]
# Apply the tokenizer's built-in chat template
chat_prompt = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
result = ""
for token in generate(model, tokenizer, chat_prompt, max_tokens=50):
result += token
parsedResult = parse_action(result)
return parsedResult
# --------- API Setup ----------
app = FastAPI()
class QueryInput(BaseModel):
state: str
@app.post("/query")
def query(input: QueryInput):
return get_finetuned_action(input.state)
# Run with: python rag_api.py
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=8000)
# TEST CURL
# curl --location 'http://localhost:8000/query' \
# --header 'Content-Type: application/json' \
# --data '{
# "state":"AngleToEnemy=7.77, AngleToEnemyScore=0.99, DistanceToEnemyScore=0.76, NearBorderArenaScore=0.81, FacingToArena=-0.99"
# }' |