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# app/main.py
import os
import json
import logging
import asyncio
from typing import Optional
import uvicorn
from fastapi import FastAPI, HTTPException
from fastapi.responses import StreamingResponse
from pydantic import BaseModel
from .model import PlutusModel, SummaryModel
from .recommender import Recommender
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("plutus.api")
_CACHE_DIR = os.getenv("HF_HOME", "/home/user/app")
DEFAULT_RECOMMEND_JSON = os.getenv(_CACHE_DIR, "recommend.json")
RECOMMEND_INDEX_PATH = os.path.join(_CACHE_DIR, "plutus_recommend_index.faiss")
RECOMMEND_META_PATH = os.path.join(_CACHE_DIR, "plutus_recommend_meta.json")
class GenerateCache:
last_query: Optional[str] = None
last_topic: Optional[str] = None
last_personality: Optional[str] = None
last_level: Optional[str] = None
last_output: Optional[str] = None
GEN_CACHE = GenerateCache()
logger.info("Loading shared Plutus LLM and recommender...")
plutus_model = PlutusModel()
summary_model = SummaryModel()
recommender = Recommender(
recommend_json_path=DEFAULT_RECOMMEND_JSON,
index_path=RECOMMEND_INDEX_PATH,
meta_path=RECOMMEND_META_PATH
)
app = FastAPI(title="Plutus Learner API")
class GenerateRequest(BaseModel):
personality: str
level: str
topic: str
query: str
max_new_tokens: int = 700
temperature: float = 0.5
top_p: float = 0.9
class RecommendRequest(BaseModel):
top_k: int = 5
class SummaryRequest(BaseModel):
top_k: int = 5
@app.get("/health")
async def health():
return {
"status": "ok",
"device": plutus_model.device
}
@app.post("/generate")
async def generate(req: GenerateRequest):
prompt = plutus_model.create_prompt(
req.personality,
req.level,
req.topic,
req.query
)
async def event_generator():
full_text = ""
for chunk in plutus_model.generate(
prompt,
max_new_tokens=req.max_new_tokens,
temperature=req.temperature,
top_p=req.top_p
):
full_text += chunk + "\n"
yield f"data: {json.dumps({'text': chunk})}\n\n"
await asyncio.sleep(0)
# Cache final result
GEN_CACHE.last_query = req.query
GEN_CACHE.last_topic = req.topic
GEN_CACHE.last_personality = req.personality
GEN_CACHE.last_level = req.level
GEN_CACHE.last_output = full_text.strip()
yield "data: [DONE]\n\n"
return StreamingResponse(
event_generator(),
media_type="text/event-stream"
)
@app.post("/recommend")
async def recommend(req: RecommendRequest):
if GEN_CACHE.last_query is None:
raise HTTPException(400, "No query found. Call /generate first.")
results = recommender.recommend_for_query(
query=GEN_CACHE.last_query,
top_k=req.top_k,
topic_boost=GEN_CACHE.last_topic
)
return {
"query": GEN_CACHE.last_query,
"results": [
{"topic": r["topic"], "type": r["type"], "url": r["url"]}
for r in results
]
}
@app.post("/summary")
async def summary(req: SummaryRequest):
if GEN_CACHE.last_output is None:
raise HTTPException(400, "No generate output found. Call /generate first.")
recs = recommender.recommend_for_query(
query=GEN_CACHE.last_query,
top_k=req.top_k,
topic_boost=GEN_CACHE.last_topic
)
async def event_generator():
for chunk in summary_model.summarize_text(
full_teaching=GEN_CACHE.last_output,
topic=GEN_CACHE.last_topic,
level=GEN_CACHE.last_level,
recommended=recs,
max_new_tokens=300
):
yield f"data: {json.dumps({'summary': chunk})}\n\n"
await asyncio.sleep(0)
yield "data: [DONE]\n\n"
return StreamingResponse(
event_generator(),
media_type="text/event-stream"
)
@app.post("/admin/build_index")
async def build_index(force: bool = False):
recommender.build_index(force=force)
return {"indexed": len(recommender.meta)}
if __name__ == "__main__":
uvicorn.run("app.main:app", host="0.0.0.0", port=7860)
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