112 lines
2.4 KiB
Python
112 lines
2.4 KiB
Python
import os
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import sys
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import uvicorn
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from fastapi import Body, FastAPI
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from fastapi.responses import FileResponse
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from fastapi.staticfiles import StaticFiles
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from pydantic import BaseModel
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# Import core LLM logic
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from llm import SOURCES_DIR, generate_text, load_or_train_model
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# --- Configuration ---
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# Models to pre-load on startup
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PRELOAD_N_GRAMS = [2, 3, 4, 5]
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UI_DIR = "ui"
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# --- Globals ---
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# Cache for loaded models: {n: model}
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MODEL_CACHE = {}
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# --- Pydantic Models ---
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class PredictRequest(BaseModel):
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prompt: str
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temperature: float = 1.6
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n: int = 4
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length: int = 5
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class PredictResponse(BaseModel):
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prediction: str
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# --- FastAPI App ---
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app = FastAPI()
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def get_model_for_n(n: int):
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"""
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Retrieves the model for a specific N from cache, or loads/trains it.
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"""
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global MODEL_CACHE
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if n in MODEL_CACHE:
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return MODEL_CACHE[n]
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print(f"Loading/Training model for N={n}...")
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model = load_or_train_model(SOURCES_DIR, n)
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MODEL_CACHE[n] = model
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return model
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@app.on_event("startup")
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def startup_event():
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"""
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On server startup, pre-load models for all specified N-grams.
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"""
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print("Server starting up. Pre-loading models...")
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for n in PRELOAD_N_GRAMS:
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get_model_for_n(n)
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print(f"Models for N={PRELOAD_N_GRAMS} loaded. Server is ready.")
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@app.post("/api/predict", response_model=PredictResponse)
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async def predict(request: PredictRequest):
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"""
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API endpoint to get the next word prediction.
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"""
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n = max(2, min(request.n, 5))
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model = get_model_for_n(n)
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if not model:
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return {"prediction": ""}
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length = max(1, min(request.length, 500))
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prediction = generate_text(
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model,
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start_prompt=request.prompt,
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length=length,
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temperature=request.temperature,
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)
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return PredictResponse(prediction=prediction)
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@app.get("/api")
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async def api_docs():
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"""
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API documentation page.
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"""
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return FileResponse(os.path.join(UI_DIR, "api.html"))
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# --- Static Files and Root ---
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app.mount("/ui", StaticFiles(directory=UI_DIR), name="ui")
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@app.get("/")
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async def read_root():
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return FileResponse(os.path.join(UI_DIR, "index.html"))
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def run():
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# Read port from environment variable, default to 8000
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port = int(os.environ.get("PORT", 8000))
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uvicorn.run(app, host="0.0.0.0", port=port)
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if __name__ == "__main__":
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run()
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