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Create app/main.py
Browse files- app/main.py +62 -0
app/main.py
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"""
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CryptoSentinel AI – FastAPI entry‑point
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"""
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import os
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from fastapi import FastAPI, Request, BackgroundTasks
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from fastapi.responses import HTMLResponse, StreamingResponse, JSONResponse
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from fastapi.staticfiles import StaticFiles
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from apscheduler.schedulers.background import BackgroundScheduler
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from dotenv import load_dotenv
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from price_fetcher import fetch_prices, CURRENT_PRICES
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from sentiment import analyze_sentiment, SentimentCache
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from pathlib import Path
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import json
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import asyncio
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load_dotenv()
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app = FastAPI(title="CryptoSentinel AI")
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templ_dir = Path(__file__).parent / "templates"
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app.mount("/static", StaticFiles(directory=Path(__file__).parent / "static"), name="static")
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scheduler = BackgroundScheduler(daemon=True)
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scheduler.add_job(fetch_prices, "interval", seconds=10) # refresh price cache
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scheduler.start()
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@app.on_event("shutdown")
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def shutdown_event():
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scheduler.shutdown(wait=False)
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# ---------- ROUTES -----------------------------------------------------------
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@app.get("/", response_class=HTMLResponse)
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async def index(request: Request):
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from fastapi.templating import Jinja2Templates
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templates = Jinja2Templates(directory=str(templ_dir))
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return templates.TemplateResponse("index.html", {"request": request})
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@app.get("/prices")
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async def prices():
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"""JSON endpoint for latest cached prices."""
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return JSONResponse(CURRENT_PRICES)
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@app.post("/sentiment")
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async def sentiment(request: Request, background_tasks: BackgroundTasks):
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body = await request.json()
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text = body.get("text", "")
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# Run expensive inference in background to keep latency low
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background_tasks.add_task(SentimentCache.compute, text)
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return {"status": "processing"}
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@app.get("/sentiment/stream")
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async def sentiment_stream():
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"""Server‑sent events stream – pushes sentiment results."""
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async def event_generator():
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last_id = 0
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while True:
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if SentimentCache.latest_id != last_id:
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last_id = SentimentCache.latest_id
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data = json.dumps(SentimentCache.latest_result)
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yield f"id:{last_id}\ndata:{data}\n\n"
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await asyncio.sleep(1)
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return StreamingResponse(event_generator(), media_type="text/event-stream")
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