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import logging |
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from fastapi import FastAPI, HTTPException, BackgroundTasks |
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from fastapi.middleware.cors import CORSMiddleware |
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from pydantic import BaseModel |
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from typing import Dict, Optional |
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import uuid |
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from datetime import datetime, timedelta |
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import asyncio |
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import random |
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from sentence_transformers import SentenceTransformer |
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from transformers import T5Tokenizer, T5ForConditionalGeneration |
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from models.LexRank import degree_centrality_scores |
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import torch |
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import nltk |
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import spacy |
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from psycopg2 import sql |
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app = FastAPI(title="Kairos News API", version="1.0") |
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app.add_middleware( |
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CORSMiddleware, |
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allow_origins=["*"], |
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allow_methods=["*"], |
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allow_headers=["*"], |
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) |
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url = "" |
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key = "" |
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opts = ClientOptions().replace(schema="articles") |
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supabase = create_client(url, key, options=opts) |
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nlp = spacy.load("pt_core_news_md") |
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model_embedding = SentenceTransformer("paraphrase-multilingual-MiniLM-L12-v2") |
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token_name = 'unicamp-dl/ptt5-base-portuguese-vocab' |
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model_name = 'recogna-nlp/ptt5-base-summ' |
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tokenizer = T5Tokenizer.from_pretrained(token_name) |
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model_summ = T5ForConditionalGeneration.from_pretrained(model_name).to('cuda') |
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jobs_db: Dict[str, Dict] = {} |
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class PostRequest(BaseModel): |
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query: str |
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topic: str |
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start_date: str |
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end_date: str |
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class JobStatus(BaseModel): |
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id: str |
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status: str |
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created_at: datetime |
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completed_at: Optional[datetime] |
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request: PostRequest |
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result: Optional[Dict] |
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@app.post("/index", response_model=JobStatus) |
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async def create_job(request: PostRequest, background_tasks: BackgroundTasks): |
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"""Create a new processing job""" |
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job_id = str(uuid.uuid4()) |
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jobs_db[job_id] = { |
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"status": "processing", |
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"created_at": datetime.now(), |
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"completed_at": None, |
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"request": request.dict(), |
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"result": None |
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} |
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logging.info(f"Job {job_id} created with request: {request.query}") |
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background_tasks.add_task(process_job, job_id) |
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return { |
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"id": job_id, |
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"status": "processing", |
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"created_at": jobs_db[job_id]["created_at"], |
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"completed_at": None, |
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"request": request, |
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"result": None |
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} |
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@app.get("/loading", response_model=JobStatus) |
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async def get_job_status(id: str): |
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"""Check job status with timeout simulation""" |
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if id not in jobs_db: |
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raise HTTPException(status_code=404, detail="Job not found") |
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job = jobs_db[id] |
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elapsed = datetime.now() - job["created_at"] |
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if elapsed < timedelta(seconds=3): |
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await asyncio.sleep(1) |
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if random.random() < 0.1 and job["status"] == "processing": |
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job["status"] = "failed" |
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job["result"] = {"error": "Random processing failure"} |
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return { |
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"id": id, |
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"status": job["status"], |
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"created_at": job["created_at"], |
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"completed_at": job["completed_at"], |
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"request": job["request"], |
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"result": job["result"] |
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} |
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async def process_job(job_id: str): |
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"""Background task to simulate processing""" |
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await asyncio.sleep(random.uniform(3, 10)) |
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if job_id in jobs_db: |
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jobs_db[job_id]["status"] = "completed" |
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jobs_db[job_id]["completed_at"] = datetime.now() |
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jobs_db[job_id]["result"] = { |
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"query": jobs_db[job_id]["request"]["query"], |
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"topic": jobs_db[job_id]["request"]["topic"], |
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"date_range": jobs_db[job_id]["request"]["date"], |
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"analysis": f"Processed results for {jobs_db[job_id]['request']['query']}", |
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"sources": ["Source A", "Source B", "Source C"], |
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"summary": "This is a generated summary based on your query." |
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} |
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@app.get("/jobs") |
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async def list_jobs(): |
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"""Debug endpoint to view all jobs""" |
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return jobs_db |