Ayomide
140 lines
4.1 KiB
Python
140 lines
4.1 KiB
Python
from fastapi import FastAPI, HTTPException
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from fastapi.middleware.cors import CORSMiddleware
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from .news_fetcher import fetch_all_news, save_raw_news, save_processed_news
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from .recommender import recommend_similar, process_articles_for_vector_db
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from .recommender import analyze_article_with_groq
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from .recommender import get_personalized_recommendations, vector_db
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from .vector_store import VectorDB
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from .config import Config
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import os
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app = FastAPI(title="DS Task AI News", version="1.0.0")
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# Add CORS middleware
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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@app.on_event("startup")
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async def startup_event():
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"""Initialize the application"""
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# Create necessary directories
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os.makedirs(Config.RAW_NEWS_PATH, exist_ok=True)
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os.makedirs(Config.PROCESSED_NEWS_PATH, exist_ok=True)
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os.makedirs("data", exist_ok=True)
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# Load existing vector database if available
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if os.path.exists(Config.VECTOR_DB_PATH):
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vector_db.load_index(Config.VECTOR_DB_PATH)
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@app.get("/")
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async def root():
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"""Root endpoint"""
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return {"message": "DS Task AI News API", "version": "1.0.0"}
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@app.get("/fetch-news")
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async def fetch_news():
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"""Fetch news from RSS feeds"""
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try:
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articles = fetch_all_news()
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if not articles:
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raise HTTPException(status_code=404, detail="No articles found")
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# Save raw news
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raw_file = save_raw_news(articles)
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# Process articles for vector database
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process_articles_for_vector_db(articles)
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# Save processed news
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processed_file = save_processed_news(articles)
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# Save vector database
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vector_db.save_index(Config.VECTOR_DB_PATH)
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return {
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"message": "News fetched successfully",
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"articles_count": len(articles),
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"raw_file": raw_file,
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"processed_file": processed_file,
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"articles": articles
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}
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"Error fetching news: {str(e)}")
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@app.get("/recommend-news")
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async def recommend_news(article_id: str):
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"""Retrieve similar news based on the selected article"""
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try:
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recommendations = recommend_similar(article_id)
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if not recommendations:
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raise HTTPException(status_code=404, detail="No recommendations found")
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return {
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"article_id": article_id,
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"recommendations": recommendations,
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"count": len(recommendations)
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}
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"Error getting recommendations: {str(e)}")
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@app.get("/analyze-article")
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async def analyze_article(article_id: str):
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"""Analyze article using Groq LLM"""
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try:
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article = vector_db.get_article_by_id(article_id)
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if not article:
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raise HTTPException(status_code=404, detail="Article not found")
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article_text = f"{article['title']} {article['content']}"
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analysis = analyze_article_with_groq(article_text)
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return {
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"article_id": article_id,
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"article": article,
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"analysis": analysis
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}
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"Error analyzing article: {str(e)}")
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@app.get("/personalized-recommendations")
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async def personalized_recommendations(interests: str):
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"""Get personalized recommendations based on user interests"""
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try:
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recommendations = get_personalized_recommendations(interests)
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return {
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"interests": interests,
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"recommendations": recommendations,
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"count": len(recommendations)
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}
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"Error getting personalized recommendations: {str(e)}")
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@app.get("/health")
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async def health_check():
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"""Health check endpoint"""
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return {"status": "healthy", "database_articles": len(vector_db.articles)}
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=8000)
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