Refactor backend configuration and enhance news fetching functionality

- Introduced a Config dataclass in config.py to manage API keys, RSS feeds, and directory paths more effectively.
- Updated the NewsFetcher class to include retry logic for fetching articles from RSS feeds.
- Modified the EmbeddingGenerator and NewsRecommender classes to utilize the new configuration structure.
- Enhanced main.py to implement API token verification for secure access to news fetching and recommendations.

README.md

@@ -55,8 +55,7 @@ DS Task AI News is a web application that uses AI technologies to fetch, analyze
      ```
 
 4. Run the application:
-   ```
-   python backend/main.py
+   ```   python backend/main.py
    ```
 
 5. Open your web browser and navigate to `http://localhost:8000`.
@@ -104,3 +103,4 @@ This project is licensed under the MIT License - see the LICENSE file for detail
 - [Cohere](https://cohere.ai/)
 - [Pinecone](https://www.pinecone.io/)
 - [Groq](https://groq.com/)
+

backend/config.py

@@ -1,5 +1,10 @@
+from dataclasses import dataclass, field
+from typing import List, Optional
 import os
 from dotenv import load_dotenv
+from fastapi import HTTPException, Depends, Security
+from fastapi.security import APIKeyHeader
+from starlette.status import HTTP_403_FORBIDDEN
 
 # Load environment variables
 
@@ -9,30 +14,59 @@ from dotenv import load_dotenv
 # Load environment variables from the specified path
 load_dotenv()
 
-# API Keys
-COHERE_API_KEY = os.getenv("COHERE_API_KEY")
-GROQ_API_KEY = os.getenv("GROQ_API_KEY")
-PINECONE_API_KEY = os.getenv("PINECONE_API_KEY")
+@dataclass
+class Config:
+    # API Keys
+    cohere_api_key: str = os.getenv("COHERE_API_KEY", "")
+    groq_api_key: str = os.getenv("GROQ_API_KEY", "")
+    pinecone_api_key: str = os.getenv("PINECONE_API_KEY", "")
+    api_token: str = os.getenv("API_TOKEN", "default_secret_token")  # Default token for development
 
-# Pinecone Configuration
-PINECONE_INDEX_NAME = os.getenv("PINECONE_INDEX_NAME", "news-articles")
+    # Pinecone Configuration
+    pinecone_index_name: str = os.getenv("PINECONE_INDEX_NAME", "news-articles")
+    vector_dimension: int = 1024  # Cohere embedding dimension
+    top_k_results: int = 5
 
-# News Sources
-RSS_FEEDS = [
+    # News Sources
+    rss_feeds: List[str] = field(default_factory=lambda: [
         # "https://feeds.feedburner.com/TechCrunch/",
         # "https://www.theverge.com/rss/index.xml",
         "https://www.wired.com/feed/rss",
         "https://www.technologyreview.com/feed/",
-]
+    ])
 
-# Vector Database Settings
-VECTOR_DIMENSION = 1024  # Cohere embedding dimension
-TOP_K_RESULTS = 5
+    # Data Directories
+    raw_news_dir: str = "data/raw_news"
+    processed_news_dir: str = "data/processed_news"
 
-# Data Directories
-RAW_NEWS_DIR = "data/raw_news"
-PROCESSED_NEWS_DIR = "data/processed_news"
+    def __post_init__(self):
+        # Create directories if they don't exist
+        os.makedirs(self.raw_news_dir, exist_ok=True)
+        os.makedirs(self.processed_news_dir, exist_ok=True)
 
-# Create directories if they don't exist
-os.makedirs(RAW_NEWS_DIR, exist_ok=True)
-os.makedirs(PROCESSED_NEWS_DIR, exist_ok=True)
+# Create a global config instance
+config = Config()
+
+# API Key header
+api_key_header = APIKeyHeader(name="X-API-Token", auto_error=False)
+
+def verify_api_token(api_key: str):
+    """
+    Verify the API token from the request header.
+    
+    Args:
+        api_key: The API key from the request header
+        
+    Returns:
+        The API key if valid
+        
+    Raises:
+        HTTPException: If the API key is invalid
+    """
+    if api_key == config.api_token:
+        print(f"API key verified: {api_key}")
+        return api_key
+    raise HTTPException(
+        status_code=HTTP_403_FORBIDDEN, 
+        detail="Invalid API token"
+    )

backend/embeddings.py

@@ -1,10 +1,10 @@
 import cohere
-from typing import List, Dict, Any
-from config import COHERE_API_KEY
+from typing import List, Dict, Any, Optional
+from config import config
 
 class EmbeddingGenerator:
-    def __init__(self):
-        self.client = cohere.Client(COHERE_API_KEY)
+    def __init__(self, cohere_client: Optional[cohere.Client] = None):
+        self.client = cohere_client or cohere.Client(config.cohere_api_key)
 
     def generate_embeddings(self, texts: List[str]) -> List[List[float]]:
         """Generate embeddings for a list of texts using Cohere."""

backend/main.py

@@ -1,4 +1,4 @@
-from fastapi import FastAPI, HTTPException, Request
+from fastapi import FastAPI, HTTPException, Request, Depends
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.templating import Jinja2Templates
 from fastapi.responses import HTMLResponse
@@ -10,13 +10,20 @@ from news_fetcher import NewsFetcher
 from embeddings import EmbeddingGenerator
 from vector_store import VectorStore
 from recommender import NewsRecommender
-from config import RAW_NEWS_DIR, PROCESSED_NEWS_DIR
+from config import config
+from fastapi import HTTPException
 
 app = FastAPI(title="DS Task AI News API")
 
 # Configure templates
 templates = Jinja2Templates(directory="backend/templates")
 
+def verify_api_token(token: str):
+    if token == config.api_token:
+        print(f"API key verified: {token}")
+        return token
+    return None
+
 # Add custom filters
 def from_json(value):
     """Parse a JSON string into a Python object."""
@@ -51,7 +58,8 @@ async def root(request: Request):
     )
 
 @app.get("/fetch-news", response_class=HTMLResponse)
-async def fetch_news(request: Request):
+def fetch_news(request: Request, token: str = Depends(verify_api_token)):
+    # print(f"Fetching news with token: {token}")
     """Fetch news from RSS feeds and store in vector database."""
     try:
         result = news_fetcher.process()
@@ -59,11 +67,11 @@ async def fetch_news(request: Request):
             raise HTTPException(status_code=404, detail=result["message"])
         
         # Get the latest processed articles
-        processed_files = sorted(os.listdir(PROCESSED_NEWS_DIR), reverse=True)
+        processed_files = sorted(os.listdir(config.processed_news_dir), reverse=True)
         if not processed_files:
             raise HTTPException(status_code=404, detail="No processed articles found")
         
-        latest_file = os.path.join(PROCESSED_NEWS_DIR, processed_files[0])
+        latest_file = os.path.join(config.processed_news_dir, processed_files[0])
         with open(latest_file, 'r', encoding='utf-8') as f:
             articles = json.load(f)
             
@@ -81,7 +89,7 @@ async def fetch_news(request: Request):
         raise HTTPException(status_code=500, detail=str(e))
 
 @app.get("/recommend-news", response_class=HTMLResponse)
-async def recommend_news(request: Request, article_id: str = None, query: str = None):
+async def recommend_news(request: Request, article_id: str = None, query: str = None, token: str = Depends(verify_api_token)):
     """Get news recommendations based on article ID or search query."""
     try:
         if article_id:
@@ -129,7 +137,7 @@ async def recommend_news(request: Request, article_id: str = None, query: str =
         raise HTTPException(status_code=500, detail=str(e))
 
 @app.get("/article/{article_id}")
-async def get_article(article_id: str):
+async def get_article(article_id: str, token: str = Depends(verify_api_token)):
     """Get a specific article and its summary."""
     try:
         # Search for the article

backend/news_fetcher.py

@@ -3,12 +3,13 @@ import json
 import os
 import logging
 from datetime import datetime
-from typing import List, Dict, Any
-from config import RSS_FEEDS, RAW_NEWS_DIR, PROCESSED_NEWS_DIR
-from embeddings import EmbeddingGenerator
-from vector_store import VectorStore
+from typing import List, Dict, Any, Optional
 from bs4 import BeautifulSoup
 import re
+import time
+from config import config
+from embeddings import EmbeddingGenerator
+from vector_store import VectorStore
 
 # Configure logging
 logging.basicConfig(
@@ -22,10 +23,18 @@ logging.basicConfig(
 logger = logging.getLogger('NewsFetcher')
 
 class NewsFetcher:
-    def __init__(self):
-        self.feeds = RSS_FEEDS
-        self.embedding_generator = EmbeddingGenerator()
-        self.vector_store = VectorStore()
+    def __init__(
+        self,
+        embedding_generator: Optional[EmbeddingGenerator] = None,
+        vector_store: Optional[VectorStore] = None,
+        max_retries: int = 3,
+        retry_delay: int = 5
+    ):
+        self.feeds = config.rss_feeds
+        self.embedding_generator = embedding_generator or EmbeddingGenerator()
+        self.vector_store = vector_store or VectorStore()
+        self.max_retries = max_retries
+        self.retry_delay = retry_delay
         logger.info("NewsFetcher initialized with %d RSS feeds", len(self.feeds))
 
     def clean_html_content(self, html_content: str) -> str:
@@ -54,11 +63,21 @@ class NewsFetcher:
         return cleaned_text
 
     def fetch_rss_news(self, feed_url: str) -> List[Dict[str, Any]]:
-        """Fetch news articles from a single RSS feed."""
+        """Fetch news articles from a single RSS feed with retry logic."""
         logger.info("Fetching news from feed: %s", feed_url)
-        feed = feedparser.parse(feed_url)
         articles = []
         
+        for attempt in range(self.max_retries):
+            try:
+                feed = feedparser.parse(feed_url)
+                if not feed.entries:
+                    logger.warning("No entries found in feed %s (attempt %d/%d)", 
+                                 feed_url, attempt + 1, self.max_retries)
+                    if attempt < self.max_retries - 1:
+                        time.sleep(self.retry_delay)
+                        continue
+                    return []
+                
                 for entry in feed.entries:
                     # Get raw content with HTML
                     raw_content = entry.get("summary", "")
@@ -81,18 +100,25 @@ class NewsFetcher:
                 logger.info("Fetched %d articles from %s", len(articles), feed_url)
                 return articles
                 
+            except Exception as e:
+                logger.error("Error fetching from %s (attempt %d/%d): %s", 
+                           feed_url, attempt + 1, self.max_retries, str(e))
+                if attempt < self.max_retries - 1:
+                    time.sleep(self.retry_delay)
+                else:
+                    logger.error("Failed to fetch from %s after %d attempts", 
+                               feed_url, self.max_retries)
+                    return []
+
     def fetch_all_news(self) -> List[Dict[str, Any]]:
         """Fetch news from all configured RSS feeds."""
         logger.info("Starting to fetch news from all %d feeds", len(self.feeds))
         all_articles = []
         
         for feed_url in self.feeds:
-            try:
             articles = self.fetch_rss_news(feed_url)
             all_articles.extend(articles)
             logger.info("Successfully fetched %d articles from %s", len(articles), feed_url)
-            except Exception as e:
-                logger.error("Error fetching from %s: %s", feed_url, str(e))
         
         logger.info("Total articles fetched: %d", len(all_articles))
         return all_articles
@@ -101,7 +127,7 @@ class NewsFetcher:
         """Save raw articles to a JSON file."""
         timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
         filename = f"raw_news_{timestamp}.json"
-        filepath = os.path.join(RAW_NEWS_DIR, filename)
+        filepath = os.path.join(config.raw_news_dir, filename)
         
         logger.info("Saving %d raw articles to %s", len(articles), filepath)
         with open(filepath, "w", encoding="utf-8") as f:
@@ -114,7 +140,7 @@ class NewsFetcher:
         """Save processed articles with embeddings to a JSON file."""
         timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
         filename = f"processed_news_{timestamp}.json"
-        filepath = os.path.join(PROCESSED_NEWS_DIR, filename)
+        filepath = os.path.join(config.processed_news_dir, filename)
         
         # Create a copy of articles without raw_content for processed storage
         processed_articles = []

backend/recommender.py

@@ -1,11 +1,11 @@
 from groq import Groq
-from typing import List, Dict, Any
-from config import GROQ_API_KEY
+from typing import List, Dict, Any, Optional
+from config import config
 import json
 
 class NewsRecommender:
-    def __init__(self):
-        self.client = Groq(api_key=GROQ_API_KEY)
+    def __init__(self, groq_client: Optional[Groq] = None):
+        self.client = groq_client or Groq(api_key=config.groq_api_key)
 
     def analyze_articles(self, articles: List[Dict[str, Any]]) -> Dict[str, Any]:
         """Analyze a set of articles using Groq to generate insights."""

backend/templates/home.html

@@ -15,7 +15,7 @@
             <div class="p-6">
                 <h2 class="text-2xl font-semibold text-gray-800 mb-4">Latest News</h2>
                 <p class="text-gray-600 mb-6">View the latest news articles fetched from our RSS feeds.</p>
-                <a href="/fetch-news" class="inline-block bg-blue-600 text-white px-6 py-3 rounded-md font-medium hover:bg-blue-700 transition-colors duration-300">
+                <a href="/fetch-news?token=default_secret_token" class="inline-block bg-blue-600 text-white px-6 py-3 rounded-md font-medium hover:bg-blue-700 transition-colors duration-300">
                     View Latest News
                 </a>
             </div>
@@ -27,7 +27,7 @@
                 <h2 class="text-2xl font-semibold text-gray-800 mb-4">News Recommendations</h2>
                 <p class="text-gray-600 mb-6">Get personalized news recommendations based on your interests.</p>
                 <div class="space-y-4">
-                    <a href="/recommend-news?query=technology" class="block bg-blue-600 text-white px-6 py-3 rounded-md font-medium hover:bg-blue-700 transition-colors duration-300 text-center">
+                    <a href="/recommend-news?query=technology?token=default_secret_token" class="block bg-blue-600 text-white px-6 py-3 rounded-md font-medium hover:bg-blue-700 transition-colors duration-300 text-center">
                         Technology News
                     </a>
                     <a href="/recommend-news?query=artificial intelligence" class="block bg-blue-600 text-white px-6 py-3 rounded-md font-medium hover:bg-blue-700 transition-colors duration-300 text-center">

backend/vector_store.py

@@ -1,16 +1,11 @@
 from pinecone import Pinecone, ServerlessSpec
-from typing import List, Dict, Any
-from config import (
-    PINECONE_API_KEY,
-    PINECONE_INDEX_NAME,
-    VECTOR_DIMENSION,
-    TOP_K_RESULTS
-)
+from typing import List, Dict, Any, Optional
+from config import config
 
 class VectorStore:
-    def __init__(self):
-        self.pinecone = Pinecone(api_key=PINECONE_API_KEY)
-        self.index_name = PINECONE_INDEX_NAME
+    def __init__(self, pinecone_client: Optional[Pinecone] = None):
+        self.pinecone = pinecone_client or Pinecone(api_key=config.pinecone_api_key)
+        self.index_name = config.pinecone_index_name
         self._ensure_index()
 
     def _ensure_index(self):
@@ -20,11 +15,11 @@ class VectorStore:
             # Create a new index with the correct dimension
             self.pinecone.create_index(
                 name=self.index_name,
-                dimension=VECTOR_DIMENSION,
+                dimension=config.vector_dimension,
                 metric="cosine",
                 spec=ServerlessSpec(cloud="aws", region="us-east-1")
             )
-            print(f"Created new index '{self.index_name}' with dimension {VECTOR_DIMENSION}")
+            print(f"Created new index '{self.index_name}' with dimension {config.vector_dimension}")
         
         self.index = self.pinecone.Index(self.index_name)
 
@@ -57,12 +52,12 @@ class VectorStore:
             print(f"Error upserting articles: {str(e)}")
             return False
 
-    def search_similar(self, query_embedding: List[float], top_k: int = TOP_K_RESULTS) -> List[Dict[str, Any]]:
+    def search_similar(self, query_embedding: List[float], top_k: int = None) -> List[Dict[str, Any]]:
         """Search for similar articles using the query embedding."""
         try:
             results = self.index.query(
                 vector=query_embedding,
-                top_k=top_k,
+                top_k=top_k or config.top_k_results,
                 include_metadata=True
             )