Add backend functionality for news fetching, processing, and recommendations

- Implemented NewsFetcher class to fetch articles from RSS feeds and clean HTML content.
- Added EmbeddingGenerator for generating embeddings using Cohere API.
- Created VectorStore for storing and retrieving articles using Pinecone.
- Developed NewsRecommender for analyzing articles and generating insights with Groq.
- Set up FastAPI application with endpoints for fetching news and providing recommendations.
- Configured logging for better traceability and debugging.
- Updated .gitignore to include environment variables and data directories.
- Added requirements.txt for project dependencies.

backend/recommender.py

@@ -0,0 +1,75 @@
+from groq import Groq
+from typing import List, Dict, Any
+from config import GROQ_API_KEY
+
+class NewsRecommender:
+    def __init__(self):
+        self.client = Groq(api_key=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."""
+        try:
+            # Prepare the prompt
+            articles_text = "\n\n".join([
+                f"Title: {article['title']}\nContent: {article['content']}"
+                for article in articles
+            ])
+            
+            prompt = f"""Analyze these news articles and provide insights:
+
+{articles_text}
+
+Please provide:
+1. Main themes and topics
+2. Key insights and trends
+3. Potential implications
+4. Related areas of interest
+
+Format the response as a JSON with these keys: themes, insights, implications, related_areas"""
+
+            # Get completion from Groq
+            completion = self.client.chat.completions.create(
+                messages=[
+                    {"role": "system", "content": "You are a news analyst providing insights about technology and AI news."},
+                    {"role": "user", "content": prompt}
+                ],
+                model="mixtral-8x7b-32768",
+                temperature=0.7,
+                max_tokens=1000
+            )
+
+            # Parse and return the analysis
+            return completion.choices[0].message.content
+        except Exception as e:
+            print(f"Error analyzing articles: {str(e)}")
+            return {
+                "themes": [],
+                "insights": [],
+                "implications": [],
+                "related_areas": []
+            }
+
+    def generate_summary(self, article: Dict[str, Any]) -> str:
+        """Generate a summary of a single article using Groq."""
+        try:
+            prompt = f"""Summarize this news article:
+
+Title: {article['title']}
+Content: {article['content']}
+
+Please provide a concise summary focusing on the key points and implications."""
+
+            completion = self.client.chat.completions.create(
+                messages=[
+                    {"role": "system", "content": "You are a news summarizer providing concise summaries of technology and AI news."},
+                    {"role": "user", "content": prompt}
+                ],
+                model="mixtral-8x7b-32768",
+                temperature=0.5,
+                max_tokens=500
+            )
+
+            return completion.choices[0].message.content
+        except Exception as e:
+            print(f"Error generating summary: {str(e)}")
+            return "Unable to generate summary."