boladeE
bc485b44b8
- Enhanced README.md with a clearer project overview, features, technologies used, and installation instructions. - Updated vector dimension in config.py from 4096 to 1024 for Cohere embeddings. - Modified main.py to serve HTML responses for the home page, news fetching, and recommendations. - Improved error handling and ensured articles have links in the responses. - Cleaned up news_fetcher.py by removing unnecessary print statements. - Updated recommender.py to refine insights generation and summary extraction. - Added Jinja2 for templating and improved the project structure for better organization. - Included API documentation for better understanding of endpoints and usage.
98 lines
3.5 KiB
Python
98 lines
3.5 KiB
Python
from groq import Groq
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from typing import List, Dict, Any
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from config import GROQ_API_KEY
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import json
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class NewsRecommender:
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def __init__(self):
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self.client = Groq(api_key=GROQ_API_KEY)
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def analyze_articles(self, articles: List[Dict[str, Any]]) -> Dict[str, Any]:
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"""Analyze a set of articles using Groq to generate insights."""
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try:
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# Prepare the prompt
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articles_text = "\n\n".join([
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f"Title: {article['title']}"
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for article in articles
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])
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prompt = f"""Analyze these news articles and provide insights:
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{articles_text}
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Please provide:
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1. Main themes and topics
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2. Key insights and trends
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3. Potential implications
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4. Related areas of interest
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Format the response as a JSON with these keys: themes, insights, implications, related_areas"""
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# Get completion from Groq
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completion = self.client.chat.completions.create(
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messages=[
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{"role": "system", "content": "You are a news analyst providing insights about technology and AI news."},
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{"role": "user", "content": prompt}
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],
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model="llama3-70b-8192",
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temperature=0.7,
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max_tokens=500
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)
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# Parse and return the analysis
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response_text = completion.choices[0].message.content
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# Try to extract JSON from the response if it's wrapped in markdown code blocks
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if "```json" in response_text:
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json_str = response_text.split("```json")[1].split("```")[0].strip()
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try:
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return json.loads(json_str)
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except json.JSONDecodeError:
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pass
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elif "```" in response_text:
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json_str = response_text.split("```")[1].split("```")[0].strip()
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try:
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return json.loads(json_str)
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except json.JSONDecodeError:
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pass
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# If we couldn't extract JSON, try to parse the entire response
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try:
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return json.loads(response_text)
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except json.JSONDecodeError:
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# If all parsing attempts fail, return the raw text
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return response_text
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except Exception as e:
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print(f"Error analyzing articles: {str(e)}")
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return {
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"themes": [],
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"insights": [],
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"implications": [],
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"related_areas": []
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}
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def generate_summary(self, article: Dict[str, Any]) -> str:
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"""Generate a summary of a single article using Groq."""
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try:
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prompt = f"""Summarize this news article:
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Title: {article['title']}
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Content: {article['content']}
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Please provide a concise summary focusing on the key points and implications."""
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completion = self.client.chat.completions.create(
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messages=[
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{"role": "system", "content": "You are a news summarizer providing concise summaries of technology and AI news."},
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{"role": "user", "content": prompt}
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],
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model="llama3-70b-8192",
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temperature=0.5,
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max_tokens=250
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)
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return completion.choices[0].message.content
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except Exception as e:
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print(f"Error generating summary: {str(e)}")
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return "Unable to generate summary."
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