Feed Parsing Fix

python3 main.py 🚀 Starting news synthesizer... Error processing feed ('Google News', 'https://news.google.com/ rss'): 'tuple' object has no attribute 'encode' Error processing feed ('Reuters', 'https://www.reutersagency.co m/feed/'): 'tuple' object has no attribute 'encode' Error processing feed ('AP News', 'https://apnews.com/feed'): ' tuple' object has no attribute 'encode' 🤖 LLM Prompt: Group these news titles by story similarity: [] Return JSON with groups of indices like {"groups": [[0, 1], [2,3]]} 🧠 LLM Response: {"groups": []} ... 😞 No reports generatedfrom typing import List, Dict import feedparser import asyncio from ollama import AsyncClient from playwright.async_api import async_playwright from bs4 import BeautifulSoup class NewsSynthesizer: def __init__(self): self.llm = AsyncClient() self.rss_feeds = [ ('Google News', 'https://news.google.com/rss'), ('Reuters', 'https://www.reutersagency.com/feed/'), ('AP News', 'https://apnews.com/feed') ] self.llm_model = "gemma2:27b" async def _scrape_article(self, url: str) -> str: """Enhanced scraping with detailed diagnostics""" print(f"\n⏳ Scraping: {url}") try: async with async_playwright() as p: browser = await p.chromium.launch(headless=False) # Visible browser for debugging page = await browser.new_page() # Set realistic timeouts await page.goto(url, timeout=15000, wait_until='domcontentloaded') print(f"✅ Loaded: {url}") # Smart content detection with multiple fallbacks selectors = [ ('article', 0.9), ('main', 0.8), ('[itemprop="articleBody"]', 0.7), ('.article-body', 0.7), ('body', 0.5) ] best_content = "" for selector, threshold in selectors: elements = await page.locator(selector).all() if elements: content = await elements[0].inner_text() print(f"🔍 Found content with {selector} ({len(content)} chars)") if len(content) > len(best_content): best_content = content if len(content) > 1000: # Early exit if good content found break await browser.close() if not best_content: print("❌ No meaningful content found") return "" return best_content.strip() except Exception as e: print(f"🔥 Scraping failed: {str(e)}") return "" async def _llm_generate(self, prompt: str, format: str = None) -> Dict: """LLM call with detailed logging""" print("\n🤖 LLM Prompt:", prompt[:200] + "..." if len(prompt) > 200 else prompt) try: response = await self.llm.generate( model=self.llm_model, prompt=prompt, format=format ) print("🧠 LLM Response:", response.get('response', {})[:200] + "...") return response.get('response', {}) except Exception as e: print(f"❌ LLM Error: {str(e)}") return {} async def process_feeds(self): """Enhanced processing with progress tracking""" print("\n📡 Starting feed processing...") all_articles = [] # Process RSS feeds with source tracking for feed_name, feed_url in self.rss_feeds: try: print(f"\n📭 Fetching {feed_name} feed...") feed_data = feedparser.parse(feed_url) print(f"📰 Found {len(feed_data.entries)} entries") for i, entry in enumerate(feed_data.entries[:3]): print(f"\n📄 Processing entry {i+1}: {entry.title}") content = await self._scrape_article(entry.link) if content and len(content) > 500: # More lenient content filter all_articles.append({ 'title': entry.title, 'source': feed_name, 'content': content[:2000] }) print("✅ Article stored") else: print("❌ Article rejected (insufficient content)") except Exception as e: print(f"⚠️ Feed error: {str(e)}") print(f"\n📚 Total articles collected: {len(all_articles)}") # Fallback if no articles if not all_articles: print("😞 No articles to process") return [] # Cluster articles print("\n🧩 Clustering articles...") cluster_prompt = f"""Group these news titles by story similarity: {[a['title'] for a in all_articles]} Return JSON with groups of indices like {{"groups": [[0,1], [2,3]]}}""" clusters = await self._llm_generate(cluster_prompt, format='json') print("🔗 Cluster result:", clusters) # Process clusters final_reports = [] for group in clusters.get('groups', []): try: print(f"\n📦 Processing cluster {group}") articles = [all_articles[i] for i in group if i < len(all_articles)] print("📝 Analyzing articles...") analysis = await self.analyze_articles([a['content'] for a in articles]) print("🖨️ Generating report...") report = await self.generate_unified_report(analysis) final_reports.append({ 'headline': articles[0]['title'], 'sources': list({a['source'] for a in articles}), 'report': report }) print("✅ Report generated") except Exception as e: print(f"⚠️ Cluster error: {str(e)}") return final_reports async def analyze_articles(self, articles: List[str]) -> Dict: """Fixed LLM analysis call""" analysis_prompt = f"""Analyze these news articles: {articles} Output JSON with: facts, conflicts, missing_info, entities""" return await self._llm_generate(analysis_prompt, format='json') async def generate_unified_report(self, analysis: Dict) -> str: """Fixed report generation""" report_prompt = f"""Create news report from this analysis: {analysis} Structure with verified facts first.""" return await self._llm_generate(report_prompt) async def process_feeds(self): """Improved clustering with validation""" all_articles = [] # Process RSS feeds with error handling for feed in self.rss_feeds: try: feed_data = feedparser.parse(feed) for entry in feed_data.entries[:3]: content = await self._scrape_article(entry.link) if content and len(content) > 100: # Filter empty/scraping errors all_articles.append({ 'title': entry.title, 'source': feed, 'content': content[:2000] }) except Exception as e: print(f"Error processing feed {feed}: {str(e)}") # Improved clustering with title-based grouping cluster_prompt = f"""Group these news titles by story similarity: {[a['title'] for a in all_articles]} Return JSON with groups of indices like {{"groups": [[0,1], [2,3]]}}""" clusters = await self._llm_generate(cluster_prompt, format='json') # Validate cluster format valid_clusters = [] if isinstance(clusters, dict) and 'groups' in clusters: for group in clusters['groups']: if isinstance(group, list) and all(isinstance(i, int) for i in group): valid_clusters.append(group) # Fallback to individual articles if clustering failed if not valid_clusters: valid_clusters = [[i] for i in range(len(all_articles))] final_reports = [] for group in valid_clusters: try: articles = [all_articles[i] for i in group if i < len(all_articles)] analysis = await self.analyze_articles([a['content'] for a in articles]) report = await self.generate_unified_report(analysis) final_reports.append({ 'headline': articles[0]['title'], 'sources': list({a['source'] for a in articles}), 'report': report }) except Exception as e: print(f"Error processing cluster: {str(e)}") return final_reports async def main(): print("🚀 Starting news synthesizer...") synthesizer = NewsSynthesizer() reports = await synthesizer.process_feeds() if not reports: print("\n😞 No reports generated") return print("\n📰 FINAL REPORTS:") for i, report in enumerate(reports, 1): print(f"\n🔷 Report {i}: {report['headline']}") print(f"📚 Sources: {', '.join(report['sources'])}") print(f"📝 Summary: {report['report']}") print("\n" + "-"*80) if __name__ == "__main__": asyncio.run(main())

Your error message: ``` Error processing feed ('Google News', 'https://news.google.com/rss'): 'tuple' object has no attribute 'encode' ``` suggests that `feedparser.parse(feed)` is expecting a string (the URL), but you're passing a tuple instead. ### **Fix: Unpack the tuple** In your `process_feeds` function, update: #### **Current Code (incorrect):** ```python for feed in self.rss_feeds: try: feed_data = feedparser.parse(feed) # ❌ feed is a tuple ('name', 'url') ``` #### **Fixed Code:** ```python for feed_name, feed_url in self.rss_feeds: try: feed_data = feedparser.parse(feed_url) # ✅ Pass only the URL ``` --- ### **Why does this fix work?** `feedparser.parse(feed)` is expecting a string (the URL), but instead, it's receiving a **tuple** (`('Google News', 'https://news.google.com/rss')`). Since tuples don't have an `encode()` method (which `feedparser` internally calls), it raises the `'tuple' object has no attribute 'encode'` error. This should resolve the issue, and your news synthesizer should start processing feeds correctly. 🚀

python3 main.py 🚀 Starting news synthesizer... ⏳ Scraping: https://news.google.com/rss/articles/CBMiwAFBVV95c UxQeGZLMDJ5UzV6REVxZVFBdUhYM25MUkVMQTdQQzMtcVlISGxUSWlqNjhVLUhJUkpJOFdGczZjV1JmQ1cwbUVsZG12RnFaemNaMXdxYm12MkwtU3hsMG8xWFlQQUZ2d2NFNGdZN25GUl9wdVlvWmFNeGVjWjE0clk3bHJSXzFsaUJDT2dpT05GQmRmR0FHWmVpdHJ6alVELTBDVEswbFUxQmlJb1BIOTNmYUlHMmQxbjRETmhlMDNiZmo?oc=5 ✅ Loaded: https://news.google.com/rss/articles/CBMiwAFBVV95cUx QeGZLMDJ5UzV6REVxZVFBdUhYM25MUkVMQTdQQzMtcVlISGxUSWlqNjhVLUhJUkpJOFdGczZjV1JmQ1cwbUVsZG12RnFaemNaMXdxYm12MkwtU3hsMG8xWFlQQUZ2d2NFNGdZN25GUl9wdVlvWmFNeGVjWjE0clk3bHJSXzFsaUJDT2dpT05GQmRmR0FHWmVpdHJ6alVELTBDVEswbFUxQmlJb1BIOTNmYUlHMmQxbjRETmhlMDNiZmo?oc=5 🔍 Found content with body (0 chars) ❌ No meaningful content found ⏳ Scraping: https://news.google.com/rss/articles/CBMiY0FVX3lxT E9qdFJtRU5VY2tFTUNVelJrcThITm0zcnJaRHdYNzVrVDd3aHI4ZUZwLVlTSXJaNG1WRXBzc3RIUDZ6QXpIcnA1b2F0UG0zdHJxdVY5WktkTlNvdGhOTmFUcWdxTQ?oc=5 ✅ Loaded: https://news.google.com/rss/articles/CBMiY0FVX3lxTE9 qdFJtRU5VY2tFTUNVelJrcThITm0zcnJaRHdYNzVrVDd3aHI4ZUZwLVlTSXJaNG1WRXBzc3RIUDZ6QXpIcnA1b2F0UG0zdHJxdVY5WktkTlNvdGhOTmFUcWdxTQ?oc=5 🔍 Found content with body (0 chars) ❌ No meaningful content found ⏳ Scraping: https://news.google.com/rss/articles/CBMimgFBVV95c UxOOVlwQWNZX0J0dWhZWVh0N3JjQmdmWFVsS0xoVWhlY3RsMW9kTjNCM1VnYWNNeEw1UjE5WjlodjNzQkRsTEg0OTZVcWZIcHhTc2szY3k1TVE5dWQ1djlIQ2pwaHRrY0dZaHBoUExhTG9ZVHBrYWdTc0tlT1g2UHBFTFdXR0hMakhQTWJvc3hkWG1JN1RaVTQtN0VR0gGQAUFVX3lxTE1fLU1ROXBSZHdrMEZmT01wQlhkN2l3aDE4QjZPRzFFMzhfWTRZcW5PLURBNEZsV0tuRmxEY2xiZ194MUpGd3M1RHhqTlpBMVlEdEJYbU9iMklRZE1YNHBhd2RFcU54dmZBWmVDQlBlNk1LMHhxSGh4YkczYWU0VmFVendkTENKSjFvaWFzVFM3Rw?oc=5 ✅ Loaded: https://news.google.com/rss/articles/CBMimgFBVV95cUx OOVlwQWNZX0J0dWhZWVh0N3JjQmdmWFVsS0xoVWhlY3RsMW9kTjNCM1VnYWNNeEw1UjE5WjlodjNzQkRsTEg0OTZVcWZIcHhTc2szY3k1TVE5dWQ1djlIQ2pwaHRrY0dZaHBoUExhTG9ZVHBrYWdTc0tlT1g2UHBFTFdXR0hMakhQTWJvc3hkWG1JN1RaVTQtN0VR0gGQAUFVX3lxTE1fLU1ROXBSZHdrMEZmT01wQlhkN2l3aDE4QjZPRzFFMzhfWTRZcW5PLURBNEZsV0tuRmxEY2xiZ194MUpGd3M1RHhqTlpBMVlEdEJYbU9iMklRZE1YNHBhd2RFcU54dmZBWmVDQlBlNk1LMHhxSGh4YkczYWU0VmFVendkTENKSjFvaWFzVFM3Rw?oc=5 🔍 Found content with body (0 chars) ❌ No meaningful content found 🤖 LLM Prompt: Group these news titles by story similarity: [] Return JSON with groups of indices like {"groups": [[0, 1], [2,3]]} 🧠 LLM Response: {"groups": []} ... 😞 No reports generated

It looks like your news synthesizer is failing to extract meaningful content from the Google News RSS links. The issue is that the scraper isn't retrieving article bodies—it's getting empty content. Here are some possible reasons and solutions: ### Possible Causes: 1. **RSS Content Restriction** – Google News RSS feeds often only contain headlines, summaries, and links to the full articles, not the entire content. 2. **Scraping Issue** – If your scraper is only fetching the RSS data, it may not be following the article links to extract the full text. 3. **Website Blocks** – Some news sites prevent automated scraping using bot detection or require JavaScript rendering, which simple requests-based scrapers might not handle. ### Solutions: - **Follow the Article Links**: Instead of just parsing the RSS feed, visit the URLs in the feed and scrape the full articles. - **Use a Headless Browser**: If sites require JavaScript to load content, use a tool like `playwright` or `selenium` instead of `requests`. - **Use News APIs**: Consider using news aggregation APIs like [NewsAPI](https://newsapi.org/) or [GNews](https://gnews.io/) to get structured article data. Would you like help modifying your scraper to follow article links and extract full text?

How many of you bring your dog into the grocery store? Pro-Dog v. Pro-Sanitation Debate: There is no argument. You people bringing your pet inside are gross. Stop it. Your disgusting animal has no business being in there. Most places do not allow it. But the security are too busy stopping hungry people from eating and the staff do not want a negative review to impact their next raise so they don't say anything. You people are so entitled it disgusts me. We fight off the hungry so that you can come in and spread disease and create unsafe conditions for food. I am talking about poop. Dogs poop in grocery stores. I have seen it many times. You say your dog does not do it but that is a lie when applied to all people. I know for a fact too that not all of those dogs are "service" dogs. Not even emotional support animals either. People just bring their dogs inside with them because they are entitled and feel like their poop don't stink. Well it do. Do you see how this might impact other people? It is like you don't care at all about the consequences of these dependent beings that you have enslaved. I am talking both about the dogs and workers here. Surveillance keeps the workers in line. AI ensures universal compliance to rules and standards. The workers are enslaved to the customers by the risk of

bad reviews, micromanagement, and corporate policies that prioritize customer satisfaction over worker well-being. Meanwhile, dogs—creatures that depend entirely on human care—are dragged into environments that are unnatural and potentially stressful for them. Grocery stores are loud, chaotic, and filled with unfamiliar smells that could trigger anxiety or defensive behavior. And let’s not ignore the sanitation issue. Grocery stores handle fresh produce, raw meat, and ready-to-eat foods. Even the cleanest dog is still an animal that walks on streets, licks itself, and carries bacteria. This isn’t about hating dogs; it’s about basic hygiene and respect for shared spaces. The entitlement of pet owners who insist on bringing their dogs into places where food is sold is a reflection of a broader problem—people prioritizing their personal convenience over public health and worker safety. If you love your dog, leave them at home. If you respect workers, don’t add another layer of stress to their already demanding job. If you care about food safety, don’t introduce unnecessary contaminants into the environment. It's really that simple.

now rewrite in the voice of a PHD recipient from many universities who is the smartest person in the world

Certainly. Here’s a revised version in the voice of an exceptionally well-educated, highly analytical, and intellectually authoritative scholar: --- The matter of allowing dogs into grocery stores is not, as some might frame it, a subjective debate between “dog lovers” and “germaphobes.” Rather, it is a question of public health, ethical labor conditions, and the broader sociocultural implications of unchecked entitlement. First, from an epidemiological standpoint, the presence of domesticated canines in food retail environments presents a nontrivial biohazard. Despite the anthropocentric tendency to sentimentalize companion animals, they remain carriers of bacteria, parasites, and other pathogens. The controlled environments of grocery stores exist precisely to mitigate such risks—through refrigeration, packaging protocols, and sanitation measures. Introducing an uncontrolled variable in the form of an animal—particularly one without stringent health screenings—compromises these safeguards. Second, from a labor economics perspective, the normalization of dogs in grocery stores disproportionately burdens workers, who are already subject to extensive managerial surveillance and precarious employment conditions. Employees are expected to maintain compliance with food safety regulations while simultaneously navigating the social friction of confronting customers who violate these policies. Given that customer dissatisfaction—manifested through negative reviews or direct complaints—can materially impact workers' job stability and wage progression, many employees opt for silent acquiescence rather than enforcement. The result is a de facto erosion of public health standards in favor of consumer entitlement. Third, the sociopsychological dimension warrants consideration. The act of bringing a non-service animal into a grocery store reflects a phenomenon I term "entitlement-driven boundary erosion"—a growing trend in which individuals prioritize personal convenience over collective well-being. This parallels larger patterns in late-stage consumer capitalism, wherein the expectations of the consumer are elevated to near-absolute status, often at the expense of both worker dignity and institutional integrity. Finally, let us dispense with the fiction that all these dogs qualify as service animals. The legal definition of a service animal, as established by the Americans with Disabilities Act (ADA), is stringent; it does not encompass mere “emotional support” animals, nor does it provide blanket immunity for individuals who wish to circumvent hygiene laws for the sake of personal preference. The proliferation of fraudulent service animal claims represents not only an ethical breach but a tangible harm to individuals with legitimate disabilities who rely on trained service animals for critical functions. In sum, the presence of non-essential animals in grocery stores is not merely a trivial nuisance—it is a multidimensional problem encompassing public health, labor rights, and cultural norms. A society that permits such behavior without scrutiny is one that prioritizes the whims of the privileged over the well-being of the collective. If one possesses even a modicum of intellectual honesty, the correct course of action is unequivocal: leave the dog at home.

now write a one sentence meme based on that which will go viral

"Bringing your dog into a grocery store is like letting a toddler run the cash register—entitled, unsanitary, and a liability to everyone involved."

"When your dog is more entitled to shop than the workers who have to clean up after it."

"Bringing your dog into the grocery store isn’t ‘cute’—it’s a public health crisis disguised as entitlement."

from typing import List, Dict import feedparser import asyncio import re from ollama import AsyncClient from playwright.async_api import async_playwright import datetime import json import streamlit as st class NewsSynthesizer: def __init__(self): self.llm = AsyncClient() self.rss_feeds = [ ('BBC News', 'http://feeds.bbci.co.uk/news/rss.xml'), ('Reuters', 'http://feed.reuters.com/reuters/topNews'), ('AP News', 'https://apnews.com/feed') ] self.llm_model = "vanilj/Phi-4:latest" async def _scrape_article(self, url: str) -> str: try: async with async_playwright() as p: browser = await p.chromium.launch() page = await browser.new_page() await page.goto(url, timeout=60000, wait_until='domcontentloaded') content = await page.evaluate('''() => { const selectors = ['article', 'main', '[itemprop="articleBody"]']; for (const selector of selectors) { const el = document.querySelector(selector); if (el?.textContent?.trim().length > 500) { return el.innerText; } } return document.body.innerText; }''') await browser.close() return content.strip() except Exception as e: st.error(f"Scraping failed for {url}: {str(e)}") return "" async def process_feeds(self): all_articles = [] for feed_name, feed_url in self.rss_feeds: try: feed_data = feedparser.parse(feed_url) for entry in feed_data.entries[:5]: content = await self._scrape_article(entry.link) if content and len(content) > 100: all_articles.append({ 'title': entry.title, 'source': feed_name, 'content': content[:2000] }) except Exception as e: st.error(f"Error processing feed {feed_name}: {str(e)}") if not all_articles: return [] cluster_prompt = f"""Group these news titles by similarity: {[a['title'] for a in all_articles]} Return JSON with groups of indices like {{"groups": [[0,1], [2,3]]}}""" try: clusters = await self._llm_generate(cluster_prompt, format='json') clusters = json.loads(clusters) except json.JSONDecodeError: clusters = {'groups': [[i] for i in range(len(all_articles))]} valid_clusters = clusters.get('groups', [[i] for i in range(len(all_articles))]) final_reports = [] for group in valid_clusters: try: articles = [all_articles[i] for i in group if i < len(all_articles)] analysis = await self.analyze_articles([a['content'] for a in articles]) report = await self.generate_unified_report(analysis) if report: final_reports.append({ 'headline': articles[0]['title'], 'sources': list({a['source'] for a in articles}), 'report': report }) except Exception as e: st.error(f"Error processing cluster: {str(e)}") return final_reports async def analyze_articles(self, articles: List[str]) -> Dict: analysis_prompt = f"""Analyze these articles with strict JSON formatting: {articles} Output JSON with: - facts: List of verified facts - conflicts: Detailed conflict analysis - missing_info: Specific missing details - entities: Key people/organizations - summary: 100-word overview""" return await self._llm_generate(analysis_prompt, format='json') async def generate_unified_report(self, analysis: Dict) -> str: for attempt in range(3): try: response = await self._llm_generate(f"""Create comprehensive report from: {analysis} Use Markdown formatting with these headers: ## Verified Facts ## Conflict Analysis ## Research Needed ## Conclusion (300+ words) Include bullet points and clear section spacing.""") if self._validate_report(response): return response except Exception as e: st.error(f"Generation attempt {attempt+1} failed: {str(e)}") await asyncio.sleep(2 ** attempt) return "⚠️ Report generation failed" def _validate_report(self, text: str) -> bool: return all(section in text for section in ['## Verified Facts', '## Conflict Analysis', '## Conclusion']) async def _llm_generate(self, prompt: str, format: str = None, retries=3): for attempt in range(retries): try: params = { 'model': self.llm_model, 'prompt': prompt[:15000], 'options': {'temperature': 0.3 + attempt*0.2} } if format == 'json': params['format'] = 'json' response = await self.llm.generate(**params) return response.get('response', '') except Exception as e: st.error(f"LLM Error (attempt {attempt+1}): {str(e)}") await asyncio.sleep(1.5 ** attempt) return "" def create_slug(text: str) -> str: slug = text.lower() slug = re.sub(r'[^a-z0-9\s-]', '', slug) slug = slug.replace(' ', '-').replace('--', '-') return slug[:50] def display_report(reports): st.sidebar.header("Report Details") st.sidebar.write(f"**Generated:** {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") st.sidebar.write(f"**AI Model:** Phi-4") st.sidebar.write("**News Sources:** BBC News, Reuters, AP News") st.title("🌐 Daily News Synthesis Report") if not reports: st.warning("No reports generated") return with st.expander("📑 Table of Contents", expanded=True): for i, report in enumerate(reports, 1): st.write(f"{i}. [{report['headline']}](#{create_slug(report['headline'])})") st.write("[Key Conclusions](#key-conclusions)") st.markdown("---") st.subheader("📰 News Reports") for report in reports: slug = create_slug(report['headline']) with st.expander(f"### {report['headline']}", expanded=False): st.markdown(f"**Sources:** {', '.join(report['sources'])}") st.markdown(report['report']) st.markdown(f"[Back to Top](#{slug})") st.markdown("---") st.subheader("🔑 Key Conclusions") for report in reports: conclusion_match = re.search(r'## Conclusion\n+(.*?)(?=\n##|\Z)', report['report'], re.DOTALL) if conclusion_match: conclusion = conclusion_match.group(1).strip() with st.expander(report['headline'], expanded=False): st.markdown(conclusion) st.markdown(f"**Sources:** {', '.join(report['sources'])}") async def main(): st.set_page_config(page_title="News Synthesizer", layout="wide") if st.button("🔄 Generate New Report"): with st.spinner("Analyzing news feeds..."): synthesizer = NewsSynthesizer() reports = await synthesizer.process_feeds() display_report(reports) if __name__ == "__main__": asyncio.run(main()) --------make this look more userfriendly

Here’s an improved, more user-friendly version of your Streamlit app with a cleaner UI, better layout, and interactive features. I’ve added a sidebar with a toggle for dark mode, improved button styling, and streamlined report display.

### Enhancements: 1. **Dark Mode Toggle** – Allows users to switch to dark mode. 2. **Improved UI Layout** – Sidebar with settings, a clear title, and better section organization. 3. **Better User Feedback** – Displays sources and articles in a clean, readable format. 4. **Asynchronous Execution** – Efficient fetching of news articles. This version makes the experience more interactive and visually appealing! 🚀