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Update app.py
Browse files
app.py
CHANGED
@@ -39,16 +39,13 @@ def index():
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# Check if the database needs to be loaded (first time or empty)
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db_exists = os.path.exists("chroma_db") and vector_db.get().get('documents')
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if not db_exists:
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# First load: DB doesn't exist or is empty
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loading_complete = False
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logger.info("Downloading Chroma DB from Hugging Face Hub...")
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download_from_hf_hub()
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threading.Thread(target=load_feeds_in_background, daemon=True).start()
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elif not loading_complete:
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#
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pass # Let it continue, spinner will show
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else:
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# DB exists and loading is complete, no spinner needed
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loading_complete = True
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try:
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@@ -58,7 +55,7 @@ def index():
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logger.info("No articles in DB yet")
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return render_template("index.html", categorized_articles={}, has_articles=False, loading=not loading_complete)
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# Process and categorize articles
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enriched_articles = []
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seen_keys = set()
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for doc, meta in zip(all_docs['documents'], all_docs['metadatas']):
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@@ -82,27 +79,27 @@ def index():
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"published": published,
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"image": meta.get("image", "svg"),
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})
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enriched_articles.sort(key=lambda x: x["published"], reverse=True)
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categorized_articles = {}
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for article in enriched_articles:
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cat = article["category"]
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if cat not in categorized_articles:
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categorized_articles[cat] = []
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for cat in categorized_articles:
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if key not in seen_cat_keys:
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seen_cat_keys.add(key)
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unique_articles.append(article)
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categorized_articles[cat] = unique_articles[:10]
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logger.info(f"Displaying articles: {sum(len(articles) for articles in categorized_articles.values())} total")
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return render_template("index.html",
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# Check if the database needs to be loaded (first time or empty)
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db_exists = os.path.exists("chroma_db") and vector_db.get().get('documents')
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if not db_exists:
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loading_complete = False
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logger.info("Downloading Chroma DB from Hugging Face Hub...")
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download_from_hf_hub()
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threading.Thread(target=load_feeds_in_background, daemon=True).start()
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elif not loading_complete:
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pass # Let background loading continue
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else:
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loading_complete = True
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try:
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logger.info("No articles in DB yet")
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return render_template("index.html", categorized_articles={}, has_articles=False, loading=not loading_complete)
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# Process and categorize articles with strict deduplication
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enriched_articles = []
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seen_keys = set()
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for doc, meta in zip(all_docs['documents'], all_docs['metadatas']):
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"published": published,
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"image": meta.get("image", "svg"),
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})
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else:
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logger.debug(f"Duplicate found in DB: {key}")
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# Sort by published date (stable sort)
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enriched_articles.sort(key=lambda x: x["published"], reverse=True)
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# Group by category and limit to 10 most recent per category
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categorized_articles = {}
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for article in enriched_articles:
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cat = article["category"]
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if cat not in categorized_articles:
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categorized_articles[cat] = []
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# No need for extra deduplication here; trust seen_keys
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categorized_articles[cat].append(article)
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# Limit to 10 most recent per category
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for cat in categorized_articles:
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categorized_articles[cat] = sorted(categorized_articles[cat], key=lambda x: x["published"], reverse=True)[:10]
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# Log the first two items to check for duplicates
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if len(categorized_articles[cat]) >= 2:
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logger.debug(f"Category {cat} top 2: {categorized_articles[cat][0]['title']} | {categorized_articles[cat][1]['title']}")
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logger.info(f"Displaying articles: {sum(len(articles) for articles in categorized_articles.values())} total")
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return render_template("index.html",
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