Update app.py

app.py

@@ -1,5 +1,4 @@
 import logging
-
 import gradio as gr
 import pandas as pd
 import torch
@@ -14,24 +13,42 @@ logging.basicConfig(
 SENTIMENT_ANALYSIS_MODEL = (
     "mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis"
 )
-
 DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 logging.info(f"Using device: {DEVICE}")
-
 logging.info("Initializing sentiment analysis model...")
 sentiment_analyzer = pipeline(
     "sentiment-analysis", model=SENTIMENT_ANALYSIS_MODEL, device=DEVICE
 )
 logging.info("Model initialized successfully")
 
-
-def fetch_articles(query):
+def fetch_articles(query, max_articles=30):
     try:
-        logging.info(f"Fetching articles for query: '{query}'")
+        logging.info(f"Fetching up to {max_articles} articles for query: '{query}'")
         googlenews = GoogleNews(lang="en")
         googlenews.search(query)
+        
+        # 첫 페이지 결과 가져오기
         articles = googlenews.result()
-        logging.info(f"Fetched {len(articles)} articles")
+        
+        # 목표 기사 수에 도달할 때까지 추가 페이지 가져오기
+        page = 2
+        while len(articles) < max_articles and page <= 10:  # 최대 10페이지까지만 시도
+            logging.info(f"Fetched {len(articles)} articles so far. Getting page {page}...")
+            googlenews.get_page(page)
+            page_results = googlenews.result()
+            
+            # 새 결과가 없으면 중단
+            if not page_results:
+                logging.info(f"No more results found after page {page-1}")
+                break
+                
+            articles.extend(page_results)
+            page += 1
+            
+        # 최대 기사 수로 제한
+        articles = articles[:max_articles]
+        
+        logging.info(f"Successfully fetched {len(articles)} articles")
         return articles
     except Exception as e:
         logging.error(
@@ -42,28 +59,21 @@ def fetch_articles(query):
             duration=5,
         )
 
-
 def analyze_article_sentiment(article):
     logging.info(f"Analyzing sentiment for article: {article['title']}")
     sentiment = sentiment_analyzer(article["desc"])[0]
     article["sentiment"] = sentiment
     return article
 
-
 def analyze_asset_sentiment(asset_name):
     logging.info(f"Starting sentiment analysis for asset: {asset_name}")
-
-    logging.info("Fetching articles")
-    articles = fetch_articles(asset_name)
-
+    logging.info("Fetching up to 30 articles")
+    articles = fetch_articles(asset_name, max_articles=30)
     logging.info("Analyzing sentiment of each article")
     analyzed_articles = [analyze_article_sentiment(article) for article in articles]
-
     logging.info("Sentiment analysis completed")
-
     return convert_to_dataframe(analyzed_articles)
 
-
 def convert_to_dataframe(analyzed_articles):
     df = pd.DataFrame(analyzed_articles)
     df["Title"] = df.apply(
@@ -72,7 +82,7 @@ def convert_to_dataframe(analyzed_articles):
     )
     df["Description"] = df["desc"]
     df["Date"] = df["date"]
-
+    
     def sentiment_badge(sentiment):
         colors = {
             "negative": "red",
@@ -81,27 +91,26 @@ def convert_to_dataframe(analyzed_articles):
         }
         color = colors.get(sentiment, "grey")
         return f'<span style="background-color: {color}; color: white; padding: 2px 6px; border-radius: 4px;">{sentiment}</span>'
-
+    
     df["Sentiment"] = df["sentiment"].apply(lambda x: sentiment_badge(x["label"]))
     return df[["Sentiment", "Title", "Description", "Date"]]
 
-
 with gr.Blocks() as iface:
     gr.Markdown("# Trading Asset Sentiment Analysis")
     gr.Markdown(
         "Enter the name of a trading asset, and I'll fetch recent articles and analyze their sentiment!"
     )
-
+    
     with gr.Row():
         input_asset = gr.Textbox(
             label="Asset Name",
             lines=1,
             placeholder="Enter the name of the trading asset...",
         )
-
+    
     with gr.Row():
         analyze_button = gr.Button("Analyze Sentiment", size="sm")
-
+    
     gr.Examples(
         examples=[
             "Bitcoin",
@@ -111,7 +120,7 @@ with gr.Blocks() as iface:
         ],
         inputs=input_asset,
     )
-
+    
     with gr.Row():
         with gr.Column():
             with gr.Blocks():
@@ -121,7 +130,7 @@ with gr.Blocks() as iface:
                     datatype=["markdown", "html", "markdown", "markdown"],
                     wrap=False,
                 )
-
+    
     analyze_button.click(
         analyze_asset_sentiment,
         inputs=[input_asset],
@@ -129,5 +138,4 @@ with gr.Blocks() as iface:
     )
 
 logging.info("Launching Gradio interface")
-iface.queue().launch()
-
+iface.queue().launch()