Delete app-backup.py

app-backup.py

@@ -1,141 +0,0 @@
-import logging
-import gradio as gr
-import pandas as pd
-import torch
-from GoogleNews import GoogleNews
-from transformers import pipeline
-
-# Set up logging
-logging.basicConfig(
-    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
-)
-
-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, max_articles=30):
-    try:
-        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()
-        
-        # 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(
-            f"Error while searching articles for query: '{query}'. Error: {e}"
-        )
-        raise gr.Error(
-            f"Unable to search articles for query: '{query}'. Try again later...",
-            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 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(
-        lambda row: f'<a href="{row["link"]}" target="_blank">{row["title"]}</a>',
-        axis=1,
-    )
-    df["Description"] = df["desc"]
-    df["Date"] = df["date"]
-    
-    def sentiment_badge(sentiment):
-        colors = {
-            "negative": "red",
-            "neutral": "gray",
-            "positive": "green",
-        }
-        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",
-            "Tesla",
-            "Apple",
-            "Amazon",
-        ],
-        inputs=input_asset,
-    )
-    
-    with gr.Row():
-        with gr.Column():
-            with gr.Blocks():
-                gr.Markdown("## Articles and Sentiment Analysis")
-                articles_output = gr.Dataframe(
-                    headers=["Sentiment", "Title", "Description", "Date"],
-                    datatype=["markdown", "html", "markdown", "markdown"],
-                    wrap=False,
-                )
-    
-    analyze_button.click(
-        analyze_asset_sentiment,
-        inputs=[input_asset],
-        outputs=[articles_output],
-    )
-
-logging.info("Launching Gradio interface")
-iface.queue().launch()