app-backup.py

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()