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import logging |
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import gradio as gr |
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import pandas as pd |
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import torch |
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from GoogleNews import GoogleNews |
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from transformers import pipeline |
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logging.basicConfig( |
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level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s" |
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) |
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SENTIMENT_ANALYSIS_MODEL = ( |
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"mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis" |
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) |
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DEVICE = "cuda" if torch.cuda.is_available() else "cpu" |
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logging.info(f"Using device: {DEVICE}") |
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logging.info("Initializing sentiment analysis model...") |
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sentiment_analyzer = pipeline( |
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"sentiment-analysis", model=SENTIMENT_ANALYSIS_MODEL, device=DEVICE |
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) |
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logging.info("Model initialized successfully") |
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def fetch_articles(query): |
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try: |
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logging.info(f"Fetching articles for query: '{query}'") |
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googlenews = GoogleNews(lang="en") |
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googlenews.search(query) |
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articles = googlenews.result() |
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logging.info(f"Fetched {len(articles)} articles") |
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return articles |
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except Exception as e: |
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logging.error( |
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f"Error while searching articles for query: '{query}'. Error: {e}" |
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) |
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raise gr.Error( |
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f"Unable to search articles for query: '{query}'. Try again later...", |
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duration=5, |
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) |
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def analyze_article_sentiment(article): |
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logging.info(f"Analyzing sentiment for article: {article['title']}") |
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sentiment = sentiment_analyzer(article["desc"])[0] |
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article["sentiment"] = sentiment |
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return article |
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def analyze_asset_sentiment(asset_name): |
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logging.info(f"Starting sentiment analysis for asset: {asset_name}") |
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logging.info("Fetching articles") |
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articles = fetch_articles(asset_name) |
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logging.info("Analyzing sentiment of each article") |
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analyzed_articles = [analyze_article_sentiment(article) for article in articles] |
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logging.info("Sentiment analysis completed") |
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return convert_to_dataframe(analyzed_articles) |
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def convert_to_dataframe(analyzed_articles): |
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df = pd.DataFrame(analyzed_articles) |
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df["Title"] = df.apply( |
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lambda row: f'<a href="{row["link"]}" target="_blank">{row["title"]}</a>', |
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axis=1, |
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) |
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df["Description"] = df["desc"] |
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df["Date"] = df["date"] |
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def sentiment_badge(sentiment): |
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colors = { |
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"negative": "red", |
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"neutral": "gray", |
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"positive": "green", |
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} |
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color = colors.get(sentiment, "grey") |
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return f'<span style="background-color: {color}; color: white; padding: 2px 6px; border-radius: 4px;">{sentiment}</span>' |
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df["Sentiment"] = df["sentiment"].apply(lambda x: sentiment_badge(x["label"])) |
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return df[["Sentiment", "Title", "Description", "Date"]] |
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with gr.Blocks() as iface: |
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gr.Markdown("# Trading Asset Sentiment Analysis") |
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gr.Markdown( |
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"Enter the name of a trading asset, and I'll fetch recent articles and analyze their sentiment!" |
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) |
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with gr.Row(): |
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input_asset = gr.Textbox( |
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label="Asset Name", |
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lines=1, |
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placeholder="Enter the name of the trading asset...", |
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) |
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with gr.Row(): |
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analyze_button = gr.Button("Analyze Sentiment", size="sm") |
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gr.Examples( |
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examples=[ |
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"Bitcoin", |
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"Tesla", |
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"Apple", |
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"Amazon", |
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], |
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inputs=input_asset, |
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) |
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with gr.Row(): |
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with gr.Column(): |
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with gr.Blocks(): |
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gr.Markdown("## Articles and Sentiment Analysis") |
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articles_output = gr.Dataframe( |
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headers=["Sentiment", "Title", "Description", "Date"], |
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datatype=["markdown", "html", "markdown", "markdown"], |
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wrap=False, |
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) |
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analyze_button.click( |
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analyze_asset_sentiment, |
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inputs=[input_asset], |
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outputs=[articles_output], |
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) |
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logging.info("Launching Gradio interface") |
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iface.queue().launch() |
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