Spaces:
Running
Running
File size: 3,614 Bytes
dc3659d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 |
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(asset_name):
logging.info(f"Fetching articles for asset: {asset_name}")
googlenews = GoogleNews(lang="en")
googlenews.search(asset_name)
articles = googlenews.result()
logging.info(f"Fetched {len(articles)} articles")
return articles
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("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()
|