Create app.py

app.py

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+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):
+    try:
+        logging.info(f"Fetching articles for query: '{query}'")
+        googlenews = GoogleNews(lang="en")
+        googlenews.search(query)
+        articles = googlenews.result()
+        logging.info(f"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 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()
+