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app.py

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+# streamlit app
+
+import streamlit as st
+import pandas as pd
+from transformers import BertTokenizer, BertForSequenceClassification
+from transformers import pipeline
+from scraper import get_latest_news
+
+# Load FinBERT model and tokenizer
+finbert = BertForSequenceClassification.from_pretrained("yiyanghkust/finbert-tone", num_labels=3)
+tokenizer = BertTokenizer.from_pretrained("yiyanghkust/finbert-tone")
+
+# Create sentiment analysis pipeline
+nlp = pipeline("sentiment-analysis", model=finbert, tokenizer=tokenizer)
+
+# Function to perform sentiment analysis
+def analyze_sentiment(text):
+    results = nlp(text)
+    sentiment_label = results[0]["label"]
+    return sentiment_label
+
+# Function to get sentiment labels for a list of headlines
+def get_sentiment_labels(headlines_list):
+    sentiment_labels = []
+    for headline in headlines_list:
+        label = analyze_sentiment(headline)
+        sentiment_labels.append(label)
+    return sentiment_labels
+
+# Function to print a Streamlit table with news headlines and sentiment labels
+def display_news_sentiment_table(headlines_list, sentiment_labels):
+    df = pd.DataFrame({
+        "Headlines": headlines_list,
+        "Sentiment": sentiment_labels
+    })
+
+    # Function to apply background colors based on sentiment labels
+    def style_func(val):
+        color_dict = {
+            "negative": 'red',
+            "positive": 'green',
+            "neutral": 'gray'
+        }
+        return f"background-color: {color_dict[val.lower()]}"
+
+    # Display the table
+    st.dataframe(df.set_index("Headlines").style.applymap(style_func, subset=["Sentiment"]))
+
+# Streamlit app
+st.title("Financial News Sentiment Analysis")
+
+# Get the latest news headlines and sentiment labels using the scraper
+latest_news_headlines = get_latest_news()
+sentiment_labels = get_sentiment_labels(latest_news_headlines)
+
+# Display the table in the Streamlit app
+display_news_sentiment_table(latest_news_headlines, sentiment_labels)
+
+# Refresh button
+if st.button("Refresh"):
+    st.experimental_rerun()
+
+# App Description
+st.markdown("---")
+st.subheader("Description")
+st.info("This app uses the [FinBERT](https://huggingface.co/yiyanghkust/finbert-tone) model from Hugging Face to perform sentiment analysis on financial news headlines. The headlines are scraped in real-time from [Finviz](https://finviz.com/). The news headlines displayed on the web app are the latest, and you can click the 'Refresh' button to update the headlines and sentiment analysis.")
+st.markdown("---")

scraper.py

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+# scraper.py
+
+# Scrape news headlines using BeautifulSoup
+from urllib.request import urlopen, Request
+from bs4 import BeautifulSoup
+
+def get_latest_news():
+    url = "https://finviz.com/news.ashx" # Link to news webpage
+    req = Request(url=url, headers={"user-agent": "my-app"})
+    response = urlopen(req)
+    html_content = response.read()
+    soup = BeautifulSoup(html_content, "html.parser")
+    headline_elements = soup.find_all("td", class_="news_link-cell")
+    latest_headlines = [headline_element.find("a", class_="tab-link").text.strip() for headline_element in headline_elements[:15]]
+    return latest_headlines