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import gradio as gr
import requests
from bs4 import BeautifulSoup
import pandas as pd
from transformers import pipeline
import plotly.graph_objects as go
from datetime import datetime, timedelta
# Sentiment Analysis Model
sentiment_model = pipeline(model="finiteautomata/bertweet-base-sentiment-analysis")
# Function to encode special characters in the search query
def encode_special_characters(text):
encoded_text = ''
special_characters = {'&': '%26', '=': '%3D', '+': '%2B', ' ': '%20'}
for char in text.lower():
encoded_text += special_characters.get(char, char)
return encoded_text
# Function to fetch news articles
def fetch_news(query, num_articles=10):
encoded_query = encode_special_characters(query)
url = f"https://news.google.com/search?q={encoded_query}&hl=en-US&gl=in&ceid=US%3Aen&num={num_articles}"
try:
response = requests.get(url)
response.raise_for_status()
except requests.RequestException as e:
print(f"Error fetching news: {e}")
return pd.DataFrame()
soup = BeautifulSoup(response.text, 'html.parser')
articles = soup.find_all('article')
news_data = []
for article in articles[:num_articles]:
link = article.find('a')['href'].replace("./articles/", "https://news.google.com/articles/")
text_parts = article.get_text(separator='\n').split('\n')
news_data.append({
'Title': text_parts[2] if len(text_parts) > 2 else 'Missing',
'Source': text_parts[0] if len(text_parts) > 0 else 'Missing',
'Time': text_parts[3] if len(text_parts) > 3 else 'Missing',
'Author': text_parts[4].split('By ')[-1] if len(text_parts) > 4 else 'Missing',
'Link': link
})
return pd.DataFrame(news_data)
# Function to perform sentiment analysis
def analyze_sentiment(text):
result = sentiment_model(text)[0]
return result['label'], result['score']
# Main function to process news and perform analysis
def news_and_analysis(query):
# Fetch news
news_df = fetch_news(query)
if news_df.empty:
return "No news articles found.", None
# Perform sentiment analysis
news_df['Sentiment'], news_df['Sentiment_Score'] = zip(*news_df['Title'].apply(analyze_sentiment))
# Create sentiment plot
sentiment_fig = go.Figure(data=[go.Bar(
x=news_df['Time'],
y=news_df['Sentiment_Score'],
marker_color=news_df['Sentiment'].map({'positive': 'green', 'neutral': 'gray', 'negative': 'red'})
)])
sentiment_fig.update_layout(title='News Sentiment Over Time', xaxis_title='Time', yaxis_title='Sentiment Score')
return news_df, sentiment_fig
# Gradio interface
with gr.Blocks() as demo:
gr.Markdown("# Financial News Sentiment Analysis")
topic = gr.Textbox(label="Enter a financial topic or company name")
analyze_btn = gr.Button(value="Analyze")
news_output = gr.DataFrame(label="News and Sentiment Analysis")
sentiment_plot = gr.Plot(label="Sentiment Analysis")
analyze_btn.click(
news_and_analysis,
inputs=[topic],
outputs=[news_output, sentiment_plot]
)
if __name__ == "__main__":
demo.launch() |