Create app.py

app.py

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+import gradio as gr
+import requests
+from bs4 import BeautifulSoup
+import pandas as pd
+from transformers import pipeline
+import yfinance as yf
+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, verify=False)
+        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']
+
+# Function to fetch stock data
+def fetch_stock_data(symbol, start_date, end_date):
+    stock = yf.Ticker(symbol)
+    data = stock.history(start=start_date, end=end_date)
+    return data
+
+# Main function to process news and perform analysis
+def news_and_analysis(query, stock_symbol):
+    # Fetch news
+    news_df = fetch_news(query)
+    
+    if news_df.empty:
+        return "No news articles found.", None, None
+    
+    # Perform sentiment analysis
+    news_df['Sentiment'], news_df['Sentiment_Score'] = zip(*news_df['Title'].apply(analyze_sentiment))
+    
+    # Fetch stock data (last 30 days)
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=30)
+    stock_data = fetch_stock_data(stock_symbol, start_date, end_date)
+    
+    # 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')
+    
+    # Create stock price plot
+    stock_fig = go.Figure(data=[go.Candlestick(
+        x=stock_data.index,
+        open=stock_data['Open'],
+        high=stock_data['High'],
+        low=stock_data['Low'],
+        close=stock_data['Close']
+    )])
+    stock_fig.update_layout(title=f'{stock_symbol} Stock Price', xaxis_title='Date', yaxis_title='Price')
+    
+    return news_df, sentiment_fig, stock_fig
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Financial News Sentiment Analysis and Market Impact")
+    
+    with gr.Row():
+        topic = gr.Textbox(label="Enter a financial topic or company name")
+        stock_symbol = gr.Textbox(label="Enter the stock symbol (e.g., RELIANCE.NS for Reliance Industries)")
+    
+    analyze_btn = gr.Button(value="Analyze")
+    
+    news_output = gr.DataFrame(label="News and Sentiment Analysis")
+    sentiment_plot = gr.Plot(label="Sentiment Analysis")
+    stock_plot = gr.Plot(label="Stock Price Movement")
+    
+    analyze_btn.click(
+        news_and_analysis,
+        inputs=[topic, stock_symbol],
+        outputs=[news_output, sentiment_plot, stock_plot]
+    )
+
+
+demo.launch()