Update app.py

app.py

@@ -6,21 +6,17 @@ from transformers import pipeline
 import plotly.express as px
 from datetime import datetime, timedelta
 
-# Load the Excel file with company names and symbols
 file_path = '/home/user/app/Top 2000 Valued Companies with Ticker Symbols.xlsx'
 companies_df = pd.read_excel(file_path)
 
-# Function to get stock symbol for a company name
 def get_stock_symbol(company_name):
     match = companies_df[companies_df['Name'].str.contains(company_name, case=False, na=False)]
     if not match.empty:
         return match.iloc[0]['Symbol']
     return None
 
-# 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'}
@@ -28,7 +24,6 @@ def encode_special_characters(text):
         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}"
@@ -58,12 +53,10 @@ def fetch_news(query, num_articles=10):
     
     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):
     url = "https://alpha-vantage.p.rapidapi.com/query"
     querystring = {"function":"TIME_SERIES_DAILY", "symbol":symbol, "outputsize":"compact", "datatype":"json"}
@@ -82,18 +75,14 @@ def fetch_stock_data(symbol):
     stock_data.columns = ["Open", "High", "Low", "Close", "Volume"]
     return stock_data
 
-# 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, None
     
-    # Perform sentiment analysis
     news_df['Sentiment'], news_df['Sentiment_Score'] = zip(*news_df['Title'].apply(analyze_sentiment))
     
-    # Create sentiment plot
     sentiment_fig = px.bar(
         news_df,
         x='Time',
@@ -104,7 +93,6 @@ def news_and_analysis(query):
         labels={'Time': 'Publication Time', 'Sentiment_Score': 'Sentiment Score'}
     )
     
-    # Check if query is a company name and fetch stock data
     stock_symbol = get_stock_symbol(query)
     if stock_symbol:
         stock_data = fetch_stock_data(stock_symbol)
@@ -114,7 +102,6 @@ def news_and_analysis(query):
     
     return news_df, sentiment_fig, None
 
-# Gradio interface
 with gr.Blocks() as demo:
     gr.Markdown(
         """
@@ -136,10 +123,4 @@ with gr.Blocks() as demo:
             stock_plot = gr.Plot(label="Stock Price Movement")
     
     analyze_btn.click(
-        news_and_analysis,
-        inputs=[topic],
-        outputs=[news_output, sentiment_plot, stock_plot]
-    )
-
-if __name__ == "__main__":
-    demo.launch()
+        news_and_analysi