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import streamlit as st
import yfinance as yf
import requests
from googleapiclient.discovery import build
from transformers import pipeline
import numpy as np
# YouTube API setup
def youtube_api_setup():
api_key = 'AIzaSyB-pW8FME-a7KMRqwEeQJStTxDPqvQNMm0' # Replace with your YouTube API key
youtube = build('youtube', 'v3', developerKey=api_key)
return youtube
# Fetch YouTube sentiment
def fetch_youtube_sentiment(symbol, youtube, sentiment_model):
search_response = youtube.search().list(q=symbol, part='snippet', maxResults=10).execute()
video_ids = [item['id']['videoId'] for item in search_response['items'] if 'videoId' in item['id']]
comments = []
for video_id in video_ids:
comment_response = youtube.commentThreads().list(part='snippet', videoId=video_id, maxResults=50).execute()
for comment in comment_response['items']:
comment_text = comment['snippet']['topLevelComment']['snippet']['textOriginal']
comments.append(comment_text)
sentiments = sentiment_model(comments)
sentiment_scores = [s['label'] for s in sentiments]
positive = sentiment_scores.count('POSITIVE')
negative = sentiment_scores.count('NEGATIVE')
return positive, negative
# Moving Average (technical analysis)
def calculate_moving_average(stock_data, window_size):
prices = stock_data['Close'].to_numpy()
moving_avg = np.convolve(prices, np.ones(window_size)/window_size, mode='valid')
return moving_avg
# Fetch stock data using Yahoo Finance
def fetch_stock_data(symbol):
stock = yf.Ticker(symbol)
stock_data = stock.history(period="1y")
return stock_data
# Main app
def main():
st.title("Empower_AI: powered by intel ONEapi")
# Input for stock symbol
stock_symbol = st.text_input("Enter Stock Symbol (e.g., AAPL, TSLA):", "AAPL")
if st.button("Analyze"):
# Fetch stock data
stock_data = fetch_stock_data(stock_symbol)
# Display stock data overview
st.subheader(f"Stock Overview - {stock_symbol}")
st.write(stock_data.tail())
# Sentiment analysis
sentiment_model = pipeline("sentiment-analysis")
# YouTube Sentiment
st.subheader("YouTube Sentiment Analysis")
youtube = youtube_api_setup()
positive_youtube, negative_youtube = fetch_youtube_sentiment(stock_symbol, youtube, sentiment_model)
st.write(f"Positive Comments: {positive_youtube}, Negative Comments: {negative_youtube}")
# Technical analysis (Moving Average)
st.subheader("Technical Analysis (Moving Average)")
window_size = st.slider("Select Moving Average Window Size:", 5, 100, 20)
moving_avg = calculate_moving_average(stock_data, window_size)
st.line_chart(moving_avg)
# Fundamental analysis
st.subheader("Fundamental Analysis")
st.write("Market Cap:", stock_data['Close'].iloc[-1] * stock_data['Volume'].mean())
st.write("Price-to-Earnings Ratio (P/E):", stock_data['Close'].iloc[-1] / (stock_data['Close'].mean()))
# Recommendation based on sentiment analysis
st.subheader("Stock Recommendation")
total_positive = positive_youtube
total_negative = negative_youtube
if total_positive > total_negative:
st.write(f"Recommendation: **BUY** {stock_symbol}")
elif total_negative > total_positive:
st.write(f"Recommendation: **SELL** {stock_symbol}")
else:
st.write(f"Recommendation: **HOLD** {stock_symbol}")
if __name__ == "__main__":
main()
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