Create app.py

app.py

@@ -0,0 +1,122 @@
+import streamlit as st
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+import requests
+import daal4py as d4p  # Intel DAAL
+import numpy as np
+import dpctl
+from sklearnex import patch_sklearn, config_context
+patch_sklearn()
+
+# Alpha Vantage API Setup (replace with your API key)
+ALPHA_VANTAGE_API_KEY = "your_alpha_vantage_api_key"
+
+# Initialize Hugging Face's sentiment analysis pipeline
+@st.cache_resource
+def load_sentiment_model():
+    return pipeline("sentiment-analysis", model="huggingface/llama-3b-instruct")
+
+# Load LLaMA model for custom recommendations or Q&A
+@st.cache_resource
+def load_llama_model():
+    model_name = "meta-llama/Llama-2-7b-chat-hf"  # Adjust this to your preferred model
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+    return tokenizer, model
+
+# Fetch stock data using Alpha Vantage
+def fetch_stock_data(symbol):
+    url = f"https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol={symbol}&apikey={ALPHA_VANTAGE_API_KEY}"
+    response = requests.get(url)
+    return response.json().get("Time Series (Daily)", {})
+
+# Compute Moving Average using Intel oneDAL
+def compute_moving_average(prices, window=5):
+    # Convert prices to a NumPy array and reshape it for DAAL
+    import numpy as np
+    price_array = np.array(prices, dtype=np.float64).reshape(-1, 1)
+
+    # Initialize Intel DAAL low-order moments algorithm (for moving average)
+    algorithm = d4p.low_order_moments()
+    
+    # Apply rolling window and calculate moving averages
+    moving_averages = []
+    for i in range(len(price_array) - window + 1):
+        window_data = price_array[i:i + window]
+        result = algorithm.compute(window_data)
+        moving_averages.append(result.mean[0])
+    
+    return moving_averages
+
+# Perform technical analysis using Alpha Vantage and oneDAL
+def technical_analysis(symbol):
+    data = fetch_stock_data(symbol)
+    
+    if data:
+        # Extract closing prices from the time series data
+        closing_prices = [float(v['4. close']) for v in data.values()]
+        dates = list(data.keys())
+
+        # Compute 5-day moving average using oneDAL
+        moving_averages = compute_moving_average(closing_prices)
+
+        # Display latest date's price and moving average
+        latest_date = dates[0]
+        latest_price = closing_prices[0]
+        latest_moving_average = moving_averages[0] if moving_averages else "N/A"
+        
+        return {
+            "Date": latest_date,
+            "Closing Price": latest_price,
+            "5-Day Moving Average": latest_moving_average
+        }
+    return {}
+
+# Streamlit Web App
+def main():
+    st.title("Stock Analysis App with Intel oneDAL")
+    st.write("""
+    This app provides a comprehensive stock analysis including:
+    - Sentiment Analysis of recent news
+    - Fundamental Analysis (Market Cap, PE Ratio, EPS)
+    - Technical Analysis (Prices, Moving Average using Intel oneDAL)
+    - Buy/Sell/Hold Recommendations
+    """)
+
+    # Input: Stock symbol of a public listed company
+    company_symbol = st.text_input("Enter the stock symbol (e.g., AAPL, TSLA, GOOGL):")
+
+    if company_symbol:
+        try:
+            # Fetch stock data from Alpha Vantage API
+            stock_data = fetch_stock_data(company_symbol)
+            
+            if stock_data:
+                # Display the fetched stock overview
+                st.subheader("Asset Overview")
+                st.json(stock_data)
+
+                # Split the sections into different boxes using Streamlit's expander
+                with st.expander("Technical Analysis (Intel oneDAL)"):
+                    st.subheader("Technical Analysis")
+                    tech_analysis = technical_analysis(company_symbol)
+                    st.write(tech_analysis)
+
+                with st.expander("Sentiment Analysis"):
+                    st.subheader("Sentiment Analysis")
+                    sentiment_model = load_sentiment_model()
+                    sentiment = sentiment_analysis(company_symbol, sentiment_model)
+                    st.write(sentiment)
+
+                with st.expander("Recommendation"):
+                    st.subheader("Recommendation")
+                    tokenizer, llama_model = load_llama_model()
+                    stock_recommendation = recommendation(company_symbol, tokenizer, llama_model)
+                    st.write(stock_recommendation)
+            else:
+                st.error(f"No data available for the symbol entered.")
+
+        except Exception as e:
+            st.error(f"An error occurred: {e}")
+
+if __name__ == "__main__":
+    main()