Update app.py

app.py

@@ -1,20 +1,79 @@
 import streamlit as st
 from transformers import pipeline
 from PIL import Image
+import yfinance as yf
+import matplotlib.pyplot as plt
+import io
 
-pipeline = pipeline(task="image-classification", model="julien-c/bullish-or-bearish")
+# Load the image classification pipeline
+classifier = pipeline(task="image-classification", model="julien-c/bullish-or-bearish")
 
-st.title("Hot Dog? Or Not?")
+# Set up the Streamlit app title
+st.title("Stock Trend Analyzer: Bullish or Bearish? 📈📉")
 
-file_name = st.file_uploader("Upload a hot dog candidate image")
+# Input for stock ticker
+ticker = st.text_input("Enter a stock ticker (e.g., AAPL, GOOGL):", "AAPL")
 
-if file_name is not None:
-    col1, col2 = st.columns(2)
+# Date range selection
+col1, col2 = st.columns(2)
+start_date = col1.date_input("Start date")
+end_date = col2.date_input("End date")
 
-    image = Image.open(file_name)
-    col1.image(image, use_column_width=True)
-    predictions = pipeline(image)
+if st.button("Analyze"):
+    # Fetch stock data
+    stock_data = yf.download(ticker, start=start_date, end=end_date)
+    
+    if stock_data.empty:
+        st.error("No data available for the selected stock and date range.")
+    else:
+        # Create a plot of the stock data
+        plt.figure(figsize=(10, 6))
+        plt.plot(stock_data.index, stock_data['Close'])
+        plt.title(f"{ticker} Stock Price")
+        plt.xlabel("Date")
+        plt.ylabel("Price")
+        plt.grid(True)
+        
+        # Save the plot to a buffer
+        buf = io.BytesIO()
+        plt.savefig(buf, format='png')
+        buf.seek(0)
+        
+        # Convert the buffer to an image
+        image = Image.open(buf)
+        
+        # Display the stock graph
+        st.image(image, caption=f"{ticker} Stock Price", use_column_width=True)
+        
+        # Classify the image
+        with st.spinner("Analyzing the trend..."):
+            predictions = classifier(image)
+        
+        # Display the predictions
+        st.header("Trend Analysis")
+        for p in predictions:
+            sentiment = p['label'].split('_')[0].capitalize()
+            confidence = round(p['score'] * 100, 1)
+            st.subheader(f"{sentiment}: {confidence}%")
+            
+            # Add color-coded bars for visual representation
+            color = "green" if sentiment == "Bullish" else "red"
+            st.progress(confidence / 100, text=f"{sentiment} Confidence")
+        
+        # Additional analysis based on the stock data
+        price_change = stock_data['Close'].iloc[-1] - stock_data['Close'].iloc[0]
+        percent_change = (price_change / stock_data['Close'].iloc[0]) * 100
+        
+        st.subheader("Price Analysis")
+        st.write(f"Price change: ${price_change:.2f}")
+        st.write(f"Percent change: {percent_change:.2f}%")
+        
+        if percent_change > 0:
+            st.success(f"The stock price increased by {percent_change:.2f}% over the selected period.")
+        elif percent_change < 0:
+            st.error(f"The stock price decreased by {abs(percent_change):.2f}% over the selected period.")
+        else:
+            st.info("The stock price remained unchanged over the selected period.")
 
-    col2.header("Probabilities")
-    for p in predictions:
-        col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")
+else:
+    st.info("Enter a stock ticker and select a date range to analyze the trend.")