Update app.py

app.py

@@ -1,103 +1,40 @@
-import sys
-import subprocess
-
-# Check and install required libraries
-required_libraries = ["streamlit", "transformers", "yfinance", "matplotlib", "Pillow"]
-for lib in required_libraries:
-    try:
-        __import__(lib)
-    except ImportError:
-        subprocess.check_call([sys.executable, "-m", "pip", "install", lib])
-
 import streamlit as st
 from transformers import pipeline
 from PIL import Image
-import yfinance as yf
-import matplotlib.pyplot as plt
 import io
 
-# Cache the stock data retrieval
-@st.cache_data
-def get_stock_data(ticker, start_date, end_date):
-    return yf.download(ticker, start=start_date, end=end_date)
-
 # Load the image classification pipeline
 try:
-    classifier = pipeline(task="image-classification", model="microsoft/resnet-50")
+    classifier = pipeline(task="image-classification", model="julien-c/bullish-or-bearish")
 except Exception as e:
     st.error(f"Error loading the model: {e}")
     st.stop()
 
 # Set up the Streamlit app title
-st.title("Stock Trend Analyzer: Bullish or Bearish? 📈📉")
-
-# Input for stock ticker
-ticker = st.text_input("Enter a stock ticker (e.g., AAPL, GOOGL):", "AAPL")
+st.title("Stock Chart Analyzer: Bullish or Bearish? 📈📉")
 
-# Date range selection
-col1, col2 = st.columns(2)
-start_date = col1.date_input("Start date")
-end_date = col2.date_input("End date")
+# File uploader for image input
+uploaded_file = st.file_uploader("Upload a stock chart image", type=["jpg", "jpeg", "png"])
 
-if st.button("Analyze"):
-    # Fetch stock data
-    with st.spinner("Fetching stock data..."):
-        stock_data = get_stock_data(ticker, start_date, end_date)
+if uploaded_file is not None:
+    # Display the uploaded image
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Stock Chart", use_column_width=True)
     
-    if stock_data.empty:
-        st.error("No data available for the selected stock and date range.")
-    else:
-        # Create a plot of the stock data
-        fig, ax = plt.subplots(figsize=(10, 6))
-        ax.plot(stock_data.index, stock_data['Close'])
-        ax.set_title(f"{ticker} Stock Price")
-        ax.set_xlabel("Date")
-        ax.set_ylabel("Price")
-        ax.grid(True)
-        
-        # Save the plot to a buffer
-        buf = io.BytesIO()
-        fig.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[:2]:  # Display top 2 predictions
-            sentiment = "Bullish" if "up" in p['label'].lower() else "Bearish"
-            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}%")
+    # 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}%")
         
-        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.")
+        # Add color-coded bars for visual representation
+        color = "green" if sentiment == "Bullish" else "red"
+        st.progress(confidence / 100, text=f"{sentiment} Confidence")
 
 else:
-    st.info("Enter a stock ticker and select a date range to analyze the trend.")
-
-# Clean up matplotlib figures
-plt.close('all')
+    st.info("Please upload a stock chart image to analyze the trend.")