Update app.py

app.py

@@ -1,367 +1,158 @@
-import torch
-import yfinance as yf
-import matplotlib.pyplot as plt
-import mplfinance as mpf
-from PIL import Image, ImageDraw, ImageFont
-import gradio as gr
-import datetime
-import logging
-from transformers import AutoProcessor, AutoModelForPreTraining
-import tempfile
-import os
-import spaces
-import pandas as pd
-import numpy as np
-from scipy import stats
-import seaborn as sns
-
-# Configure logging
-logging.basicConfig(filename='debug.log', level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')
-
-# Load the chart_analysis model and processor
-processor = AutoProcessor.from_pretrained("mobenta/chart_analysis")
-model = AutoModelForPreTraining.from_pretrained("mobenta/chart_analysis")
-
-@spaces.GPU
-def predict(image, input_text):
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    model.to(device)
-
-    image = image.convert("RGB")
-    inputs = processor(text=input_text, images=image, return_tensors="pt")
-    inputs = {k: v.to(device) for k, v in inputs.items()}
-    
-    prompt_length = inputs['input_ids'].shape[1]
-    generate_ids = model.generate(**inputs, max_new_tokens=512)
-    output_text = processor.batch_decode(generate_ids[:, prompt_length:], skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-
-    return output_text
-
-def fetch_stock_data(ticker='TSLA', start='2010-01-01', end=None, interval='1d'):
-    if end is None:
-        end = datetime.date.today().isoformat()
-    try:
-        logging.debug(f"Fetching data for {ticker} from {start} to {end} with interval {interval}")
-        stock = yf.Ticker(ticker)
-        data = stock.history(start=start, end=end, interval=interval)
-        if data.empty:
-            logging.warning(f"No data fetched for {ticker} in the range {start} to {end}")
-            return pd.DataFrame()
-        logging.debug(f"Fetched data with {len(data)} rows")
-        return data
-    except Exception as e:
-        logging.error(f"Error fetching data: {e}")
-        return pd.DataFrame()
-
-def create_stock_chart(data, ticker, filename='chart.png', timeframe='1d', indicators=None):
-    try:
-        logging.debug(f"Creating chart for {ticker} with timeframe {timeframe} and saving to {filename}")
-        title = f"{ticker.upper()} Price Data (Timeframe: {timeframe})"
-
-        plt.rcParams["axes.titlesize"] = 10
-        my_style = mpf.make_mpf_style(base_mpf_style='charles')
-
-        # Calculate indicators if selected
-        addplot = []
-        if indicators:
-            if 'RSI' in indicators:
-                delta = data['Close'].diff(1)
-                gain = delta.where(delta > 0, 0)
-                loss = -delta.where(delta < 0, 0)
-                avg_gain = gain.rolling(window=14).mean()
-                avg_loss = loss.rolling(window=14).mean()
-                rs = avg_gain / avg_loss
-                rsi = 100 - (100 / (1 + rs))
-                addplot.append(mpf.make_addplot(rsi, panel=2, color='orange', ylabel='RSI'))
-            if 'SMA21' in indicators:
-                logging.debug("Calculating SMA 21")
-                sma_21 = data['Close'].rolling(window=21).mean()
-                addplot.append(mpf.make_addplot(sma_21, color='purple', linestyle='dashed'))
-            if 'SMA50' in indicators:
-                logging.debug("Calculating SMA 50")
-                sma_50 = data['Close'].rolling(window=50).mean()
-                addplot.append(mpf.make_addplot(sma_50, color='orange', linestyle='dashed'))
-            if 'SMA200' in indicators:
-                logging.debug("Calculating SMA 200")
-                sma_200 = data['Close'].rolling(window=200).mean()
-                addplot.append(mpf.make_addplot(sma_200, color='brown', linestyle='dashed'))
-            if 'VWAP' in indicators:
-                logging.debug("Calculating VWAP")
-                vwap = (data['Volume'] * (data['High'] + data['Low'] + data['Close']) / 3).cumsum() / data['Volume'].cumsum()
-                addplot.append(mpf.make_addplot(vwap, color='blue', linestyle='dashed'))
-            if 'Bollinger Bands' in indicators:
-                logging.debug("Calculating Bollinger Bands")
-                sma = data['Close'].rolling(window=20).mean()
-                std = data['Close'].rolling(window=20).std()
-                upper_band = sma + (std * 2)
-                lower_band = sma - (std * 2)
-                addplot.append(mpf.make_addplot(upper_band, color='green', linestyle='dashed'))
-                addplot.append(mpf.make_addplot(lower_band, color='green', linestyle='dashed'))
-
-        fig, axlist = mpf.plot(data, type='candle', style=my_style, volume=True, addplot=addplot, returnfig=True)
-        fig.suptitle(title, y=0.98)
-
-        # Save chart image
-        fig.savefig(filename, dpi=300)
-        plt.close(fig)
-
-        # Open and add financial data to the image
-        image = Image.open(filename)
-        draw = ImageDraw.Draw(image)
-        font = ImageFont.load_default()  # Use default font, you can also use custom fonts if available
-
-        # Financial metrics to add
-        metrics = {
-            "Ticker": ticker,
-            "Latest Close": f"${data['Close'].iloc[-1]:,.2f}",
-            "Volume": f"{data['Volume'].iloc[-1]:,.0f}"
-        }
-
-        # Add additional metrics if indicators are present
-        if 'SMA21' in indicators:
-            metrics["SMA 21"] = f"${data['Close'].rolling(window=21).mean().iloc[-1]:,.2f}"
-        if 'SMA50' in indicators:
-            metrics["SMA 50"] = f"${data['Close'].rolling(window=50).mean().iloc[-1]:,.2f}"
-        if 'SMA200' in indicators:
-            metrics["SMA 200"] = f"${data['Close'].rolling(window=200).mean().iloc[-1]:,.2f}"
-
-        # Draw metrics on the image
-        y_text = image.height - 50  # Starting y position for text
-        for key, value in metrics.items():
-            text = f"{key}: {value}"
-            draw.text((10, y_text), text, font=font, fill=(255, 255, 255))  # White color text
-            y_text += 20
-
-        # Resize image
-        new_size = (image.width * 3, image.height * 3)
-        resized_image = image.resize(new_size, Image.LANCZOS)
-        resized_image.save(filename)
-
-        logging.debug(f"Resized image with timeframe {timeframe} and ticker {ticker} saved to {filename}")
-    except Exception as e:
-        logging.error(f"Error creating or resizing chart: {e}")
-        raise
-
-def combine_images(image_paths, output_path='combined_chart.png'):
-    try:
-        logging.debug(f"Combining images {image_paths} into {output_path}")
-        images = [Image.open(path) for path in image_paths]
-
-        # Calculate total width and max height for combined image
-        total_width = sum(img.width for img in images)
-        max_height = max(img.height for img in images)
-
-        combined_image = Image.new('RGB', (total_width, max_height))
-        x_offset = 0
-        for img in images:
-            combined_image.paste(img, (x_offset, 0))
-            x_offset += img.width
-
-        combined_image.save(output_path)
-        logging.debug(f"Combined image saved to {output_path}")
-        return output_path
-    except Exception as e:
-        logging.error(f"Error combining images: {e}")
-        raise
-
-def perform_trend_analysis(data):
-    # Perform trend analysis
-    close_prices = data['Close']
-    time_index = np.arange(len(close_prices))
-    slope, intercept, r_value, p_value, std_err = stats.linregress(time_index, close_prices)
-    
-    trend_line = slope * time_index + intercept
-    
-    plt.figure(figsize=(12, 6))
-    plt.plot(close_prices.index, close_prices, label='Close Price')
-    plt.plot(close_prices.index, trend_line, color='red', label='Trend Line')
-    plt.title('Trend Analysis')
-    plt.xlabel('Date')
-    plt.ylabel('Price')
-    plt.legend()
-    
-    trend_filename = 'trend_analysis.png'
-    plt.savefig(trend_filename)
-    plt.close()
-    
-    trend_strength = abs(r_value)
-    trend_direction = "upward" if slope > 0 else "downward"
-    
-    analysis_text = f"Trend Analysis:\n"
-    analysis_text += f"The stock shows a {trend_direction} trend.\n"
-    analysis_text += f"Trend strength (R-squared): {trend_strength:.2f}\n"
-    analysis_text += f"Slope: {slope:.4f}\n"
-    
-    return analysis_text, trend_filename
-
-def perform_correlation_analysis(data_dict):
-    # Perform correlation analysis
-    combined_data = pd.DataFrame({ticker: data['Close'] for ticker, data in data_dict.items()})
-    correlation_matrix = combined_data.corr()
-    
-    plt.figure(figsize=(10, 8))
-    sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', vmin=-1, vmax=1, center=0)
-    plt.title('Correlation Analysis')
-    
-    corr_filename = 'correlation_analysis.png'
-    plt.savefig(corr_filename)
-    plt.close()
-    
-    analysis_text = f"Correlation Analysis:\n"
-    for i in range(len(correlation_matrix.columns)):
-        for j in range(i+1, len(correlation_matrix.columns)):
-            ticker1 = correlation_matrix.columns[i]
-            ticker2 = correlation_matrix.columns[j]
-            corr = correlation_matrix.iloc[i, j]
-            analysis_text += f"Correlation between {ticker1} and {ticker2}: {corr:.2f}\n"
-    
-    return analysis_text, corr_filename
-
-def comprehensive_investment_strategy():
-    strategy = """
-    Comprehensive Investment Strategy Analysis:
-
-    1. Fundamental Analysis:
-    - Assess financial health using key metrics like P/E ratio, EPS growth, debt-to-equity ratio, and free cash flow.
-    - Analyze quarterly and annual earnings reports, focusing on revenue growth, profit margins, and management guidance.
-    - Consider industry trends such as technological disruption, regulatory changes, and shifting consumer preferences.
-
-    2. Technical Analysis:
-    - Utilize chart patterns like head and shoulders, double tops/bottoms, and cup and handle.
-    - Employ technical indicators including Moving Averages, RSI, MACD, and Bollinger Bands.
-    - Incorporate volume analysis to confirm trend strength and potential reversals.
-
-    3. Macroeconomic Analysis:
-    - Monitor key economic indicators: GDP growth, inflation rates, unemployment figures, and consumer sentiment indices.
-    - Track central bank policies, particularly interest rate decisions and quantitative easing programs.
-    - Evaluate geopolitical events' impact through news analysis and global market correlations.
-
-    4. Risk Management:
-    - Implement diversification across sectors, geographies, and asset classes.
-    - Use position sizing based on account size and individual stock volatility.
-    - Set stop-loss orders at key technical levels, typically 5-15% below purchase price depending on stock volatility.
-
-    5. Sentiment Analysis:
-    - Gauge market sentiment through tools like the VIX, put/call ratio, and investor surveys.
-    - Monitor social media trends and financial news sentiment using natural language processing tools.
-    - Apply contrarian strategies when extreme bullish or bearish sentiment is detected, supported by fundamental and technical analysis.
-
-    6. Options Trading:
-    - Employ covered calls for income generation on long-term holds.
-    - Use protective puts to hedge downside risk on larger positions.
-    - Implement iron condors or credit spreads to take advantage of high implied volatility environments.
-
-    7. Long-Term Investing:
-    - Identify companies with strong competitive advantages, consistent revenue growth, and solid balance sheets.
-    - Focus on businesses with high return on invested capital (ROIC) and effective management teams.
-    - Include dividend aristocrats and growth stocks at a reasonable price (GARP) for a balanced approach.
-
-    8. Market Psychology:
-    - Apply principles of behavioral finance, recognizing common biases like herd mentality and recency bias.
-    - Maintain a trading journal to track decisions and emotions, promoting self-awareness and improvement.
-    - Develop and stick to a rules-based system to minimize emotional decision-making.
-
-    This comprehensive strategy aims to balance various analytical approaches, providing a robust framework for investment decisions across different market conditions.
-    """
-    return strategy
-
-def gradio_interface(ticker1, ticker2, ticker3, ticker4, start_date, end_date, query, analysis_type, interval, indicators):
-    try:
-        logging.debug(f"Starting gradio_interface with tickers: {ticker1}, {ticker2}, {ticker3}, {ticker4}, start_date: {start_date}, end_date: {end_date}, query: {query}, analysis_type: {analysis_type}, interval: {interval}")
-
-        if analysis_type == 'Comprehensive Investment Strategy':
-            return comprehensive_investment_strategy(), None
-
-        tickers = [ticker1, ticker2, ticker3, ticker4]
-        chart_paths = []
-        data_dict = {}
-
-        for i, ticker in enumerate(tickers):
-            if ticker:
-                data = fetch_stock_data(ticker, start=start_date, end=end_date, interval=interval)
-                if data.empty:
-                    return f"No data available for {ticker} in the specified date range.", None
-                data_dict[ticker] = data
-                with tempfile.NamedTemporaryFile(delete=False, suffix='.png') as temp_chart:
-                    chart_path = temp_chart.name
-                    create_stock_chart(data, ticker, chart_path, timeframe=interval, indicators=indicators)
-                    chart_paths.append(chart_path)
-
-        if analysis_type == 'Comparative Analysis' and len(chart_paths) > 1:
-            with tempfile.NamedTemporaryFile(delete=False, suffix='.png') as temp_combined_chart:
-                combined_chart_path = temp_combined_chart.name
-                combine_images(chart_paths, combined_chart_path)
-                insights = predict(Image.open(combined_chart_path), query)
-                return insights, combined_chart_path
-        elif analysis_type == 'Trend Analysis':
-            if len(data_dict) > 0:
-                first_ticker = list(data_dict.keys())[0]
-                analysis_text, trend_chart = perform_trend_analysis(data_dict[first_ticker])
-                return analysis_text, trend_chart
-            else:
-                return "No data available for trend analysis.", None
-        elif analysis_type == 'Correlation Analysis':
-            if len(data_dict) > 1:
-                analysis_text, corr_chart = perform_correlation_analysis(data_dict)
-                return analysis_text, corr_chart
-            else:
-                return "At least two tickers are required for correlation analysis.", None
-        else:
-# Single ticker analysis
-            if chart_paths:
-                insights = predict(Image.open(chart_paths[0]), query)
-                return insights, chart_paths[0]
-            else:
-                return "No tickers provided.", None
-    except Exception as e:
-        logging.error(f"Error in Gradio interface: {e}")
-        return f"Error processing image or query: {e}", None
-
 def gradio_app():
     with gr.Blocks() as demo:
         gr.Markdown("""
         ## 📈 Advanced Stock Analysis Dashboard
-
-        This application provides a comprehensive stock analysis tool that allows users to input up to four stock tickers, specify date ranges, and select various financial indicators. The core functionalities include:
-
-        1. **Data Fetching and Chart Creation**: Historical stock data is fetched from Yahoo Finance, and candlestick charts are generated with optional financial indicators like RSI, SMA, VWAP, and Bollinger Bands.
-
-        2. **Text Analysis and Insights Generation**: The application uses a pre-trained model based on the Paligema architecture to analyze the input chart and text query, generating insightful analysis based on the provided financial data and context.
-
-        3. **Trend Analysis**: Performs trend analysis on a single stock, showing the trend line and providing information about the trend strength and direction.
-
-        4. **Correlation Analysis**: Analyzes the correlation between multiple stocks, providing a correlation matrix and heatmap.
-
-        5. **Comprehensive Investment Strategy**: Provides a detailed investment strategy based on fundamental analysis, technical analysis, macroeconomic factors, risk management, and more.
-
-        6. **User Interface**: Users can interactively select stocks, date ranges, intervals, and indicators. The app supports single ticker analysis, comparative analysis, trend analysis, correlation analysis, and comprehensive investment strategy.
-
-        7. **Logging and Debugging**: Detailed logging helps in debugging and tracking the application's processes.
-
-        8. **Enhanced Image Processing**: The app adds financial metrics and annotations to the generated charts, ensuring clear presentation of data.
-
-        This tool leverages various analysis techniques to provide detailed insights into stock market trends, offering an interactive and educational experience for users.
+        
+        [... rest of the markdown ...]
         """)
 
-        with gr.Row():
-            ticker1 = gr.Textbox(label="Primary Ticker", value="AAPL")
-            ticker2 = gr.Textbox(label="Secondary Ticker", value="MSFT")
-            ticker3 = gr.Textbox(label="Third Ticker", value="GOOGL")
-            ticker4 = gr.Textbox(label="Fourth Ticker", value="AMZN")
-
-        with gr.Row():
-            start_date = gr.Textbox(label="Start Date", value="2022-01-01")
-            end_date = gr.Textbox(label="End Date", value=datetime.date.today().isoformat())
-            interval = gr.Dropdown(label="Interval", choices=['1m', '2m', '5m', '15m', '30m', '60m', '90m', '1h', '1d', '5d', '1wk', '1mo', '3mo'], value='1d')
+        ticker_options = [
+            "ES=F: E-Mini S&P 500 Sep 24",
+            "YM=F: Mini Dow Jones Indus.-$5 Sep 24",
+            "NQ=F: Nasdaq 100 Sep 24",
+            "RTY=F: E-mini Russell 2000 Index Future",
+            "ZB=F: U.S. Treasury Bond Futures, Sep-",
+            "ZN=F: 10-Year T-Note Futures, Sep-2024",
+            "ZF=F: Five-Year US Treasury Note Future",
+            "ZT=F: 2-Year T-Note Futures, Sep-2024",
+            "GC=F: Gold",
+            "MGC=F: Micro Gold Futures, Dec-2024",
+            "SI=F: Silver",
+            "SIL=F: Micro Silver Futures, Sep-2024",
+            "PL=F: Platinum Oct 24",
+            "HG=F: Copper Sep 24",
+            "PA=F: Palladium Sep 24",
+            "CL=F: Crude Oil",
+            "HO=F: Heating Oil Aug 24",
+            "NG=F: Natural Gas Aug 24",
+            "RB=F: RBOB Gasoline Aug 24",
+            "BZ=F: Brent Crude Oil Last Day Financ",
+            "BO=F: Mont Belvieu LDH Propane (OPIS)",
+            "ZC=F: Corn Futures, Dec-2024",
+            "ZO=F: Oat Futures, Dec-2024",
+            "KE=F: KC HRW Wheat Futures, Sep-2024",
+            "ZR=F: Rough Rice Futures, Sep-2024",
+            "ZM=F: S&P Composite 1500 ESG Tilted I",
+            "ZL=F: Soybean Oil Futures, Dec-2024",
+            "ZS=F: Soybean Futures, Nov-2024",
+            "GF=F: WisdomTree International High D",
+            "HE=F: Lean Hogs Futures, Aug-2024",
+            "LE=F: Live Cattle Futures, Aug-2024",
+            "CC=F: Cocoa Sep 24",
+            "KC=F: Coffee Sep 24",
+            "CT=F: Cotton Oct 24",
+            "LBS=F: Random Length Lumber Futures",
+            "OJ=F: Orange Juice Sep 24",
+            "^IRX: 13 WEEK TREASURY BILL",
+            "^FVX: Treasury Yield 5 Years",
+            "^TNX: CBOE Interest Rate 10 Year T No",
+            "^TYX: Treasury Yield 30 Years",
+            "EURUSD=X: EUR/USD",
+            "JPY=X: USD/JPY",
+            "GBPUSD=X: GBP/USD",
+            "AUDUSD=X: AUD/USD",
+            "NZDUSD=X: NZD/USD",
+            "EURJPY=X: EUR/JPY",
+            "GBPJPY=X: GBP/JPY",
+            "EURGBP=X: EUR/GBP",
+            "EURCAD=X: EUR/CAD",
+            "EURSEK=X: EUR/SEK",
+            "EURCHF=X: EUR/CHF",
+            "EURHUF=X: EUR/HUF",
+            "EURJPY=X: EUR/JPY",
+            "CNY=X: USD/CNY",
+            "HKD=X: USD/HKD",
+            "SGD=X: USD/SGD",
+            "INR=X: USD/INR",
+            "MXN=X: USD/MXN",
+            "PHP=X: USD/PHP",
+            "IDR=X: USD/IDR",
+            "THB=X: USD/THB",
+            "MYR=X: USD/MYR",
+            "ZAR=X: USD/ZAR",
+            "RUB=X: USD/RUB",
+            "BTC-USD: Bitcoin USD",
+            "ETH-USD: Ethereum USD",
+            "USDT-USD: Tether USDT USD",
+            "BNB-USD: BNB USD",
+            "SOL-USD: Solana USD",
+            "USDC-USD: USD Coin USD",
+            "XRP-USD: XRP",
+            "STETH-USD: Lido Staked ETH USD",
+            "DOGE-USD: Dogecoin USD",
+            "TON11419-USD: Toncoin USD",
+            "ADA-USD: Cardano USD",
+            "WSTETH-USD: Lido wstETH USD",
+            "WTRX-USD: Wrapped TRON USD",
+            "TRX-USD: TRON USD",
+            "AVAX-USD: Avalanche USD",
+            "WETH-USD: Wrapped Bitcoin USD",
+            "SHIB-USD: Shiba Inu USD",
+            "DOT-USD: Polkadot USD",
+            "LINK-USD: Chainlink USD",
+            "BCH-USD: Bitcoin Cash USD",
+            "EDLC-USD: Edelman USD",
+            "NEAR-USD: NEAR Protocol USD",
+            "EETH-USD: staked.fi ETH USD",
+            "LEO-USD: UNUS SED LEO USD",
+            "TSLA: Tesla, Inc.",
+            "NVDA: NVIDIA Corporation",
+            "F: Ford Motor Company",
+            "DXCM: DexCom, Inc.",
+            "SWN: Southwestern Energy Company",
+            "AAL: American Airlines Group Inc.",
+            "AMD: Advanced Micro Devices, Inc.",
+            "PLUG: Plug Power Inc.",
+            "BAC: Bank of America Corporation",
+            "MARA: Marathon Digital Holdings, Inc.",
+            "GOOGL: Alphabet Inc.",
+            "AAPL: Apple Inc.",
+            "MMM: 3M Company",
+            "NKE: NIKE, Inc.",
+            "PFE: Pfizer Inc.",
+            "SMYMY: Sirius XM Holdings Inc.",
+            "INTC: Intel Corporation",
+            "SOFI: SoFi Technologies, Inc.",
+            "BTGR: BTS Group Holdings Public Company Limited",
+            "LCID: Lucid Group, Inc.",
+            "PLTR: Palantir Technologies Inc.",
+            "NWL: Newell Brands Inc.",
+            "VALE: Vale S.A.",
+            "CLSK: CleanSpark, Inc.",
+            "BTE: Baytex Energy Corp.",
+            "ASTS: AST SpaceMobile, Inc.",
+            "ET: Energy Transfer LP",
+            "AMZN: Amazon.com, Inc.",
+            "HBI: Hanesbrands Inc.",
+            "CMG: Chipotle Mexican Grill, Inc.",
+            "GOOG: Alphabet Inc.",
+            "AGNC: AGNC Investment Corp.",
+            "RIVN: Rivian Automotive, Inc.",
+            "GOLD: Barrick Gold Corporation",
+            "AVTR: Avantor, Inc.",
+            "CMCSA: Comcast Corporation",
+            "RIG: Transocean Ltd.",
+            "MUV2: Micron Technology, Inc.",
+            "WBD: Warner Bros. Discovery, Inc.",
+            "AVGO: Broadcom Inc.",
+            "CCL: Carnival Corporation & plc",
+            "MSFT: Microsoft Corporation",
+            "WMT: Walmart Inc.",
+            "HBAN: Huntington Bancshares Incorporated",
+            "GM: General Motors Company",
+            "BN: Brookfield Corporation",
+            "CSCO: Cisco Systems, Inc.",
+            "ERIC: Telefonaktiebolaget LM Ericsson (publ)",
+            "INFY: Infosys Limited"
+        ]
 
         with gr.Row():
-            indicators = gr.CheckboxGroup(label="Indicators", choices=['RSI', 'SMA21', 'SMA50', 'SMA200', 'VWAP', 'Bollinger Bands'], value=['SMA21', 'SMA50'])
-            analysis_type = gr.Radio(label="Analysis Type", choices=['Single Ticker', 'Comparative Analysis', 'Trend Analysis', 'Correlation Analysis', 'Comprehensive Investment Strategy'], value='Single Ticker')
+            ticker1 = gr.Dropdown(label="Primary Ticker", choices=ticker_options, value="AAPL: Apple Inc.")
+            ticker2 = gr.Dropdown(label="Secondary Ticker", choices=ticker_options, value="MSFT: Microsoft Corporation")
+            ticker3 = gr.Dropdown(label="Third Ticker", choices=ticker_options, value="GOOGL: Alphabet Inc.")
+            ticker4 = gr.Dropdown(label="Fourth Ticker", choices=ticker_options, value="AMZN: Amazon.com, Inc.")
 
-        query = gr.Textbox(label="Analysis Query", value="Analyze the price trends.")
-        analyze_button = gr.Button("Analyze")
-        output_image = gr.Image(label="Analysis Chart")
-        output_text = gr.Textbox(label="Generated Insights", lines=10)
+        # ... rest of the code ...
 
         analyze_button.click(
             fn=gradio_interface,