Update app.py

app.py

@@ -1,4 +1,4 @@
-import yfinance as yf
+mport yfinance as yf
 import matplotlib.pyplot as plt
 import numpy as np
 from concurrent.futures import ThreadPoolExecutor, as_completed
@@ -9,14 +9,84 @@ import gradio as gr
 from cachetools import cached, TTLCache
 import cProfile
 import pstats
-import timesfm  # Import the TimeSFM module
 
 # Global fontsize variable
 FONT_SIZE = 32
 
 # Company ticker mapping
 COMPANY_TICKERS = { 
-    # [same as provided]
+    'Google': 'GOOGL',
+    'NVIDIA': 'NVDA',
+    'Microsoft': 'MSFT',
+    'Twilio': 'TWLO',
+    'Meta': 'META',
+    'Workday': 'WDAY',
+    'SAP': 'SAP',
+    'Apple': 'AAPL',
+    'Tesla': 'TSLA',
+    'Amazon': 'AMZN',
+    'Oracle': 'ORCL',
+    'Uber': 'UBER',
+    'ADP': 'ADP',
+    'Adobe':'ADBE',
+    'Cadence Design Systems': 'CDNS',
+    'Salesforce':'CRM',
+    'Constellation Software': 'CNSWF', 
+    'Palo Alto': 'PANW',
+    'Autodesk': 'ADSK',
+    'Intuit': 'INTU',
+    'Confluent': 'CFLT',
+    'CrowdStrike': 'CRWD',
+    'UIPath':'PATH',
+    'Synopsys': 'SNPS',
+    'Palantir': 'PLTR',
+    'Mongodb': 'MDB',
+    'Reddit':'RDDT',
+    'Cloudflare': 'NET',
+    'DoorDash':'DASH',
+    'Datadog': 'DDOG',
+    'Duolingo': 'DUOL',
+    'HubSpot': 'HUBS',
+    'Trade Desk': 'TTD',
+    'Samsara': 'IOT',
+    'ServiceNow': 'NOW',
+    'ANSYS': 'ANSS',
+    'Zeta Global Holdings': 'ZETA',
+    'Veeva Systems' : 'VEEV',
+    'Box Inc': 'BOX',
+    'Airbnb': 'ABNB',
+    'AppFolio': 'APPF',
+    'Fortinet': 'FTNT',
+    'Snowflake': 'SNOW',
+    'Zscaler': 'ZS',
+    'Okta': 'OKTA',
+    'Docusign':'DOCU',
+    'Elastic NV': 'ESTC',
+    'NetApp': 'NTAP',
+    'Guidewire': 'GWRE',
+    'Monday.com': 'MNDY',
+    'Atlassian': 'TEAM',
+    'Shopify': 'SHOP',
+    'HashiCorp': 'HCP',
+    'Qualys': 'QLYS',
+    'Gitlab': 'GTLB',
+    'JFrog': 'FROG',
+    'Procore': 'PCOR',
+    'C3.ai': 'AI',
+    'Dynatrace': 'DT',
+    'Rubrik': 'RBRK',  
+    'nCino': 'NCNO',
+    'SentinelOne': 'S',
+    'Klaviyo': 'KVYO',
+    'Braze': 'BRZE',
+    'Q2': 'QTWO',
+    'Tenable': 'TENB',
+    'DigitalOcean': 'DOCN',
+    'Workiva': 'WK',
+    'Smartsheet': 'SMAR',
+    'Unity Software': 'U',
+    'Squarespace': 'SQSP',
+    'Wix.com': 'WIX'
 }
 
 # Cache with 1-day TTL
@@ -56,29 +126,6 @@ def plot_to_image(plt, title, market_cap):
     buf.seek(0)
     return Image.open(buf)
 
-def generate_forecast(data, horizon=30):
-    """Generate a 30-day close price forecast using TimeSFM."""
-    tfm = timesfm.TimesFm(
-        context_len=128,
-        horizon_len=horizon,
-        input_patch_len=32,
-        output_patch_len=128,
-        num_layers=20,
-        model_dims=1280,
-        backend='cpu',  # or 'gpu'
-    )
-    tfm.load_from_checkpoint('path_to_checkpoint')
-    
-    # Prepare data for forecasting
-    close_prices = data['Close'].values[-128:]  # Use the last 128 days for context
-    forecast, lower_bound, upper_bound = tfm.predict(close_prices)
-    
-    # Generate future dates
-    last_date = data.index[-1]
-    future_dates = [last_date + np.timedelta64(i, 'D') for i in range(1, horizon + 1)]
-    
-    return future_dates, forecast, lower_bound, upper_bound
-
 def plot_indicator(data, company_name, ticker, indicator, market_cap):
     """Plot selected technical indicator for a single company."""
     plt.figure(figsize=(16, 10))
@@ -98,12 +145,6 @@ def plot_indicator(data, company_name, ticker, indicator, market_cap):
         plt.plot(data.index, signal, label='Signal Line')
         plt.bar(data.index, macd - signal, label='MACD Histogram')
         plt.ylabel('MACD', fontsize=FONT_SIZE)
-    elif indicator == "Forecast":
-        future_dates, forecast, lower_bound, upper_bound = generate_forecast(data)
-        plt.plot(data.index, data['Close'], label='Historical Close Price')
-        plt.plot(future_dates, forecast, label='Forecasted Close Price')
-        plt.fill_between(future_dates, lower_bound, upper_bound, color='gray', alpha=0.3, label='Confidence Interval')
-        plt.ylabel('Price', fontsize=FONT_SIZE)
 
     return plot_to_image(plt, f'{company_name} ({ticker}) {indicator}', market_cap)
 
@@ -137,13 +178,13 @@ def plot_indicators(company_names, indicator_types):
 
 def select_all_indicators(select_all):
     """Select or deselect all indicators based on the select_all flag."""
-    indicators = ["SMA", "MACD", "Forecast"]
+    indicators = ["SMA", "MACD"]
     return indicators if select_all else []
 
 def launch_gradio_app():
     """Launch the Gradio app for interactive plotting."""
     company_choices = list(COMPANY_TICKERS.keys())
-    indicators = ["SMA", "MACD", "Forecast"]
+    indicators = ["SMA", "MACD"]
 
     def fetch_and_plot(company_names, indicator_types):
         images, error_message, total_market_cap = plot_indicators(company_names, indicator_types)