Update app.py

app.py

@@ -9,10 +9,6 @@ import numpy as np
 
 logging.basicConfig(level=logging.INFO)
 
-########################################
-# OKX endpoints & utility
-########################################
-
 OKX_TICKERS_ENDPOINT = "https://www.okx.com/api/v5/market/tickers?instType=SPOT"
 OKX_CANDLE_ENDPOINT = "https://www.okx.com/api/v5/market/candles"
 
@@ -81,9 +77,6 @@ def create_technical_charts(df):
 
     return fig1, fig2, fig3
 
- ########################################
-# OKX Data Fetching Functions
-########################################
 
 def fetch_okx_symbols():
     """
@@ -202,9 +195,7 @@ def fetch_okx_candles(symbol, timeframe="1H", total=2000):
     logging.info(f"Fetched {len(df_all)} rows for {symbol}.")
     return df_all, ""
 
-########################################
-# Prophet Pipeline
-########################################
+
 
 def prepare_data_for_prophet(df):
     if df.empty:
@@ -273,9 +264,7 @@ def prophet_wrapper(
     future_only = full_forecast.loc[len(df_prophet):, ["ds", "yhat", "yhat_lower", "yhat_upper"]]
     return future_only, ""
 
-########################################
-# Plotting Functions
-########################################
+
 
 def create_forecast_plot(forecast_df):
     if forecast_df.empty:
@@ -324,9 +313,7 @@ def create_forecast_plot(forecast_df):
     )
     return fig
 
-########################################
-# Main Prediction Function
-########################################
+
 
 def predict(
     symbol,
@@ -401,9 +388,14 @@ def main():
     symbols = fetch_okx_symbols()
     
     with gr.Blocks(theme=gr.themes.Base()) as demo:
+        
         with gr.Row():
             gr.Markdown("# CryptoVision")
-            
+
+    gr.HTML("""<a href="https://visitorbadge.io/status?path=https%3A%2F%2Fopenfree-CryptoVision.hf.space">
+               <img src="https://api.visitorbadge.io/api/visitors?path=https%3A%2F%2Fopenfree-CryptoVision.hf.space&countColor=%23263759" />
+               </a>""")
+        
         with gr.Row():
             with gr.Column(scale=1):
                 with gr.Group():