Update app.py

app.py

@@ -157,33 +157,63 @@ def predict_volatility(date):
 
 
 ################### GRADIO INTERFACE
+import gradio as gr
+import pandas as pd
+
+################### HELPER FUNCTION TO RETURN TABLE ###################
+def predict_volatility_for_table(date):
+    """
+    Returns a DataFrame with Nowcast and Forecast predictions for the given date.
+    """
+    # Run your existing predict_volatility function
+    point, low, high, for_point, for_low, for_high = predict_volatility(date)
+
+    # Create a DataFrame to display nicely
+    data = {
+        "Type": ["Nowcast (t)", "Forecast (t+1)"],
+        "Volatility": [point, for_point],
+        "Low 95% CI": [low, for_low],
+        "High 95% CI": [high, for_high]
+    }
+
+    df = pd.DataFrame(data)
+
+    # Round values for better readability
+    df[["Volatility", "Low 95% CI", "High 95% CI"]] = df[["Volatility", "Low 95% CI", "High 95% CI"]].round(4)
+
+    return df
+
+################### GRADIO WRAPPER ###################
 def gradio_predict(date):
+    """
+    Wrapper for Gradio. Returns a DataFrame for display.
+    """
+    # Validate date format
     try:
         pd.to_datetime(date)
     except:
-        return "❌ Invalid date format. Please use YYYY-MM-DD."
+        return pd.DataFrame({"Error": ["❌ Invalid date format. Use YYYY-MM-DD."]})
 
+    # Attempt to predict
     try:
-        point, low, high, for_point, for_low, for_high = predict_volatility(date)
-
-        return (
-            f"NOWCAST (t)\n"
-            f"Volatility: {point:.4f}\n"
-            f"95% CI: [{low:.4f}, {high:.4f}]\n\n"
-            f"FORECAST (t+1)\n"
-            f"Volatility: {for_point:.4f}\n"
-            f"95% CI: [{for_low:.4f}, {for_high:.4f}]"
-        )
+        df = predict_volatility_for_table(date)
+        return df
     except Exception as e:
-        return f"⚠️ Error while computing prediction:\n{str(e)}"
+        return pd.DataFrame({"Error": [f"⚠️ Error while computing prediction:\n{str(e)}"]})
 
+################### GRADIO INTERFACE ###################
 demo = gr.Interface(
     fn=gradio_predict,
     inputs=gr.Textbox(label="Date (YYYY-MM-DD)"),
-    outputs=gr.Textbox(label="Prediction"),
+    outputs=gr.Dataframe(label="Volatility Predictions", headers=["Type", "Volatility", "Low 95% CI", "High 95% CI"]),
     title="Crypto Volatility Predictor",
-    description="Enter a date to get predicted volatility and 95% confidence interval."
+    description=(
+        "Enter a date to get predicted BTC volatility and 95% confidence intervals.\n"
+        "Nowcast = today's volatility, Forecast = next day's volatility."
+    ),
+    allow_flagging="never"
 )
 
 if __name__ == "__main__":
     demo.launch()
+