Update app.py

app.py

@@ -226,20 +226,27 @@ def build_lstm_model(input_shape):
     return model
 
 # Calculate prediction based of LTSM model learning
-def calculate_prediction_with_ltsm(symbol="BTC", period="5d", interval="5m"): 
+def calculate_prediction_with_ltsm(symbol="BTC", period="5d", interval="5m"):
+    st.write("**Fetched Data...")
     data = get_crypto_data(symbol, period, interval)
     prices = [(pd.to_datetime(index, unit='m'), price) for index, price in data['Close'].items()] 
     df = pd.DataFrame(prices, columns=['Date', 'Price'])
 
+    st.write("**Preparing data for LTSM Model training......")
     X, Y, scaler = prepare_lstm_data(df)
+
+    st.write("**Build LTSM Model with X shape data.........")
     model = build_lstm_model((X.shape[1], 1))
 
     # Train the model
-    print("Model training on the latest data from CoinGecko!")
+    st.write("**Train LTSM Model with X,Y shape data with batch_size(32), epochs(10)............")
     model.fit(X, Y, batch_size=32, epochs=10)
     
     # Predict the next price point
+    st.write("**Sequence LTSM Model with X,Y shape data with batch_size(32), epochs(10)...............")
     last_sequence = X[-1].reshape(1, X.shape[1], 1)
+
+    st.write("**Predict realtime price with  LTSM Model trained with X,Y shape data with batch_size(32), epochs(10)..................")
     scaled_prediction = model.predict(last_sequence)
     predicted_price = scaler.inverse_transform(scaled_prediction)
 
@@ -452,5 +459,18 @@ elif predict_lstm_button:
         interval = "5m"
         predicted_price = calculate_prediction_with_ltsm(symbol, period, interval)
 
-        st.write("### **Final Predicted value by learned LTSM model based on last 5 days with 5 interval of closing data** ###")
-        st.write(f"**Predicted next realtime price: ${predicted_price[0][0]:.10f}**")
+        #st.write("### **Final Predicted value by learned LTSM model based on last 5 days with 5 interval of closing data** ###")
+        #st.write(f"**Predicted next realtime price: ${predicted_price[0][0]:.10f}**")
+
+        st.markdown(
+            """
+            <h3 style="color: #FFD700;">Final Predicted Value by Learned LSTM Model</h3>
+            <p style="font-size: 1.5em; color: #32CD32;">
+                Based on the last 5 days with 5-minute intervals of closing data:
+            </p>
+            <p style="font-size: 2em; font-weight: bold; color: #FF4500;">
+                Predicted Next Realtime Price: ${predicted_price[0][0]:.10f}
+            </p>
+            """, 
+    unsafe_allow_html=True
+)