Update app.py

app.py

@@ -1,14 +1,20 @@
+from sklearn.preprocessing import MinMaxScaler
 import gradio as gr
 import pandas as pd
 import numpy as np
-from pycaret.regression import load_model, predict_model  # Change to regression if needed
+# Change to regression if needed
+from pycaret.regression import load_model, predict_model, setup
+
 
 # Load your dataframe
-df = pd.read_csv("data.csv")  # Replace with your actual data file
+df = pd.read_csv("data.csv", index_col=0)  # Replace with your actual data file
 
-# Identify columns
+clf1 = setup(data=df, target='Mu (kN⋅m)', normalize=False)
+# Use the same scaler that was used during training (important!)
+scaler = MinMaxScaler()
 input_columns = df.columns[:-1].tolist()  # First 4 columns as input
 target_column = df.columns[-1]  # Last column as target
+scaler.fit(df[input_columns])
 
 # Min-Max scaling per column
 min_max_dict = {
@@ -16,15 +22,24 @@ min_max_dict = {
 }
 
 # Load your PyCaret model
-model = load_model("TVAESynthesizer_best")  # Replace with your actual saved model
+# Replace with your actual saved model
+
+
+def pycaret_predict_function(*inputs):
+    # Convert input tuple to DataFrame
+    dfx = pd.DataFrame([inputs], columns=input_columns)
+    dfx_scaled = pd.DataFrame(scaler.transform(dfx), columns=input_columns)
+    breakpoint()
+    print("Input DataFrame for prediction:", dfx_scaled)
+    model = load_model("TVAESynthesizer_best")
+    prediction = predict_model(model, data=dfx_scaled)
+    return prediction["prediction_label"].values[0]
 
-def pycaret_predict_function(inputs):
-    # Convert input list to DataFrame
-    input_data = pd.DataFrame([inputs], columns=input_columns)
+    try:
+        pass
+    except KeyError as e:
+        return 'Error: Prediction label not found. Please check the model output: ' + str(e)
 
-    # Predict using PyCaret model
-    prediction = predict_model(model, data=input_data)
-    return prediction[target_column].values[0]
 
 # Create Gradio inputs dynamically based on scaled range
 input_components = [
@@ -47,7 +62,8 @@ with gr.Blocks() as demo:
 
     predict_btn = gr.Button("Predict")
 
-    predict_btn.click(fn=pycaret_predict_function, inputs=inputs, outputs=output)
+    predict_btn.click(fn=pycaret_predict_function,
+                      inputs=inputs, outputs=output)
 
 if __name__ == "__main__":
     demo.launch()