Update app.py

app.py

@@ -1,8 +1,8 @@
 import streamlit as st
 import pandas as pd
-from pycaret.classification import *
-from pycaret.regression import *
-from pycaret.clustering import *
+from pycaret.classification import setup as classification_setup, compare_models as compare_classification_models, evaluate_model as evaluate_classification_model, save_model as save_classification_model, plot_model as plot_classification_model
+from pycaret.regression import setup as regression_setup, compare_models as compare_regression_models, evaluate_model as evaluate_regression_model, save_model as save_regression_model, plot_model as plot_regression_model
+from pycaret.clustering import setup as clustering_setup, compare_models as compare_clustering_models, evaluate_model as evaluate_clustering_model, save_model as save_clustering_model, plot_model as plot_clustering_model
 from ydata_profiling import ProfileReport
 from streamlit_pandas_profiling import st_profile_report
 import os
@@ -27,7 +27,7 @@ if app_mode == "Data Upload":
     uploaded_file = st.file_uploader("Upload CSV Dataset", type=["csv"])
     if uploaded_file:
         df = pd.read_csv(uploaded_file)
-        st.session_state.df = df
+        st.session_state['df'] = df
         st.write("---")
         st.subheader("Dataset Preview")
         st.dataframe(df.head(10))
@@ -52,48 +52,58 @@ elif app_mode == "Model Training":
         st.warning("Please upload a dataset first.")
         st.stop()
 
-    df = st.session_state.df
+    df = st.session_state['df']
     problem_type = st.selectbox("Problem Type", ["Classification", "Regression", "Clustering"])
     target = st.selectbox("Select Target Column", df.columns) if problem_type != "Clustering" else None
 
     if st.button("Setup PyCaret"):
         with st.spinner("Setting up PyCaret..."):
             if problem_type == "Classification":
-                setup(data=df, target=target, session_id=123, verbose=False)
-                st.session_state.problem_type = "Classification"
-                st.session_state.setup_complete = True
+                classification_setup(data=df, target=target, session_id=123, verbose=False)
+                st.session_state['problem_type'] = "Classification"
+                st.session_state['setup_complete'] = True
             elif problem_type == "Regression":
-                setup(data=df, target=target, session_id=123, verbose=False)
-                st.session_state.problem_type = "Regression"
-                st.session_state.setup_complete = True
+                regression_setup(data=df, target=target, session_id=123, verbose=False)
+                st.session_state['problem_type'] = "Regression"
+                st.session_state['setup_complete'] = True
             elif problem_type == "Clustering":
-                setup(data=df, session_id=123, verbose=False)
-                st.session_state.problem_type = "Clustering"
-                st.session_state.setup_complete = True
+                clustering_setup(data=df, session_id=123, verbose=False)
+                st.session_state['problem_type'] = "Clustering"
+                st.session_state['setup_complete'] = True
             st.success("PyCaret setup complete! You can now train models.")
 
-    if st.session_state.get("setup_complete", False):
+    if st.session_state.get('setup_complete', False):
         st.subheader("Train Models")
         if st.button("Compare Models"):
             with st.spinner("Comparing models..."):
-                best_model = compare_models()
-                st.session_state.best_model = best_model
+                if st.session_state['problem_type'] == "Classification":
+                    best_model = compare_classification_models()
+                elif st.session_state['problem_type'] == "Regression":
+                    best_model = compare_regression_models()
+                elif st.session_state['problem_type'] == "Clustering":
+                    best_model = compare_clustering_models()
+                st.session_state['best_model'] = best_model
                 st.success(f"Best Model: {best_model}")
 
         if 'best_model' in st.session_state:
             st.subheader("Model Evaluation")
             if st.button("Evaluate Model"):
                 with st.spinner("Evaluating model..."):
-                    if st.session_state.problem_type == "Classification":
-                        evaluate_model(st.session_state.best_model)
-                    elif st.session_state.problem_type == "Regression":
-                        evaluate_model(st.session_state.best_model)
-                    elif st.session_state.problem_type == "Clustering":
-                        evaluate_model(st.session_state.best_model)
+                    if st.session_state['problem_type'] == "Classification":
+                        evaluate_classification_model(st.session_state['best_model'])
+                    elif st.session_state['problem_type'] == "Regression":
+                        evaluate_regression_model(st.session_state['best_model'])
+                    elif st.session_state['problem_type'] == "Clustering":
+                        evaluate_clustering_model(st.session_state['best_model'])
                     st.success("Model evaluation complete!")
 
             if st.button("Save Model"):
-                save_model(st.session_state.best_model, "best_model")
+                if st.session_state['problem_type'] == "Classification":
+                    save_classification_model(st.session_state['best_model'], "best_model")
+                elif st.session_state['problem_type'] == "Regression":
+                    save_regression_model(st.session_state['best_model'], "best_model")
+                elif st.session_state['problem_type'] == "Clustering":
+                    save_clustering_model(st.session_state['best_model'], "best_model")
                 st.success("Model saved as `best_model.pkl`!")
                 with open("best_model.pkl", "rb") as f:
                     st.download_button("Download Model", f, file_name="best_model.pkl")
@@ -105,17 +115,17 @@ elif app_mode == "Validation & Exploration":
         st.stop()
 
     st.subheader("Model Performance")
-    if st.session_state.problem_type == "Classification":
+    if st.session_state['problem_type'] == "Classification":
         st.write("Classification Report:")
-        plot_model(st.session_state.best_model, plot="confusion_matrix", display_format="streamlit")
-        plot_model(st.session_state.best_model, plot="auc", display_format="streamlit")
-    elif st.session_state.problem_type == "Regression":
+        plot_classification_model(st.session_state['best_model'], plot="confusion_matrix", display_format="streamlit")
+        plot_classification_model(st.session_state['best_model'], plot="auc", display_format="streamlit")
+    elif st.session_state['problem_type'] == "Regression":
         st.write("Regression Metrics:")
-        plot_model(st.session_state.best_model, plot="residuals", display_format="streamlit")
-        plot_model(st.session_state.best_model, plot="error", display_format="streamlit")
-    elif st.session_state.problem_type == "Clustering":
+        plot_regression_model(st.session_state['best_model'], plot="residuals", display_format="streamlit")
+        plot_regression_model(st.session_state['best_model'], plot="error", display_format="streamlit")
+    elif st.session_state['problem_type'] == "Clustering":
         st.write("Clustering Results:")
-        plot_model(st.session_state.best_model, plot="cluster", display_format="streamlit")
+        plot_clustering_model(st.session_state['best_model'], plot="cluster", display_format="streamlit")
 
 # Custom CSS
 st.markdown("""