ss

pages/3_Training the Model.py

@@ -137,53 +137,52 @@ def tuned_parameters(model):
 
 def step3_page():
     st.header("Training the Model")
-    st.subheader("Exploring the data")
-    st.title("Solar Forecasting App")
-
+    st.subheader("Data Collection")
+    st.info("Upload the dataset  which has weather related attributes for model training in .csv format.")
     # Display the image and information in a grid layout
     col1 = st.columns([1])
 
     with col1[0]:
-        data = {
-            'Timestamp': ['11/1/2022 0:20', '11/1/2022 0:25'],
-            'Total_Power (kW)': [37337, 44590],
-            'PV_Output': [296.6, 298.4],
-            'Solar_Irradiance': [0, 0],
-            'Temperature': [25.1, 24.7],
-            'Rain_Fall': [42.6, 42.6],
-            'Wind_Speed': [0.6, 0.4]
-        }
-        df = pd.DataFrame(data)
-        st.subheader("Example of CSV file DataFrame")
-        st.table(df)
-
-    csv_file = st.sidebar.file_uploader("Upload CSV", type=['csv'])
-
-    if csv_file is not None:
-        data = process_file(csv_file)
-
-        train_size = st.sidebar.slider("Select Train Dataset Size (%)", min_value=10, max_value=90, value=70)
-
-        models = ['LightGBM', 'Random Forest']
-        model_choice = st.sidebar.selectbox('Choose Model', models)
-
-        tune_model = st.sidebar.checkbox('Tune Hyperparameters')
-
-        y_test, y_pred, model = model_predict(data, model_choice, train_size, tune_model)
-
-        # Display feature importance
-        if st.sidebar.checkbox('Show feature importance'):
-            feature_names = ['Solar_Irradiance', 'Temperature', 'Rain_Fall', 'Wind_speed', 'PV_Output_lag',
-                             'PV_Output_mean']
-            fig = feature_importance_plot(model, feature_names)
-            with _lock:
-                st.pyplot(fig)
-
-        fig = show_output(y_test, y_pred)
-
-        download_link(y_test, y_pred)
-
-        download_plot(fig)
+        # data = {
+        #     'Timestamp': ['11/1/2022 0:20', '11/1/2022 0:25'],
+        #     'Total_Power (kW)': [37337, 44590],
+        #     'PV_Output': [296.6, 298.4],
+        #     'Solar_Irradiance': [0, 0],
+        #     'Temperature': [25.1, 24.7],
+        #     'Rain_Fall': [42.6, 42.6],
+        #     'Wind_Speed': [0.6, 0.4]
+        # }
+        csv_file = st.file_uploader("Upload CSV", type=['csv'])
+        if csv_file is not None:
+            data = process_file(csv_file)
+            df = pd.DataFrame(data)
+            st.subheader("Example of CSV file DataFrame")
+            st.dataframe(df)
+
+            train_size = st.sidebar.slider("Select Train Dataset Size (%)", min_value=10, max_value=90, value=70)
+
+            models = ['LightGBM', 'Random Forest']
+            model_choice = st.sidebar.selectbox('Choose Model', models)
+
+            tune_model = st.sidebar.checkbox('Tune Hyperparameters')
+
+            y_test, y_pred, model = model_predict(data, model_choice, train_size, tune_model)
+
+            # Display feature importance
+            if st.sidebar.checkbox('Show feature importance'):
+                feature_names = ['Solar_Irradiance', 'Temperature', 'Rain_Fall', 'Wind_speed', 'PV_Output_lag',
+                                 'PV_Output_mean']
+                fig = feature_importance_plot(model, feature_names)
+                with _lock:
+                    st.pyplot(fig)
+
+            fig = show_output(y_test, y_pred)
+
+            download_link(y_test, y_pred)
+
+            download_plot(fig)
+        else:
+            st.error("Please upload a valid .csv file")
 
     if st.button("Complete"):
         complete_level(LEVEL)