Added testing page

.gitignore

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+agent/backend/__pycache__/data.cpython-310.pyc
+agent/__pycache__/__init__.cpython-310.pyc
+agent/backend/__pycache__/data.cpython-310.pyc
+agent/backend/__pycache__/loss.cpython-310.pyc
+agent/backend/__pycache__/models.cpython-310.pyc
+agent/backend/__pycache__/utils.cpython-310.pyc
+agent/dashboard/__pycache__/__init__.cpython-310.pyc
+agent/dashboard/__pycache__/data.cpython-310.pyc
+agent/dashboard/__pycache__/testing.cpython-310.pyc
+agent/dashboard/__pycache__/training.cpython-310.pyc

agent/dashboard/testing.py

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+import solara
+import torch
+import solara.express as solara_px
+from .training import local_state as training_state
+from .data import state as data_state
+from ..backend.utils import predict
+
+
+
+local_state = solara.reactive(
+    {
+        'predictions': solara.reactive({}),
+        'render_count': solara.reactive(0),
+    }
+    )
+
+def force_render():
+    local_state.value['render_count'].set(1 + local_state.value['render_count'].value)
+
+@solara.component
+def ScatterPlot(predictions, render_count):
+    for col in predictions.keys():
+        with solara.Row():
+            solara_px.scatter(
+                predictions[col]['training'],
+                x = 'prediction',
+                y = 'target',
+                title=f'{col}'
+            )
+            
+
+@solara.component
+def Page():
+    df = data_state.value['data']
+
+    filter, set_filter = solara.use_cross_filter(id(df))
+
+    dff = df
+    if filter is not None:
+        dff = df[filter]
+
+    def make_predictions():
+        model = training_state.value['model'].value
+        if model is None:
+            print('There is no pre-trained model! Please train your model.')
+        else:
+            print('There is a pre-trained model')
+            input_cols = training_state.value['input_cols'].value
+            output_cols = training_state.value['output_cols'].value
+            trn_ratio = training_state.value['trn_ratio'].value
+            batch_size_trn = training_state.value['batch_size_trn'].value
+            batch_size_val = training_state.value['batch_size_val'].value
+            seed = training_state.value['seed'].value
+            predictions = predict(model, dff, input_cols, output_cols, trn_ratio, 
+                batch_size_trn, batch_size_val, seed)
+        
+            local_state.value['predictions'].set(predictions)
+            force_render()
+            
+    solara.Button(label='Output Predictions', on_click=make_predictions)
+    ScatterPlot(local_state.value['predictions'].value, local_state.value['render_count'].value)