compute change

app.py

@@ -1,55 +1,55 @@
-import evaluate
-from evaluate.utils import launch_gradio_widget
-from pathlib import Path
-import sys
-import os
-
-from .logging import get_logger
-logger = get_logger(__name__)
-
-###
-def launch_gradio_widget(metric):
-    """Launches `metric` widget with Gradio."""
-
-    try:
-        import gradio as gr
-    except ImportError as error:
-        logger.error("To create a metric widget with Gradio make sure gradio is installed.")
-        raise error
-
-    local_path = Path(sys.path[0])
-    # if there are several input types, use first as default.
-    if isinstance(metric.features, list):
-        (feature_names, feature_types) = zip(*metric.features[0].items())
-    else:
-        (feature_names, feature_types) = zip(*metric.features.items())
-    gradio_input_types = infer_gradio_input_types(feature_types)
-
-    def compute(data):
-        return metric.compute(**parse_gradio_data(data, gradio_input_types))
-
-    iface = gr.Interface(
-        fn=compute,
-        inputs=gr.Dataframe(
-            headers=feature_names,
-            col_count=len(feature_names),
-            row_count=1,
-            datatype=json_to_string_type(gradio_input_types),
-        ),
-        outputs=gr.Textbox(label=metric.name),
-        description=(
-            metric.info.description + "\nIf this is a text-based metric, make sure to wrap you input in double quotes."
-            " Alternatively you can use a JSON-formatted list as input."
-        ),
-        title=f"Metric: {metric.name}",
-        article=parse_readme(local_path / "README.md"),
-        # TODO: load test cases and use them to populate examples
-        # examples=[parse_test_cases(test_cases, feature_names, gradio_input_types)]
-    )
-
-    iface.launch()
-###
-
-
-module = evaluate.load("saicharan2804/my_metric")
-launch_gradio_widget(module)
+# import evaluate
+# from evaluate.utils import launch_gradio_widget
+# from pathlib import Path
+# import sys
+# import os
+
+# from .logging import get_logger
+# logger = get_logger(__name__)
+
+# ###
+# def launch_gradio_widget(metric):
+#     """Launches `metric` widget with Gradio."""
+
+#     try:
+#         import gradio as gr
+#     except ImportError as error:
+#         logger.error("To create a metric widget with Gradio make sure gradio is installed.")
+#         raise error
+
+#     local_path = Path(sys.path[0])
+#     # if there are several input types, use first as default.
+#     if isinstance(metric.features, list):
+#         (feature_names, feature_types) = zip(*metric.features[0].items())
+#     else:
+#         (feature_names, feature_types) = zip(*metric.features.items())
+#     gradio_input_types = infer_gradio_input_types(feature_types)
+
+#     def compute(data):
+#         return metric.compute(**parse_gradio_data(data, gradio_input_types))
+
+#     iface = gr.Interface(
+#         fn=compute,
+#         inputs=gr.Dataframe(
+#             headers=feature_names,
+#             col_count=len(feature_names),
+#             row_count=1,
+#             datatype=json_to_string_type(gradio_input_types),
+#         ),
+#         outputs=gr.Textbox(label=metric.name),
+#         description=(
+#             metric.info.description + "\nIf this is a text-based metric, make sure to wrap you input in double quotes."
+#             " Alternatively you can use a JSON-formatted list as input."
+#         ),
+#         title=f"Metric: {metric.name}",
+#         article=parse_readme(local_path / "README.md"),
+#         # TODO: load test cases and use them to populate examples
+#         # examples=[parse_test_cases(test_cases, feature_names, gradio_input_types)]
+#     )
+
+#     iface.launch()
+# ###
+
+
+# module = evaluate.load("saicharan2804/my_metric")
+# launch_gradio_widget(module)

my_metric.py

@@ -6,8 +6,8 @@ import pandas as pd
 
 def _compute(self, list_of_generated_smiles):
     test_set = moses.get_dataset('test')
-    preprocessed_smiles = [smile for smile in list_of_generated_smiles if moses.utils.canonicalize_smiles(smile)]
-
+    # preprocessed_smiles = [smile for smile in list_of_generated_smiles if moses.utils.canonicalize_smiles(smile)]
+    preprocessed_smiles = list_of_generated_smiles
     results = metrics.get_all_metrics(preprocessed_smiles, test_set)
 
     return results