Code changes

app.py

@@ -0,0 +1,67 @@
+import evaluate
+from evaluate.utils import launch_gradio_widget
+import gradio as gr
+# 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/molgenevalmetric")
+# launch_gradio_widget(module)
+
+iface = gr.Interface(
+    fn = module,
+    inputs=[
+        gr.File(label="Generated SMILES"), 
+        gr.File(label="Training Data", value=None),
+        ],
+    outputs="text"
+)
+
+iface.launch()

requirements.txt

@@ -0,0 +1,6 @@
+evaluate
+rdkit
+molsets
+pandas==1.5.3
+gradio==3.43.1
+PyTDC