Update app.py

app.py

@@ -1,6 +1,48 @@
+from evaluate.utils import launch_gradio_widget,parse_readme
 import evaluate
-from evaluate.utils import launch_gradio_widget
+import os
+# os.environ['http_proxy']='http://localhost:7890'
+# os.environ['https_proxy']='http://localhost:7890'
+import gradio as gr
+from evaluate.utils.gradio import infer_gradio_input_types,json_to_string_type,parse_gradio_data
+from pathlib import Path
+from datasets import Value
+import sys
 
+def cal_oppo_refuse_match(predictions):
+    refuse=evaluate.load('libraxiong/oppo_refuse_match')
+    return refuse.compute(predictions=predictions)
 
-module = evaluate.load("libraxiong/oppo_refuse_match")
-launch_gradio_widget(module)
+def launch_gradio_widget(metric):
+    """Launches `metric` widget with Gradio."""
+    # del os.environ['http_proxy']
+    # del os.environ['https_proxy']
+    # os.environ['no_proxy']='localhost, 127.0.0.1, ::1'
+    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])
+    def compute(data):
+        print(data)
+        data=eval(data)
+        return metric.compute(predictions=data)
+
+    iface = gr.Interface(
+        fn=compute,
+        inputs=gr.Textbox(label=metric.name),
+        outputs=gr.Textbox(label=metric.name),
+        description=(
+            metric.info.description + "\nIf this is a text-based metric. Just input the predictions,the format is ['some','some']"
+        ),
+        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()
+if __name__=='__main__':
+    launch_gradio_widget(evaluate.load('libraxiong/oppo_refuse_match'))