Update app.py

app.py

@@ -1,21 +1,16 @@
 import numpy
 import gradio
-from huggingface_hub import from_pretrained_keras
+import huggingface_hub
 import json
-from json import JSONEncoder
 
-class NumpyArrayEncoder(JSONEncoder):
+class NumpyArrayEncoder(json.JSONEncoder):
     def default(self, obj):
         if isinstance(obj, numpy.ndarray):
             return obj.tolist()
         return JSONEncoder.default(self, obj)
 
-def print_shape_through(x):
-    print(x.shape)
-    return x
-
-analysis_network = from_pretrained_keras("cmudrc/wave-energy-analysis")
-synthesis_network = from_pretrained_keras("cmudrc/wave-energy-synthesis")
+analysis_network = huggingface_hub.from_pretrained_keras("cmudrc/wave-energy-analysis")
+synthesis_network = huggingface_hub.from_pretrained_keras("cmudrc/wave-energy-synthesis")
    
 with gradio.Blocks() as demo:
     geometry = gradio.Textbox(label="geometry")     
@@ -24,7 +19,7 @@ with gradio.Blocks() as demo:
     analyze_it = gradio.Button("Analyze")
     synthesize_it = gradio.Button("Synthesize")
 
-    analyze_it.click(fn=lambda x: json.dumps(analysis_network.predict(print_shape_through(numpy.asarray(json.loads(x)))), cls=NumpyArrayEncoder), inputs=[geometry], outputs=[spectrum], api_name="analyze")
-    synthesize_it.click(fn=lambda x: json.dumps(synthesis_network.predict(print_shape_through(numpy.asarray(json.loads(x)))), cls=NumpyArrayEncoder), inputs=[spectrum], outputs=[geometry], api_name="synthesize")
+    analyze_it.click(fn=lambda x: json.dumps(analysis_network.predict(numpy.asarray([json.loads(x)])), cls=NumpyArrayEncoder), inputs=[geometry], outputs=[spectrum], api_name="analyze")
+    synthesize_it.click(fn=lambda x: json.dumps(synthesis_network.predict(numpy.asarray(json.loads(x))), cls=NumpyArrayEncoder), inputs=[spectrum], outputs=[geometry], api_name="synthesize")
     
-demo.launch()
+demo.launch(debug=True)