Update app.py

app.py

@@ -1,24 +1,26 @@
 import numpy
 import gradio
 from huggingface_hub import from_pretrained_keras
+import json
+from json import JSONEncoder
 
-S = 5
-N = 1000
-D = 3
-F = 64
-G = 32
+class NumpyArrayEncoder(JSONEncoder):
+    def default(self, obj):
+        if isinstance(obj, numpy.ndarray):
+            return obj.tolist()
+        return JSONEncoder.default(self, obj)
 
 analysis_network = from_pretrained_keras("cmudrc/wave-energy-analysis")
 synthesis_network = from_pretrained_keras("cmudrc/wave-energy-synthesis")
    
 with gradio.Blocks() as demo:
-    geometry = gradio.Textbox(label="geometry")     
-    spectrum = gradio.Textbox(label="spectrum")
+    geometry = gradio.JSON(label="geometry")     
+    spectrum = gradio.JSON(label="spectrum")
 
     analyze_it = gradio.Button("Analyze")
     synthesize_it = gradio.Button("Synthesize")
 
-    analyze_it.click(fn=lambda x: analysis_network.predict(numpy.fromstring(x, dtype=float)).tostring(), inputs=[geometry], outputs=[spectrum], api_name="analyze")
-    synthesize_it.click(fn=lambda x: synthesis_network.predict(numpy.fromstring(x, dtype=int)).tostring(), 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))), inputs=[spectrum], outputs=[geometry], api_name="synthesize")
     
 demo.launch()