app

app.py

@@ -8,17 +8,19 @@ model = from_pretrained_keras("keras-io/timeseries_transformer_classification")
 
 def detect_issue(file):
   df = pd.read_csv(file,header=None)
-  pred = model.predict(df)[0]
-  problem = 'No problem'
-  if(np.argmax(pred)==1):
-    problem = 'Engine problem'
-  return problem, pred[1]
+  preds = model.predict(df)
+  result = []
+  for i,pred in enumerate(preds):
+    result.append(['Sample ' + str(i+1), np.argmax(pred),pred[np.argmax(pred)]]
+  return pd.DataFrame(result,header=['Sample','class','confidence']
   
 
 iface = gr.Interface(detect_issue,gr.inputs.File(label="csv file"),
-	outputs=[
-	        gr.outputs.Textbox(label="Engine issue"),
-       		gr.outputs.Textbox(label="Engine issue score")], examples=["sample.csv"]
+	"dataframe",
+	#outputs=[
+	#        gr.outputs.Textbox(label="Engine issue"),
+       	#	gr.outputs.Textbox(label="Engine issue score")], 
+	examples=["sample.csv"]
 #	examples = ["sample.csv"],
 )