Update app.py

app.py

@@ -1,18 +1,13 @@
+import os
 import gradio as gr
 import numpy as np
 from tensorflow.keras.models import load_model
 import cv2
 
 
-def image_predict (image_pt):
-    #model_path = 'model/resnet_ct.h5'
-    model_path = 'resnet_ct.h5'
+def image_predict (image):
+    model_path = 'resnet_ct.h5'  
     h5_model = load_model(model_path)
-
-    #OLD IMAGE
-    image = cv2.imread(image_pt)
-    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-    image = cv2.resize(image, (224, 224))
     image = np.array(image) / 255
     image = np.expand_dims(image, axis=0)
     h5_prediction = h5_model.predict(image)  
@@ -20,20 +15,16 @@ def image_predict (image_pt):
     print(h5_prediction)
     probability = h5_prediction[0]
     print("H5 Predictions:")
-    status = 'error'
-    probability = 0
+    print (probability)
     if probability[0] > 0.5:
         covid_chest_pred = str('%.2f' % (probability[0] * 100) + '% COVID-Positive')
-        status = 'Covid-Positive'
         probability = (probability[0] * 100)
     else:
         covid_chest_pred = str('%.2f' % ((1 - probability[0]) * 100) + '% COVID-Negative')
-        status = 'Covid-Negative'
         probability = ((1 - probability[0]) * 100)
-    print(covid_chest_pred)
-    return {'status': str(status), 'probability': str(probability) }
+    return  covid_chest_pred
 
 
 
-myApp = gr.Interface(fn=image_predict, inputs="image", outputs="label")
+myApp = gr.Interface(fn=image_predict, inputs="image", outputs="text")
 myApp.launch()#share=True