Create app.py

app.py

@@ -0,0 +1,35 @@
+import gradio as gr
+
+
+def image_predict (image_pt):
+    model_path = 'model/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)  
+    print('Prediction from h5 model: {}'.format(h5_prediction))
+    print(h5_prediction)
+    probability = h5_prediction[0]
+    print("H5 Predictions:")
+    status = 'error'
+    probability = 0
+    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) }
+
+
+
+myApp = gr.Interface(fn=image_predict, inputs="image", outputs="label")
+myApp.launch()