Update app.py

app.py

@@ -42,6 +42,14 @@ def normalize_2d(matrix):
     return matrix
 
 # Create predict function
+
+# Detect the image
+
+def detect(imagepath):
+
+    pix=model.predict(imagepath, confidence=40, overlap=30)
+    return pix
+    
 def predict(img):
 
     start_time = timer()
@@ -68,7 +76,7 @@ def predict(img):
     pred_time = round(timer() - start_time, 5)
     
     # Return the prediction dictionary and prediction time 
-    return pred_labels_and_probs, pred_time,pix1
+    return pred_labels_and_probs, pred_time
 
 ### 4. Gradio app ###
 
@@ -89,16 +97,24 @@ outputs_image = [
 ]
 
 # Create the Gradio demo
-demo = gr.Interface(fn=predict, # mapping function from input to output
-                    inputs=inputs_image, # what are the inputs?
+app1 = gr.Interface(fn=predict, # mapping function from input to output
+                    inputs=gr.Image(type="pil"), # what are the inputs?
                     outputs=[gr.Label(num_top_classes=2, label="Predictions"), # what are the outputs?
-                             gr.Number(label="Prediction time (s)"),
-                             outputs_image], # our fn has two outputs, therefore we have two outputs
+                             gr.Number(label="Prediction time (s)")
+                            ], # our fn has two outputs, therefore we have two outputs
                     # Create examples list from "examples/" directory
                     examples=example_list, 
                     title=title,
                     description=description,
                     article=article)
 
+app2=gr.Interface(fn=detect,
+                 inputs=inputs_image,
+                 outputs=outputs_image,
+                 title=title)
+
+demo = gr.TabbedInterface([app1, app2], ["Classify", "Detect"])
+
+
 # Launch the demo!
 demo.launch()