Update app.py

app.py

@@ -35,16 +35,6 @@ def load_model():
     detection_model = tf.saved_model.load(saved_model_dir)
     return detection_model
 
-# def load_model2():
-#     wget.download("https://nyp-aicourse.s3-ap-southeast-1.amazonaws.com/pretrained-models/balloon_model.tar.gz")
-#     tarfile.open("balloon_model.tar.gz").extractall()
-#     model_dir = 'saved_model'    
-#     detection_model = tf.saved_model.load(str(model_dir))
-#     return detection_model    
-
-# samples_folder = 'test_samples
-# image_path = 'test_samples/sample_balloon.jpeg
-# 
 
 def predict(pilimg):
 
@@ -116,6 +106,7 @@ def detect_video(video):
     # Release resources
     cap.release()
 
+    
 REPO_ID = "apailang/mytfodmodel"
 detection_model = load_model()
 # pil_image = Image.open(image_path)
@@ -124,21 +115,39 @@ detection_model = load_model()
 # predicted_img = predict(image_arr)
 # predicted_img.save('predicted.jpg')
 
-# gr.Interface(fn=predict,
-#              inputs=gr.Image(type="pil"),
-#              outputs=gr.Image(type="pil"),
-#              title="Image Prediction Interface",
-#             description="Upload a Image for prediction",
-#              ).launch(share=True)
+test1 = os.path.join(os.path.dirname(__file__), "data/test1.jpeg")  
+test2 = os.path.join(os.path.dirname(__file__), "data/test2.jpeg")  
+test3 = os.path.join(os.path.dirname(__file__), "data/test3.jpeg")  
+test4 = os.path.join(os.path.dirname(__file__), "data/test4.jpeg")
+test5 = os.path.join(os.path.dirname(__file__), "data/test5.jpeg")
+test6 = os.path.join(os.path.dirname(__file__), "data/test6.jpeg")
+test7 = os.path.join(os.path.dirname(__file__), "data/test7.jpeg")
+test8 = os.path.join(os.path.dirname(__file__), "data/test8.jpeg")
+test9 = os.path.join(os.path.dirname(__file__), "data/test9.jpeg")
+test10 = os.path.join(os.path.dirname(__file__), "data/test10.jpeg")
+test11 = os.path.join(os.path.dirname(__file__), "data/test11.jpeg")
+test12 = os.path.join(os.path.dirname(__file__), "data/test12.jpeg")
+
+ gr.Interface(fn=predict,
+             inputs=gr.Image(type="pil"),
+              outputs=gr.Image(type="pil"),
+              title="Image Prediction Interface",
+              description="Upload a Image for prediction",
+               examples=[[test1],[test2],[test3],[test4],[test5],[test6],[test7],[test8],[test9],[test10],[test11],[test12],],
+                cache_examples=True
+             ).launch(share=True)
 
 
 a = os.path.join(os.path.dirname(__file__), "data/a.mp4")  # Video
 b = os.path.join(os.path.dirname(__file__), "data/b.mp4")  # Video
 c = os.path.join(os.path.dirname(__file__), "data/c.mp4")  # Video
 
+basename = Path(video_in_file).stem
+video_out_file = os.path.join('data/detected' + '.mp4')
+samples_folder = 'test_samples'
 
 demo = gr.Interface(
-    fn=detect_video,
+    fn=lambda x: x, #detect_video
     inputs=gr.Video(),
     outputs=gr.Video(),
     examples=[
@@ -150,6 +159,4 @@ demo = gr.Interface(
 )
 
 if __name__ == "__main__":
-    demo.launch()
-
-
+    demo.launch()