Update app.py

app.py

@@ -101,26 +101,22 @@ def prediction(path_image):
         ax[1].imshow(image_np,aspect='auto');
 
 
-        return objects
-    images = ["img1.jpg","img2.jpg","img3.jpg","img4.jpg"]
-    with st.sidebar:
-        st.write("choose an image")
-        st.image(images)
-    file = st.file_uploader('Upload an Image',type=(["jpeg","jpg","png"]))
-
-    if file is None:
-        st.write("Please upload an image file")
-    else:
-        image= Image.open(file)
-        st.image(image,use_column_width = True)
+        return fig
+    inputs = gr.inputs.Image(type = 'filepath')
+    EXAMPLES = ["img1.jpg","img2.jpg","img3.jpg","img4.jpg"]
+    DESCRIPTION = """An image is occluded if the image is blocked by any object. 
+    For example if an electric pole is filled with bushes,the image is occluded since it is not clear and blocked.
+    Mobil-net is used to train a model with occluded and non occluded images, so that it can correctly classify the images.
+    Occlusion detection can be used to filter unclear images and take safety measures."""
+    
         
-    image_path = file
+    image_path = inputs
     model_path = "//inference"
     model_PATH_TO_CKPT = "frozen_inference_graph.pb"
     path_to_labels = "tf_label_map.pbtxt"
 
     result = main(image_path,model_path,model_PATH_TO_CKPT,path_to_labels)
-    st.write(result)
+    
     # print("result-",result)
     # list_to_be_sorted= [{'class': 'Y', 'score': 99.97, 'box': (157, 191, 269, 288)}, {'class': '6', 'score': 99.93, 'box': (158, 191, 247, 267)}, {'class': '9', 'score': 99.88, 'box': (156, 190, 179, 196)}, {'class': '4', 'score': 99.8, 'box': (156, 189, 198, 219)}, {'class': '1', 'score': 99.65, 'box': (157, 189, 222, 244)}, {'class': 'F', 'score': 63.4, 'box': (155, 185, 157, 175)}]
     newlist = sorted(result, key=lambda k: k['box'][3],reverse=False)
@@ -140,4 +136,17 @@ def prediction(path_image):
     print("total time : ",round((total_time_end-total_time_start),2))
     st.write(str(simplejson.dumps(response)))
 
-prediction("img1")
+
+
+demo_app = gr.Interface(
+    fn=occ_prediction, 
+    inputs=inputs,
+    outputs= "image",
+    title = "Tag Diciphering",
+    description = DESCRIPTION,
+    examples = EXAMPLES,
+    #cache_example = True,
+    #live = True,
+    theme = 'huggingface'
+)
+demo_app.launch()