Update app.py

app.py

@@ -131,25 +131,25 @@ def predict_beetle(img):
                         image_edge_buffer=50)
     image_lst = pre_process.col_image_lst
     print("Objects detected")
-    # get predictions for all segments
-    conf_dict_lst = []
-    output_lst = []
-    img_cnt = len(image_lst)
-    for i in range(0,img_cnt):
-        prob_ar = np.array(bc_model.predict(image_lst[i])[2])
-        print(f"Beetle classified - {i}")
-        unkown_prob = unkown_prob_calc(probs=prob_ar, wedge_threshold=0.85, wedge_magnitude=5, wedge='dynamic')
-        prob_ar = np.append(prob_ar, unkown_prob)
-        prob_ar = np.around(prob_ar*100, decimals=1)
-        # only show the top 5 predictions
-        # Sorting the dictionary by value in descending order and taking the top items
-        top_num = 3
-        conf_dict = {labels[i]: float(prob_ar[i]) for i in range(len(prob_ar))}
-        conf_dict = dict(sorted(conf_dict.items(), key=lambda item: item[1], reverse=True)[:top_num])
-        conf_dict_lst.append(str(conf_dict)[1:-1]) # remove dictionary brackets
-        result = list(zip(image_lst, conf_dict_lst))
-        print(f"Classification processed - {i}")
-    return(result)
+    # # get predictions for all segments
+    # conf_dict_lst = []
+    # output_lst = []
+    # img_cnt = len(image_lst)
+    # for i in range(0,img_cnt):
+    #     prob_ar = np.array(bc_model.predict(image_lst[i])[2])
+    #     print(f"Beetle classified - {i}")
+    #     unkown_prob = unkown_prob_calc(probs=prob_ar, wedge_threshold=0.85, wedge_magnitude=5, wedge='dynamic')
+    #     prob_ar = np.append(prob_ar, unkown_prob)
+    #     prob_ar = np.around(prob_ar*100, decimals=1)
+    #     # only show the top 5 predictions
+    #     # Sorting the dictionary by value in descending order and taking the top items
+    #     top_num = 3
+    #     conf_dict = {labels[i]: float(prob_ar[i]) for i in range(len(prob_ar))}
+    #     conf_dict = dict(sorted(conf_dict.items(), key=lambda item: item[1], reverse=True)[:top_num])
+    #     conf_dict_lst.append(str(conf_dict)[1:-1]) # remove dictionary brackets
+    #     result = list(zip(image_lst, conf_dict_lst))
+    #     print(f"Classification processed - {i}")
+    return(image_lst)
 
 
 # gradio app