Use a single model

app.py

@@ -142,20 +142,20 @@ def predict_fn(input_img):
     hair_result_probabilty = float(hair_probabilities[0][1].round(3))
     old_result_probabilty = float(old_lr_probabilities[0][1].round(3))
     # get decision string
-    if result_probabilty > 0.85:
-        if hair_result_probabilty < 0.24:
-            logger.info("hair_result_probabilty < 0.5")
-            result_probabilty = hair_result_probabilty
-            decision = "AUTO REJECT"
-        else:
-            decision = "AUTO ACCEPT"
-    elif result_probabilty < 0.24:
+    if result_probabilty > 0.9:
+        # if hair_result_probabilty < 0.24:
+        #     logger.info("hair_result_probabilty < 0.5")
+        #     result_probabilty = hair_result_probabilty
+        #     decision = "AUTO REJECT"
+        # else:
+        decision = "AUTO ACCEPT"
+    elif result_probabilty < 0.2:
         logger.info("result_probabilty < 0.2")
         decision = "AUTO REJECT"
-    elif old_result_probabilty < 0.06:
-        logger.info("old_result_probabilty < 0.06")
-        result_probabilty = old_result_probabilty
-        decision = "AUTO REJECT"
+    # elif old_result_probabilty < 0.06:
+    #     logger.info("old_result_probabilty < 0.06")
+    #     result_probabilty = old_result_probabilty
+    #     decision = "AUTO REJECT"
     else:
         decision = "MODERATION"
     logger.info(f"decision: {decision}")
@@ -172,8 +172,8 @@ iface = gr.Interface(
     outputs="text",
     description="""
     The model returns the probability of the image being a base body. If
-    probability > 0.85, the image can be automatically tagged as a base body. If
-    probability < 0.25, the image can be automatically REJECTED as NOT as base
+    probability > 0.9, the image can be automatically tagged as a base body. If
+    probability < 0.2, the image can be automatically REJECTED as NOT as base
     body. All other cases will be submitted for moderation.
 
     Please flag if you think the decision is wrong.