score

app.py

@@ -40,9 +40,10 @@ learn = cnn_learner(paw_dls, models.resnet50, pretrained=False, metrics=error_ra
 learn.to('cpu')
 catanddog = learn.load('dog')
 
-st.title("Hot Dog? Or Not?")
+st.title("PawPularity Score")
 
-file_name = st.file_uploader("Upload a hot dog candidate image")
+file_name = st.file_uploader("Upload a photo of your pet a get an estimate how popular it is gonna be!", type=["jpg"])
+my_bar = st.progress(0, text="PawScore")
 
 if file_name is not None:
     col1, col2 = st.columns(2)
@@ -62,11 +63,16 @@ if file_name is not None:
     if pred == 0:
         learn.load('pure-dog-5')
         pred = learn.predict(image)
-        print("Score for this doggy:", int(pred[2] * 100))
+        col2.header("Score for this doggo:")
+        col2.subheader(f"{ round(float(pred[2]) * 100, 1)}%")
+        my_bar.progress(round(float(pred[2]) * 100), text="PawScore")
+
     if pred == 1:
         learn.load(f'pure-cat-0')
         pred = learn.predict(image)
         print(pred)
         print("Score for this catto:", min(int(pred[2] * 100 + 35), 100))
-    col2.header("Probabilities")
-    col2.subheader(f"{ pred[2] }: { round(float(pred[2]) * 100, 1)}%")
+        col2.header("Score for this catto:")
+        col2.subheader(f"{ round(float(pred[2]) * 100, 1)}%")
+        my_bar.progress(round(float(pred[2]) * 100), text="PawScore")
+