Update app.py

app.py

@@ -57,20 +57,20 @@ if file is not None:
     img_reshaped = image_resize(df)
     
     # Get prediction
-    pred = model.predict(img_reshaped[0])
+    pred = model.predict(img_reshaped)
     label = np.argmax(pred)
 
-    label_map = {4: ('nv', ' melanocytic nevi'), 
-             6: ('mel', 'melanoma'),
-             2: ('bkl', 'benign keratosis-like lesions'),
-             1: ('bcc' , ' basal cell carcinoma'),
-             5: ('vasc', 'pyogenic granulomas and hemorrhage'),  
-             0: ('akiec', 'Actinic keratoses and intraepithelial carcinomae'),
-             3: ('df', 'dermatofibroma')}
-
-    if label in label_map:
-        label_name = label_map[label][0]
-        full_name = label_map[label][1]
+    # label_map = {4: ('nv', ' melanocytic nevi'), 
+    #          6: ('mel', 'melanoma'),
+    #          2: ('bkl', 'benign keratosis-like lesions'),
+    #          1: ('bcc' , ' basal cell carcinoma'),
+    #          5: ('vasc', 'pyogenic granulomas and hemorrhage'),  
+    #          0: ('akiec', 'Actinic keratoses and intraepithelial carcinomae'),
+    #          3: ('df', 'dermatofibroma')}
+
+    # if label in label_map:
+    #     label_name = label_map[label][0]
+    #     full_name = label_map[label][1]
     
     # Display image and result 
     col1, col2 = st.columns(2)
@@ -79,7 +79,8 @@ if file is not None:
         # st.image(image)
     with col2:
         st.header("Prediction")
-        st.metric("Digit", full_name)
+        st.write(label)
+        # st.metric("Digit", full_name)
 
 
 # import streamlit as st