Update app.py

app.py

@@ -27,7 +27,7 @@ from gradcam import GradCAM  # Import your GradCAM class
 
 if "model" not in st.session_state:
     st.session_state.model = tf.keras.models.load_model(
-        "models/best_model.h5"
+        "best_model.h5"
     )
 if "framework"  not in st.session_state:
     st.session_state.framework = "Tensorflow"
@@ -226,7 +226,7 @@ class_labels = ["Cyst", "Normal", "Stone", "Tumor"]
 
 
 def load_tensorflow_model():
-    tf_model = tf.keras.models.load_model("models/best_model.h5")
+    tf_model = tf.keras.models.load_model("best_model.h5")
     return tf_model
 
 if st.session_state.framework =="TensorFlow":
@@ -239,7 +239,7 @@ if st.session_state.framework =="TensorFlow":
         return predictions
 
 if st.session_state.framework == "PyTorch": 
-    logo_path = "images/pytorch.png"
+    logo_path = "pytorch.png"
     bg_color = "#FF5733"  # For example, a warm red/orange
     bg_color_iv = "orange"  # For example, a warm red/orange
 
@@ -311,7 +311,7 @@ def get_layers_data(model, prefix=""):
 
 ###########################################
 main_bg_ext = "png"
-main_bg = "images/bg1.jpg"
+main_bg = "bg1.jpg"
 # Read and encode the logo image
 
 with open(logo_path, "rb") as image_file:
@@ -859,7 +859,7 @@ if page == "Home":
 
     # components.html(html_string)  # JavaScript works
     # st.markdown(html_string, unsafe_allow_html=True)
-    image_path = "images/image.jpg"
+    image_path = "image.jpg"
 
     st.container()
     st.markdown(
@@ -955,7 +955,7 @@ if page == "Home":
         placeholder.empty()
         st.markdown(content, unsafe_allow_html=True)
     else:
-        default_image_path = "images/image.jpg"
+        default_image_path = "image.jpg"
         with open(image_path, "rb") as image_file:
             encoded_image = base64.b64encode(image_file.read()).decode()
 
@@ -974,7 +974,7 @@ if page == "Home":
                 unsafe_allow_html=True,
             )
 if page == "pome":
-    gif_path = "images/bg3.gif"
+    gif_path = "bg3.gif"
     with open(gif_path, "rb") as image_file:
         encode_image = base64.b64encode(image_file.read()).decode()
     st.markdown(
@@ -1015,12 +1015,12 @@ if page == "pome":
 
             if toggle and st.session_state.framework != "PyTorch":
                 st.session_state.framework = "PyTorch"
-                st.session_state.model = torch.load('models/kidney_model .pth', map_location=torch.device('cpu'))
+                st.session_state.model = torch.load('kidney_model .pth', map_location=torch.device('cpu'))
                 st.rerun()
             elif not toggle and st.session_state.framework != "TensorFlow":
                 st.session_state.framework = "TensorFlow"
                 st.session_state.model = tf.keras.models.load_model(
-        "models/best_model.h5"
+        "best_model.h5"
     )
                 st.rerun()
         print(st.session_state.framework)