missing fix gradcam without file

app.py

@@ -106,24 +106,6 @@ def load_model():
         cfg=cfg
     )
 
-
-
-
-def compute_similarities(features, database):
-    similarities = dict()
-    dist_fn = getattr(spatial.distance, DISTANCE)
-    for file_name, elems in  database.items():
-        for elem in elems:
-            similarities[file_name] = dict(
-                dist=dist_fn(elem["features"], features),
-                file_name=file_name,
-                box=elem["roi"],
-                type=elem["type"]
-            )
-    similarities = OrderedDict(sorted(similarities.items(), key=lambda e: e[1]["dist"]))
-    return similarities
-
-
 def draw_box(file_name, box, type, model, resize_input=False):
     height, width, channels = img.shape 
     
@@ -225,15 +207,24 @@ MODEL = "./models/model.pth"
 PCA_MODEL = "./models/pca.pkl"
 FEATURES_DATABASE = "./assets/features/features.json"
 
-DISTANCE = "cosine"
-TH = 0.5
+st.header("Explainable oral lesion detection")
+st.markdown("""Demo for the paper [Explainable diagnosis of oral cancer via deep learning and case-based reasoning](https://mlpi.ing.unipi.it/doctoralai/)
+
+Upload an image using the form below and click on "Process"
+""")
+FILE = st.file_uploader("Image", type=["jpg", "jpeg", "png"])
+TH = st.slider("Threshold", min_value=0.0, max_value=1.0, value=0.5)
+
+process = st.button("Process")
 
 state = st.empty()
-tooltip = st.empty()
 
-state.write("Loading model...")
-model = load_model()
+if process:
+    state.write("Loading model...")
+    model = load_model()
 
-img = cv2.imread(FILE)
-img = cv2.resize(img, (800, 800))
-explain(img, model)
+    nparr = np.fromstring(FILE.getvalue(), np.uint8)
+    img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+    #img = cv2.imread(FILE)
+    img = cv2.resize(img, (800, 800))
+    explain(img, model)