Update app.py

app.py

@@ -13,48 +13,47 @@ X = Y = 224
 import os
 os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
 
-def preprocess_image(uploaded_image, predicted_label):
-    # Read image bytes from uploaded file
-    file_bytes = np.asarray(bytearray(uploaded_image.read()), dtype=np.uint8)
-    img = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
-    
-    if img is None:
-        raise ValueError("Failed to load the uploaded image.")
-
-    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+def preprocess_image(image, predicted_label):
+    img= cv2.imread(image.name)
+    img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
     img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
     thresh = threshold_otsu(img_gray)
     img_otsu = img_gray < thresh
     total_area = img_otsu.size
     black_area = np.count_nonzero(img_otsu == 0)
     white_area = np.count_nonzero(img_otsu == 1)
-
+    #print(total_area)
+    #print(white_area)
+    #print(black_area)
     if predicted_label != 'lung_n':
         if white_area >= 300000:
             level = 3
         elif 200000 <= white_area < 3000000:
             level = 2
-        elif 100000 <= white_area < 200000:
+        elif  100000 <= white_area < 200000:
             level = 1
         elif white_area < 100000:
             level = 0
 
         if level == 3:
-            st.error("Cancer type: " + predicted_label)
+            st.error("Cancer type: "+ predicted_label )
             st.error("Level of Cancer: 3")
-        elif level == 2:
-            st.warning("Cancer type: " + predicted_label)
+            #st.error("Please consult a doctor immediately")
+        elif level == 2: 
+            st.warning("Cancer type:  "+ predicted_label )
             st.warning("Level of Cancer: 2")
+
         elif level == 1:
-            st.info("Cancer type: " + predicted_label)
+            st.info("Cancer type: "+ predicted_label)
             st.info("Level of Cancer: 1")
         elif level == 0:
-            st.success("Cancer type: " + predicted_label)
+            st.success("Cancer type: "+ predicted_label)
             st.success("Level of Cancer: 0")
     else:
         st.success("Predicted as Lung with Benign Tumor")
 
 
+
 # Create a Streamlit application
 def main():
     st.title('Lung Cancer Detection & Severity Level using Deep Learning')