Update main.py

main.py

@@ -10,6 +10,16 @@ from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
 from sklearn.neighbors import NearestNeighbors
 from numpy.linalg import norm
 from chatbot import Chatbot  # Assuming you have a chatbot module
+import zipfile
+
+# Define the path to the zip file and the directory to extract to
+zip_file_path = 'images.zip'
+extract_to = 'images'
+
+# Check if the images directory already exists to avoid re-extracting
+if not os.path.exists(extract_to):
+    with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
+        zip_ref.extractall(extract_to)
 
 # Define function for feature extraction
 def feature_extraction(img_path, model):
@@ -39,10 +49,10 @@ def save_uploaded_file(uploaded_file):
         with open(file_path, 'wb') as f:
             f.write(uploaded_file.getbuffer())
         st.success(f"File saved to {file_path}")
-        return True
+        return file_path
     except Exception as e:
         st.error(f"Error saving file: {e}")
-        return False
+        return None
 
 # Function to show dashboard content
 def show_dashboard():
@@ -66,16 +76,28 @@ def show_dashboard():
     # File upload section
     uploaded_file = st.file_uploader("Choose an image")
     if uploaded_file is not None:
-        if save_uploaded_file(uploaded_file):
+        file_path = save_uploaded_file(uploaded_file)
+        if file_path:
             # Display the uploaded image
-            display_image = Image.open(uploaded_file)
-            st.image(display_image)
+            try:
+                display_image = Image.open(file_path)
+                st.image(display_image)
+            except Exception as e:
+                st.error(f"Error displaying uploaded image: {e}")
 
             # Feature extraction
-            features = feature_extraction(os.path.join("uploads", uploaded_file.name), model)
+            try:
+                features = feature_extraction(file_path, model)
+            except Exception as e:
+                st.error(f"Error extracting features: {e}")
+                return
 
             # Recommendation
-            indices = recommend(features, feature_list)
+            try:
+                indices = recommend(features, feature_list)
+            except Exception as e:
+                st.error(f"Error in recommendation: {e}")
+                return
 
             # Display recommended products
             col1, col2, col3, col4, col5 = st.columns(5)