Update app.py

app.py

@@ -30,6 +30,44 @@ def load_data():
 
 data = load_data()
 
+# Helper functions
+@st.cache_data
+def download_and_process_image(image_url):
+    try:
+        response = requests.get(image_url)
+        response.raise_for_status()  # Raises an HTTPError for bad responses
+        image = Image.open(BytesIO(response.content))
+        
+        # Convert image to RGB mode if it's in RGBA mode
+        if image.mode == 'RGBA':
+            image = image.convert('RGB')
+        
+        return image
+    except requests.RequestException as e:
+        st.error(f"Error downloading image: {e}")
+        return None
+    except Exception as e:
+        st.error(f"Error processing image: {e}")
+        return None
+
+def segment_image(image_path):
+    # Implement your segmentation logic here
+    # For now, we'll just return the original image
+    return Image.open(image_path)
+
+def get_image_embedding(image):
+    image_tensor = preprocess_val(image).unsqueeze(0).to(device)
+    with torch.no_grad():
+        image_features = model.encode_image(image_tensor)
+        image_features /= image_features.norm(dim=-1, keepdim=True)
+    return image_features.cpu().numpy()
+
+def setup_roboflow_client(api_key):
+    return InferenceHTTPClient(
+        api_url="https://outline.roboflow.com",
+        api_key=api_key
+    )
+
 # Process database with segmentation
 @st.cache_data
 def process_database():
@@ -120,101 +158,4 @@ if api_key:
                         st.write(f"Discount: {img['info']['discount']}%")
                         st.write(f"Similarity: {img['similarity']:.2f}")
 else:
-    st.warning("Please enter your Roboflow API Key to use the app.")
-
-
-# Process database with segmentation
-@st.cache_data
-def download_and_process_image(image_url):
-    try:
-        response = requests.get(image_url)
-        response.raise_for_status()  # Raises an HTTPError for bad responses
-        image = Image.open(BytesIO(response.content))
-        
-        # Convert image to RGB mode if it's in RGBA mode
-        if image.mode == 'RGBA':
-            image = image.convert('RGB')
-        
-        return image
-    except requests.RequestException as e:
-        st.error(f"Error downloading image: {e}")
-        return None
-    except Exception as e:
-        st.error(f"Error processing image: {e}")
-        return None
-
-def process_database():
-    database_embeddings = []
-    database_info = []
-    for item in data:
-        image_url = item['이미지 링크'][0]
-        product_id = item.get('\ufeff상품 ID') or item.get('상품 ID')
-        
-        image = download_and_process_image(image_url)
-        if image is None:
-            continue
-        
-        # Save the image temporarily
-        temp_path = f"temp_{product_id}.jpg"
-        image.save(temp_path, 'JPEG')
-        
-        segmented_image = segment_image(temp_path)
-        embedding = get_image_embedding(segmented_image)
-        
-        database_embeddings.append(embedding)
-        database_info.append({
-            'id': product_id,
-            'category': item['카테고리'],
-            'brand': item['브랜드명'],
-            'name': item['제품명'],
-            'price': item['정가'],
-            'discount': item['할인율'],
-            'image_url': image_url
-        })
-    
-    return np.vstack(database_embeddings), database_info
-
-def find_similar_images(query_embedding, top_k=5):
-    similarities = np.dot(database_embeddings, query_embedding.T).squeeze()
-    top_indices = np.argsort(similarities)[::-1][:top_k]
-    
-    results = []
-    for idx in top_indices:
-        results.append({
-            'info': database_info[idx],
-            'similarity': similarities[idx]
-        })
-    
-    return results
-
-uploaded_file = st.file_uploader("Choose an image...", type="jpg")
-if uploaded_file is not None:
-    image = Image.open(uploaded_file)
-    st.image(image, caption='Uploaded Image', use_column_width=True)
-    
-    if st.button('Find Similar Items'):
-        with st.spinner('Processing...'):
-            # Save uploaded image temporarily
-            temp_path = "temp_upload.jpg"
-            image.save(temp_path)
-            
-            # Segment the uploaded image
-            segmented_image = segment_image(temp_path)
-            st.image(segmented_image, caption='Segmented Image', use_column_width=True)
-            
-            # Get embedding for segmented image
-            query_embedding = get_image_embedding(segmented_image)
-            similar_images = find_similar_images(query_embedding)
-            
-            st.subheader("Similar Items:")
-            for img in similar_images:
-                col1, col2 = st.columns(2)
-                with col1:
-                    st.image(img['info']['image_url'], use_column_width=True)
-                with col2:
-                    st.write(f"Name: {img['info']['name']}")
-                    st.write(f"Brand: {img['info']['brand']}")
-                    st.write(f"Category: {img['info']['category']}")
-                    st.write(f"Price: {img['info']['price']}")
-                    st.write(f"Discount: {img['info']['discount']}%")
-                    st.write(f"Similarity: {img['similarity']:.2f}")
+    st.warning("Please enter your Roboflow API Key to use the app.")