Update db

app.py

@@ -251,124 +251,132 @@ with st.sidebar:
 
     top_similar_count = st.number_input("Top Similarities to be Displayed", value=3, min_value=1, step=1, format="%i")
 
-if st.button('Check for Infringement'):
-    global log_output
 
-    tab1, tab2 = st.tabs(["📊 Output", "🖥️ Console"])
-
-    with tab2:
-        log_output = st.empty()
-
-    with tab1:
-        with st.spinner('Processing...'):
+col1,col2 = st.columns([7,3])
+
+with col1:
+    run_streamlit = st.button('Check for Infringement')
+
+
+if run_streamlit:
+        global log_output
+
+        tab1, tab2 = st.tabs(["📊 Output", "🖥️ Console"])
+
+        with tab2:
+            log_output = st.empty()
+
+        with tab1:
+            with st.spinner('Processing...'):
+
+                if len(os.listdir('/home/user/app/embeddings'))<2:
+                    download_db()
+                    print("\u2713 Downloaded Database\n\n")
+
+                with StreamCapture() as logger:
+                    top_similar_values = score(main_product, main_url, product_count, link_count, search_method, logger, log_output)
+
+            st.success('✅ Processing complete!')
+
+            st.subheader("📈 Cosine Similarity Scores")
+
+            for main_text, main_vector, response, _ in top_similar_values:
+                product_name = response['metadatas'][0][0]['product_name']
+                link = response['metadatas'][0][0]['url']
+                similar_text = response['metadatas'][0][0]['text']
+                # similar_text_refined = imporve_text(similar_text)
+                # main_text_refined = imporve_text(main_text)
+
+                cosine_score = cosine_similarity([main_vector], response['embeddings'][0])[0][0]
+
+                # Display the product information
+                with st.expander(f"### Product: {product_name} - Score: {cosine_score:.4f}"):
+                    link = link.replace(" ","%20")
+                    st.markdown(f"[View Product Manual]({link})")
+                    tab1, tab2 = st.tabs(["Raw Text", "Refined Text"])
+                    with tab2:
+                        col1, col2 = st.columns(2)
+                        with col1:
+                            st.markdown(f"*Main Text:\n* {imporve_text(main_text)}")
+                        with col2:
+                            st.markdown(f"*Similar Text\n:* {imporve_text(similar_text)}")
+                    
+                    with tab1:
+                        col1, col2 = st.columns(2)
+                        with col1:
+                            st.markdown(f"*Main Text:* {main_text}")
+                        with col2:
+                            st.markdown(f"*Similar Text:* {similar_text}")
+
+            if need_image == 'True':
+                with st.spinner('Processing Images...'):
+                    emb_main , main_prod_imgs = get_image_embeddings(main_product)
+                    similar_prod = extract_similar_products(main_product)[0]
+                    emb_similar , similar_prod_imgs = get_image_embeddings(similar_prod)
+
+                    similarity_matrix = np.zeros((5, 5))
+                    for i in range(5):
+                        for j in range(5):
+                            similarity_matrix[i][j] = cosine_similarity([emb_main[i]], [emb_similar[j]])[0][0]
+
+                    st.subheader("Image Similarity")
+                    # Create an interactive heatmap
+                    fig = px.imshow(similarity_matrix,
+                                    labels=dict(x=f"{similar_prod} Images", y=f"{main_product} Images", color="Similarity"),
+                                    x=[f"Image {i+1}" for i in range(5)],
+                                    y=[f"Image {i+1}" for i in range(5)],
+                                    color_continuous_scale="Viridis")
+
+                    # Add title to the heatmap
+                    fig.update_layout(title="Image Similarity Heatmap")
+
+                    # Display the interactive heatmap
+                    st.plotly_chart(fig)
+
+
+
+                    @st.experimental_fragment
+                    def image_viewer():
+                        # Form to handle image selection
+
+                        st.subheader("Image Viewer")
+
+                        selected_row = st.selectbox('Select a row (Main Product Image)', [f'Image {i+1}' for i in range(5)])
+                        selected_col = st.selectbox('Select a column (Similar Product Image)', [f'Image {i+1}' for i in range(5)])
+
+                        # Get the selected indices from session state
+                        row_idx = int(selected_row.split()[1]) - 1
+                        col_idx = int(selected_col.split()[1]) - 1
+
+                        col1, col2 = st.columns(2)
+
+                        with col1:
+                            st.image(main_prod_imgs[row_idx], caption=f'Main Product Image {row_idx+1}', use_column_width=True)
+                        with col2:
+                            st.image(similar_prod_imgs[col_idx], caption=f'Similar Product Image {col_idx+1}', use_column_width=True)
+
+                    # Call the fragment
+                    image_viewer()
+            
+
+@st.experimental_dialog("Confirm Database Backup")
+def update():
+    st.write("Do you want to backup the new changes in the database?")
+    if st.button("Confirm",type="primary"):
+        st.write("Updating Database....")
+        st.session_state.update = {"Done": True}
+
+        update_db()
+
+        st.success('Backup Complete!', icon="✅")
+        time.sleep(2)
+        st.rerun()
+
+if "update" not in st.session_state:
+    with col2:
+        update_button = st.button("Update Database",type="primary")
+        if update_button:
+            update()
 
-            for path in os.listdir('/home/user/app/embeddings'):
-                print(path)
-            if os.path.exists('/home/user/app/embeddings'):
-                download_db()
-                print("\u2713 Downloaded Database\n\n")
-
-            with StreamCapture() as logger:
-                top_similar_values = score(main_product, main_url, product_count, link_count, search_method, logger, log_output)
-
-        st.success('✅ Processing complete!')
-
-        st.subheader("📈 Cosine Similarity Scores")
-
-        for main_text, main_vector, response, _ in top_similar_values:
-            product_name = response['metadatas'][0][0]['product_name']
-            link = response['metadatas'][0][0]['url']
-            similar_text = response['metadatas'][0][0]['text']
-            # similar_text_refined = imporve_text(similar_text)
-            # main_text_refined = imporve_text(main_text)
-
-            cosine_score = cosine_similarity([main_vector], response['embeddings'][0])[0][0]
-
-            # Display the product information
-            with st.expander(f"### Product: {product_name} - Score: {cosine_score:.4f}"):
-                link = link.replace(" ","%20")
-                st.markdown(f"[View Product Manual]({link})")
-                tab1, tab2 = st.tabs(["Raw Text", "Refined Text"])
-                with tab2:
-                    col1, col2 = st.columns(2)
-                    with col1:
-                        st.markdown(f"*Main Text:\n* {imporve_text(main_text)}")
-                    with col2:
-                        st.markdown(f"*Similar Text\n:* {imporve_text(similar_text)}")
-                
-                with tab1:
-                    col1, col2 = st.columns(2)
-                    with col1:
-                        st.markdown(f"*Main Text:* {main_text}")
-                    with col2:
-                        st.markdown(f"*Similar Text:* {similar_text}")
-
-        if need_image == 'True':
-            with st.spinner('Processing Images...'):
-                emb_main , main_prod_imgs = get_image_embeddings(main_product)
-                similar_prod = extract_similar_products(main_product)[0]
-                emb_similar , similar_prod_imgs = get_image_embeddings(similar_prod)
-
-                similarity_matrix = np.zeros((5, 5))
-                for i in range(5):
-                    for j in range(5):
-                        similarity_matrix[i][j] = cosine_similarity([emb_main[i]], [emb_similar[j]])[0][0]
-
-                st.subheader("Image Similarity")
-                # Create an interactive heatmap
-                fig = px.imshow(similarity_matrix,
-                                labels=dict(x=f"{similar_prod} Images", y=f"{main_product} Images", color="Similarity"),
-                                x=[f"Image {i+1}" for i in range(5)],
-                                y=[f"Image {i+1}" for i in range(5)],
-                                color_continuous_scale="Viridis")
-
-                # Add title to the heatmap
-                fig.update_layout(title="Image Similarity Heatmap")
-
-                # Display the interactive heatmap
-                st.plotly_chart(fig)
-
-
-
-                @st.experimental_fragment
-                def image_viewer():
-                    # Form to handle image selection
-
-                    st.subheader("Image Viewer")
-
-                    selected_row = st.selectbox('Select a row (Main Product Image)', [f'Image {i+1}' for i in range(5)])
-                    selected_col = st.selectbox('Select a column (Similar Product Image)', [f'Image {i+1}' for i in range(5)])
-
-                    # Get the selected indices from session state
-                    row_idx = int(selected_row.split()[1]) - 1
-                    col_idx = int(selected_col.split()[1]) - 1
-
-                    col1, col2 = st.columns(2)
-
-                    with col1:
-                        st.image(main_prod_imgs[row_idx], caption=f'Main Product Image {row_idx+1}', use_column_width=True)
-                    with col2:
-                        st.image(similar_prod_imgs[col_idx], caption=f'Similar Product Image {col_idx+1}', use_column_width=True)
-
-                # Call the fragment
-                image_viewer()
-
-def zip_folder(folder_path, zip_name):
-    # Create a zip file from the folder
-    shutil.make_archive(zip_name, 'zip', folder_path)
-    return zip_name + '.zip'
-
-folder_path = '/home/user/app/embeddings'
-zip_name = 'embedding'
-
-if st.button("Download"):
-    zip_file = zip_folder(folder_path, zip_name)
-    with open(zip_file, "rb") as f:
-        st.download_button(
-            label="Download ZIP",
-            data=f,
-            file_name=zip_file,
-            mime="application/zip"
-        )