updated

pages/1_📊_About.py

@@ -9,7 +9,26 @@ tab1, tab2 = st.tabs(["Product Discovery", "Question And Answering"])
 with tab1:
     st.header("Product Discovery")
     with st.expander("Details"):
-        st.write("""Lorem Ipsum""")
+        st.markdown("**Application:** Gofynd.com")
+        st.markdown("**Total Products:** 40,000")
+        st.markdown("**Data Used:**")
+        st.markdown("- Text fields - Name, Brand, Category, Gender")
+        st.markdown("- Images - Single Images (First image of the product)")
+        st.markdown("Embedding Model: Used CLIP model for Generating the Embeddings for Text and Image")
+        st.markdown("Weightage [Text, Image]: [0.1, 0.9]")
+        st.markdown("Vector Database: Milvus")
+        st.markdown("ML Pipeline Orchestration: Vertex AI Pipeline")
+        st.markdown("Supported Input data format:")
+        st.markdown("Similarity Metric: Inner Product")
+        st.markdown("Type of Index: FLAT_L2")
+        st.subheader("Input Sample Data Format:")
+        # Insert your input sample data here
+        1. Pull the Catalog data from Fynd Platform
+        2. Parse Textual and Image data from Catalog data
+        3. Generate Embeddings for Textual fields
+        4. Generate Embeddings for Image 
+        5. Aggregate Embeddings with help of defined weightage
+        6. Store it to Vector DB - Milvus
 
 with tab2:
     st.header("Question And Answering")