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app.py

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+import streamlit as st
+import pickle
+
+# Function to load pickle files
+def read_pickle_files(pickle_file):
+    with open(pickle_file, 'rb') as f:
+        return pickle.load(f)
+
+# Load the necessary pickle files
+cross_sell_data = read_pickle_files("fynd.cross_sell_recommendations-000000000000000000000001s.pkl")
+upsell_data = read_pickle_files("fynd.up_sell_recommendations_000000000000000000000002s.pkl")
+catalog_data = read_pickle_files("clickstream_data_sephora_sephora_products_1724404940894(1).pkl")
+uid_name_pairs = read_pickle_files("uid_name_pairs.pkl")
+
+# Create a mapping from product_id to product name for dropdown
+product_name_to_id = {name: uid for name, uid in uid_name_pairs.items()}
+
+# Create a reverse mapping from product_id to product_name for display purposes
+product_id_to_name = {uid: name for name, uid in uid_name_pairs.items()}
+
+# Function to get product image URL from catalog
+def get_product_image_url(product_id, catalog_data):
+    for product in catalog_data:
+        if product['uid'] == product_id:
+            medias = product.get('medias', [])
+            if medias and 'url' in medias[0]:
+                return medias[0]['url']
+    return None
+
+# Extract product names from recommendation data
+def extract_product_list(recommendation_data):
+    product_ids = [entry['product_id'] for entry in recommendation_data]
+    # Map the product IDs to names for the dropdown
+    return [product_id_to_name[product_id] for product_id in product_ids if product_id in product_id_to_name]
+
+# Extract recommendations for a specific product_id
+def get_recommendations(product_id, recommendation_data):
+    for product in recommendation_data:
+        if product['product_id'] == product_id:
+            return product['recommendations']
+    return []
+
+# Streamlit App Layout
+st.title("Product Recommendations")
+
+# Dropdown for selecting type (cross-sell or up-sell)
+recommendation_type = st.selectbox("Select recommendation type:", ["Cross-sell", "Up-sell"])
+
+# Choose the appropriate data based on recommendation type
+if recommendation_type == "Cross-sell":
+    recommendations_data = cross_sell_data
+elif recommendation_type == "Up-sell":
+    recommendations_data = upsell_data
+
+# Get the list of product names for the dropdown
+product_list = extract_product_list(recommendations_data)
+
+# Dropdown for selecting a product by name
+selected_product_name = st.selectbox("Select a product:", product_list)
+
+# Get the selected product's ID using the name
+selected_product_id = product_name_to_id.get(selected_product_name)
+
+# Display recommendations for the selected product
+if selected_product_id:
+    recommendations = get_recommendations(selected_product_id, recommendations_data)
+    
+    if recommendations:
+        st.subheader(f"Recommendations for {selected_product_name}")
+        for recommendation in recommendations:
+            product_name = recommendation.get('product_name')
+            product_image_url = get_product_image_url(recommendation['product_id'], catalog_data)
+            
+            if product_image_url:
+                st.image(product_image_url, width=100)  # Display image
+            st.write(f"{product_name}")
+    else:
+        st.write("No recommendations found for this product.")

clickstream_data_sephora_sephora_products_1724404940894(1).pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3f3e3565f77f7caf2dbe01721d069edf9a468b70654767aeec3cc2c6bd8c4eda
+size 116037960

fynd.cross_sell_recommendations-000000000000000000000001s.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1bfa8af834b192563f67095e128b888669024641d66781baf81fc1c7be8ca8a8
+size 108384783

fynd.up_sell_recommendations_000000000000000000000002s.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:47b70f4be3b7eb9f69c0289aa2a808409248e6849a01f18e722863369e4cdd54
+size 118980067

uid_name_pairs.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8575bfa829cce8796a1a2dd1a2b612b2e552871ba0c61daad067f311021d7c55
+size 756290