feat: generated files

app.py

@@ -87,7 +87,7 @@ st.markdown("""
 """)
 
 # Navigation menu
-page = st.selectbox("Select the tool you want to use", ["", "Customer Analysis", "Customer Recommendations"])
+page = st.selectbox("Select the tool you want to use", ["", "Customer Analysis", "Basket Recommendations"])
 
 # Home Page
 if page == "":
@@ -188,12 +188,11 @@ elif page == "Customer Analysis":
             st.warning(f"No data found for customer {customer_code}. Please check the code.")
 
 # Customer Recommendations Page
-elif page == "Customer Recommendations":
-    st.title("Customer Recommendations")
-    st.markdown("""
-        Get tailored recommendations for your customers based on their purchasing history.
-    """)
+elif page == "Basket Recommendations":
+    st.title("Basket Recommendations")
+    st.markdown("""Get tailored product recommendations for your customers based on their purchasing basket.""")
 
+    # Use the same customer search as in Customer Analysis
     partial_code = st.text_input("Enter part of Customer Code for Recommendations (or leave empty to see all)")
     if partial_code:
         filtered_customers = df[df['CLIENTE'].str.contains(partial_code)]
@@ -202,15 +201,30 @@ elif page == "Customer Recommendations":
     customer_list = filtered_customers['CLIENTE'].unique()
     customer_code = st.selectbox("Select Customer Code for Recommendations", customer_list)
 
+    # Basket input section
     if customer_code:
-        customer_data = df[df["CLIENTE"] == str(customer_code)]
-
-        if not customer_data.empty:
-            st.write(f"### Purchase History for Customer {customer_code}")
-            st.write(customer_data)
-
-            st.write(f"### Recommended Products for Customer {customer_code}")
-            # Placeholder for recommendation logic
-            st.write("Product A, Product B, Product C")
-        else:
-            st.warning(f"No data found for customer {customer_code}. Please check the code.")
+        st.write(f"### Purchase Basket for Customer {customer_code}")
+
+        # Create input fields for article codes and units
+        basket_items = []
+        num_lines = st.number_input("How many lines do you want to add?", min_value=1, value=1, step=1)
+        
+        for i in range(num_lines):
+            col1, col2 = st.columns(2)
+            with col1:
+                article_code = st.text_input(f"Enter Article Code for Line {i + 1}")
+            with col2:
+                units = st.number_input(f"Enter Units for Line {i + 1}", min_value=1, value=1, step=1)
+
+            # Add each article code and units to the list
+            if article_code and units:
+                basket_items.append((article_code, units))
+
+        # Generate the concatenated string
+        input_cesta = ''.join([f"{code * units}" for code, units in basket_items])
+
+        # Show the result
+        st.write(f"Generated input_cesta: {input_cesta}")
+
+        # Placeholder for passing the input_cesta to the model
+        st.write("Model is not yet implemented, but input_cesta is ready.")

customer_analysis.py

@@ -1,22 +0,0 @@
-import streamlit as st
-import pandas as pd
-import plotly.express as px
-
-st.set_page_config(page_title="Customer Analysis", page_icon=":mag:")
-
-st.title("Customer Analysis")
-st.markdown("""
-    Use the tools below to explore your customer data.
-""")
-
-# Cargar y visualizar datos
-uploaded_file = st.file_uploader("Upload your CSV file", type="csv")
-if uploaded_file:
-    df = pd.read_csv(uploaded_file)
-    st.write("## Dataset Overview", df.head())
-
-    # Mostrar un gráfico interactivo
-    st.markdown("### Sales per Customer")
-    customer_sales = df.groupby("CLIENTE")["VENTA_ANUAL"].sum().reset_index()
-    fig = px.bar(customer_sales, x="CLIENTE", y="VENTA_ANUAL", title="Annual Sales per Customer")
-    st.plotly_chart(fig)

customer_recommendation.py

@@ -1,24 +0,0 @@
-import streamlit as st
-import pandas as pd
-
-st.set_page_config(page_title="Customer Recommendations", page_icon=":chart_with_upwards_trend:")
-
-st.title("Customer Recommendations")
-st.markdown("""
-    Get tailored recommendations for your customers based on their purchasing history.
-""")
-
-# Cargar los datos
-uploaded_file = st.file_uploader("Upload your CSV file", type="csv")
-if uploaded_file:
-    df = pd.read_csv(uploaded_file)
-    customer_id = st.selectbox("Select a Customer", df["CLIENTE"].unique())
-
-    # Mostrar datos y recomendación
-    st.write(f"### Purchase History for Customer {customer_id}")
-    customer_data = df[df["CLIENTE"] == customer_id]
-    st.write(customer_data)
-
-    # Generar recomendaciones (placeholder)
-    st.write(f"### Recommended Products for Customer {customer_id}")
-    st.write("Product A, Product B, Product C")