Update app.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+
+# Streamlit UI for the Amazon PPC Keyword Optimizer
+st.title("Amazon PPC Keyword Optimizer")
+st.write("Suggest optimal keywords for Amazon PPC campaigns based on product details and target audience.")
+
+# Input fields for product details
+st.header("Product Details")
+product_name = st.text_input("Enter product name")
+product_category = st.selectbox("Select product category", ["Electronics", "Clothing", "Home", "Beauty", "Toys"])
+product_features = st.text_area("Enter key product features (e.g., color, size, functionality)")
+
+# Input fields for target audience
+st.header("Target Audience")
+audience_gender = st.selectbox("Select audience gender", ["Male", "Female", "Unisex"])
+audience_age = st.slider("Select audience age range", min_value=18, max_value=65, value=(25, 45))
+audience_location = st.text_input("Enter target location (e.g., USA, UK)")
+
+# Input fields for existing product keywords
+st.header("Existing Product Keywords")
+keywords_input = st.text_area("Enter current product keywords (comma-separated)")
+
+# Generate recommended keywords based on input
+def generate_keywords(product_category, product_features, audience_gender, audience_age, audience_location, keywords_input):
+    # Sample logic to generate recommended keywords (can be expanded with a more advanced model)
+    
+    keywords = set(keywords_input.split(","))
+    
+    # Add product-specific features
+    features_keywords = product_features.split(",")
+    for feature in features_keywords:
+        keywords.add(feature.strip())
+    
+    # Add category-related keywords
+    category_keywords = {
+        "Electronics": ["gadgets", "tech", "smartphone", "device", "electronics"],
+        "Clothing": ["fashion", "apparel", "clothing", "t-shirt", "jeans"],
+        "Home": ["home", "furniture", "decor", "household"],
+        "Beauty": ["beauty", "skincare", "cosmetics", "makeup"],
+        "Toys": ["toys", "kids", "play", "educational", "fun"]
+    }
+    
+    for keyword in category_keywords.get(product_category, []):
+        keywords.add(keyword)
+    
+    # Add audience-related keywords
+    if audience_gender:
+        keywords.add(audience_gender.lower())
+    if audience_location:
+        keywords.add(audience_location.lower())
+    keywords.add(f"age{audience_age[0]}-{audience_age[1]}")
+
+    return list(keywords)
+
+# Button to generate recommended keywords
+if st.button("Generate Recommended Keywords"):
+    # Generate keywords based on user input
+    recommended_keywords = generate_keywords(product_category, product_features, audience_gender, audience_age, audience_location, keywords_input)
+    
+    # Display the recommended keywords
+    st.write("### Recommended Keywords")
+    st.write(", ".join(recommended_keywords))
+    
+    # Prepare DataFrame for CSV export
+    df_keywords = pd.DataFrame(recommended_keywords, columns=["Recommended Keywords"])
+    
+    # Button to download the CSV file
+    st.download_button(
+        label="Download Recommended Keywords as CSV",
+        data=df_keywords.to_csv(index=False),
+        file_name="recommended_keywords.csv",
+        mime="text/csv"
+    )