Update app.py

app.py

@@ -45,7 +45,7 @@ tfidf_matrix = tfidf.fit_transform(products_df['description'].fillna(''))
 # Compute the cosine similarity matrix
 cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)
 
-# Function to get recommendations
+# Function to get recommendations based on product name
 def get_recommendations(product_name, cosine_sim=cosine_sim):
     if product_name not in products_df['product_name'].values:
         return pd.DataFrame(columns=['product_name', 'description'])  # Return empty DataFrame if not found
@@ -56,6 +56,11 @@ def get_recommendations(product_name, cosine_sim=cosine_sim):
     product_indices = [i[0] for i in sim_scores]
     return products_df.iloc[product_indices][['product_name', 'description']]
 
+# Define the recommend_product function for Gradio
+def recommend_product(selected_product):
+    recommendations = get_recommendations(selected_product)
+    return recommendations
+
 # Create the Gradio interface
 product_names = products_df['product_name'].dropna().tolist()  # List of product names
 interface = gr.Interface(