File size: 4,115 Bytes
788b9b3 493b35f 788b9b3 493b35f 788b9b3 11b1e95 788b9b3 493b35f 788b9b3 493b35f 788b9b3 493b35f 788b9b3 493b35f 788b9b3 493b35f 788b9b3 493b35f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 |
import pandas as pd
import gradio as gr
from datetime import datetime
from inference import RecommendationEngine
import spaces
class QueryInputForm:
def __init__(self):
# Predefined options for channel and device type
self.channel_options = [
'Paid Social', 'Paid Search - Brand', 'Organic', 'Email - Transactional',
'Affiliate', 'Paid Search', 'Direct', 'Referral', 'Email - Marketing',
'Paid Search - Brand Reactivation', 'SMS - Marketing', 'Email - Trigger',
'Referral - Whitelabel', 'Referral - Merchant', 'Social', 'SMS - Trigger',
]
self.device_type_options = [
'Mobile', 'Desktop', 'Phablet', 'Tablet', 'TV',
'Portable Media Player', 'Wearable',
]
# Default values for the form
self.default_query_text = "pizza"
# Initialize the recommender engine
self.recommender_engine = RecommendationEngine()
@spaces.GPU
def get_recommendations(self, channel, device_type, query_text):
# Pass the query information to the recommender engine
raw_query = {
'user_id': "new_user", # any user will be considered as a new user
'channel': channel,
'device_type': device_type,
'query_text': query_text,
'time': datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f"), # query time
}
# Get recommendations
self.recommender_engine.get_recommendations(raw_query)
recommendations_df = pd.DataFrame(self.recommender_engine.recommendations)
recommendations_df = recommendations_df.style.format({'Score': '{:.2f}'})
# Return the recommendations as a dataframe
return gr.update(value=recommendations_df)
# Instantiate the form
form = QueryInputForm()
# Gradio interface
def recommendation_interface(channel, device_type, query_text):
return form.get_recommendations(channel, device_type, query_text)
with gr.Blocks() as interface:
gr.Markdown("<h1 class='title'>E-Commerce Recommendation Engine Demo</h1>")
with gr.Row(): # Use Row to place components side by side
with gr.Column():
channel_dropdown = gr.Dropdown(choices=form.channel_options, label="Channel")
device_dropdown = gr.Dropdown(choices=form.device_type_options, label="Device Type")
query_input = gr.Textbox(value=form.default_query_text, label="Query Text")
submit_button = gr.Button("Submit", elem_id="submit-button")
with gr.Column(scale=3):
gr.Markdown("<h2>Top Recommendations:</h2>")
recommendation_output = gr.Dataframe(
show_label=False,
headers = [
'Score', 'Product Name', 'Category', 'Price (in cents)', 'Reviews',
'Merchant', 'City', 'State', 'Region',
'Free Shipping', 'Sold Out', 'Editor\'s Pick', 'On Sale',
],
interactive=False,
elem_id="recommendation-table",
max_height=400,
column_widths=["90px", "350px", "250px", "90px", "90px", "200px", "150px", "150px", "150px", "90px", "90px", "90px", "90px"]
)
submit_button.click(
recommendation_interface,
inputs=[channel_dropdown, device_dropdown, query_input],
outputs=recommendation_output,
)
# Add custom CSS for styling
interface.css = """
.title {
color: #E76E00;
text-align: center;
margin: 40px;
margin-bottom: 30px !important;
font-size: 36px;
font-weight: bold;
}
#submit-button {
background-color: #E76E00;
color: white;
border: none;
padding: 10px 20px;
font-size: 16px;
border-radius: 5px;
cursor: pointer;
}
#submit-button:hover {
background-color: #FF8C00;
}
#recommendation-table .gradio-header {
background-color: #FF8C00; /* Orange header */
color: white; /* White text */
font-weight: bold; /* Optional: Bold text */
}
"""
if __name__ == "__main__":
interface.launch()
|