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import streamlit as st
from transformers import pipeline
import pickle
import os
import pandas as pd
import seaborn as sns
import ast
import string
import re
st.set_page_config(
page_title="Offer Recommender",
layout="wide"
)
pipe = pipeline(task="zero-shot-classification", model="facebook/bart-large-mnli")
dire = "DS_NLP_search_data"
@st.cache_data
def get_processed_offers():
processed_offers = pd.read_csv(os.path.join(dire, "processed_offers.csv"))
processed_offers["CATEGORY"] = processed_offers["CATEGORY"].map(ast.literal_eval)
return processed_offers
@st.cache_data
def get_categories_data():
cats = pd.read_csv(os.path.join(dire, "categories.csv"))
return cats
@st.cache_data
def get_offers_data():
offers = pd.read_csv(os.path.join(dire, "offer_retailer.csv"))
return offers
@st.cache_data
def get_categories(cats_):
categories = list(cats_["IS_CHILD_CATEGORY_TO"].unique())
for x in ["Mature"]:
if x in categories:
categories.remove(x)
return categories
def get_confidence_charts(results):
df = (
pd.DataFrame({"Category": results["labels"], "probability": results["scores"]})
.sort_values(by="probability", ascending=False)
.reset_index(drop=True)
)
df.index += 1
# Add styling
cmGreen = sns.light_palette("blue", as_cmap=True)
cmRed = sns.light_palette("red", as_cmap=True)
df = df.style.background_gradient(
cmap=cmGreen,
subset=[
"probability",
],
)
format_dictionary = {
"Score": "{:.1%}",
}
df = df.format(format_dictionary)
return df
def check_in_offer(search_str, offer_rets):
offers = []
# print(offer_rets)
for i in range(len(offer_rets)):
offer_str = offer_rets.iloc[i]["OFFER"]
# print(offer_str)
parsed_str = offer_str.lower().translate(str.maketrans('', '', string.punctuation))
parsed_str = re.sub('[^a-zA-Z0-9 \n\.]', '', parsed_str)
# print(parsed_str)
if search_str.lower() in parsed_str.split(" "):
offers.append(offer_str)
df = pd.DataFrame({"OFFER":offers})
# print(df)
return df
def is_retailer(search_str, threshold=0.5):
processed_search_str = search_str.lower().capitalize()
labels = pipe(processed_search_str,
candidate_labels=["brand", "retailer", "item"],
)
return labels["labels"][0] == "retailer" and labels["scores"][0] > threshold
@st.cache
def get_prod_categories():
retail_mapping = {}
for retailer in list(offer_rets["RETAILER"].unique()):
query_direct_retail = complete_df[complete_df["RETAILER"] == retailer]
prod_cats = query_direct_retail["PRODUCT_CATEGORY"].unique()
retail_mapping[retailer] = prod_cats
return retail_mapping
def get_most_overlap(retailer, offer_rets, retail_mapping, top_n=3):
overlaps = {}
for key, value in retail_mapping.items():
if key != retailer.upper():
overlap = set(value).intersection(set(retail_mapping[retailer.upper()]))
overlaps[key] = len(overlap)
sorted_overlaps = dict(sorted(overlaps.items(), key=lambda x:x[1], reverse=True))
related_retailers = list({k:sorted_overlaps[k] for k in list(sorted_overlaps)[:top_n]}.keys())
offers = list(offer_rets[offer_rets["RETAILER"].isin(related_retailers)]["OFFER"])
df = pd.DataFrame({"OFFERS": offers})
return df
def perform_cat_inference(search_str, categories, cats, processed_offers):
labels = pipe(search_str,
candidate_labels=categories,
)
print(labels)
# labels = [l for i, l in enumerate(labels["labels"]) if labels["scores"][i] > 0.20]
filtered_cats = list(cats[cats["IS_CHILD_CATEGORY_TO"].isin(labels["labels"][:3])]["PRODUCT_CATEGORY"].unique())
labels_2 = pipe(search_str,
candidate_labels=filtered_cats,
)
print(labels_2)
top_labels = labels_2["labels"][:3]
print(top_labels)
offers = processed_offers[processed_offers["CATEGORY"].apply(lambda x: bool(set(x) & set(top_labels)))]["OFFER"].reset_index()
return offers, labels, labels_2
def main():
col_1, col_2, col_3 = st.columns(3)
search_str = col_2.text_input("Enter a retailer, brand, or category").capitalize()
processed_offers = get_processed_offers()
cats = get_categories_data()
offer_rets = get_offers_data()
categories = get_categories(cats)
# retail_mapping = get_prod_categories()
if col_2.button("Search", type="primary"):
retail = is_retailer(search_str)
direct_offers = check_in_offer(search_str, offer_rets)
if retail:
col_2.table(direct_offers)
# related_offers = get_most_overlap(retailer, offer_rets, retail_mapping, top_n=3)
# col_2.table(related_offers[~related_offers["OFFER"].isin(list(direct_offers["OFFER"]))])
else:
col_2.table(direct_offers)
related_offers, labels_1, labels_2 = perform_cat_inference(search_str, categories, cats, processed_offers)
col_2.table(related_offers[~related_offers["OFFER"].isin(list(direct_offers["OFFER"]))])
col_2.table(pd.DataFrame({"labels": labels_1["labels"][:5], "scores": labels_1["scores"][:5]}))
col_2.table(pd.DataFrame({"labels": labels_2["labels"][:5], "scores": labels_2["scores"][:5]}))
# df = get_confidence_charts(labels_2)
# st.table(df)
if __name__ == "__main__":
main()
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