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import gradio as gr
import pandas as pd
import pickle
from sentence_transformers import SentenceTransformer, util
import re
mdl_name = 'sentence-transformers/all-distilroberta-v1'
model = SentenceTransformer(mdl_name)
embedding_cache_path = "scotch_embd_distilroberta.pkl"
with open(embedding_cache_path, "rb") as fIn:
cache_data = pickle.load(fIn)
embedding_table = cache_data["embeddings"]
reviews = cache_data["data"]
reviews['price'] = reviews.price.apply(lambda x: re.findall("\d+", x.replace(",","").replace(".00","").replace("$",""))[0]).astype('int')
def user_query_recommend(query, price_rng):
# Embed user query
embedding = model.encode(query)
# Calculate similarity with all reviews
sim_scores = util.cos_sim(embedding, embedding_table)
#print(sim_scores.shape)
# Recommend
recommendations = reviews.copy()
recommendations['sim'] = sim_scores.T
recommendations = recommendations.sort_values('sim', ascending=False)
if price_rng == "$0-$50":
min_p, max_p = 0, 50
if price_rng == "$50-$100":
min_p, max_p = 50, 100
if price_rng == "$100+":
min_p, max_p = 100, 10000
recommendations = recommendations.loc[(recommendations.price >= min_p) &
(recommendations.price <= max_p),
['name', 'price', 'description']].head(5)
return recommendations.reset_index(drop=True)
interface = gr.Interface(
user_query_recommend,
inputs=[gr.inputs.Textbox(lines=5, label = "enter flavour profile"),
gr.inputs.Radio(choices = ["$0-$50", "$50-$100", "$100+"], default="$0-$50", type="value", label='Price range')],
outputs=gr.outputs.Dataframe(max_rows=1, overflow_row_behaviour="paginate", type="pandas", label="Scotch recommendations"),
title = "Scotch Recommendation",
description = "Looking for scotch recommendations and have some flavours in mind? \nGet recommendations at a preferred price range using semantic search :) ",
examples=[["very sweet with lemons and oranges and marmalades", "$0-$50"],
["smoky peaty earthy and spicy","$50-$100"],
["salty and spicy with exotic fruits", "$100+"],
["fragrant nose with chocolate, toffee, pudding and caramel", "$50-$100"],
],
theme="grass",
)
interface.launch(
enable_queue=True,
#cache_examples=True,
) |