app.py

import os
os.system('pip install openpyxl')
os.system('pip install sentence-transformers')
import pandas as pd
import gradio as gr
from sentence_transformers import SentenceTransformer
from sklearn.neighbors import NearestNeighbors
import numpy as np
import pandas as pd

from sentence_transformers import SentenceTransformer

model = SentenceTransformer('all-mpnet-base-v2') #all-MiniLM-L6-v2 #all-mpnet-base-v2
# os.chdir(os.path.dirname(__file__))
df = pd.read_parquet('df_encoded.parquet')

#prepare model
nbrs = NearestNeighbors(n_neighbors=4, algorithm='ball_tree').fit(df['text_vector_'].values.tolist())

def search(df, query):
    product = model.encode(query).tolist()
    # product = df.iloc[0]['text_vector_'] #use one of the products as sample

    distances, indices = nbrs.kneighbors([product]) #input the vector of the reference object

    #print out the description of every recommended product
    return df.iloc[list(indices)[0]][['Description', 'UnitPrice', 'Country']]

import gradio as gr
import os

#the first module becomes text1, the second module file1
def greet(text1): 
    return search(df, text1)

with gr.Blocks(theme=gr.themes.Soft(primary_hue='amber', secondary_hue='gray', neutral_hue='amber')) as demo:
    gr.Markdown(
    """
    # Try our DEMO!!!
    """
    )
    txt = gr.Textbox(value='A Christmas present🎄for my 5 years old Kid!!!', label='What are you looking for?')
    btn = gr.Button(value="Search for Product")
    state = gr.Dataframe()
    # btn.click(greet, inputs='text', outputs=['dataframe'])
    btn.click(greet, [txt], [state])

demo.launch(share=False)

# iface = gr.Interface(
#     fn=greet,
#     inputs=[
#         gr.Textbox(value='A Christmas present🎄for my 5 years old Kid!!!', label='Describe the product to search, then press submit')
#         ],
#     outputs=["dataframe"],
#     title='DEMO: Ecommerce Product Recommendation'
# )
# iface.launch(share=False)