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#%%
import gradio as gr
import faiss
import numpy as np
from sentence_transformers import SentenceTransformer
import pandas as pd
# load resources
df = pd.read_csv('final_2.csv')
model = SentenceTransformer('all-MiniLM-L6-v2')
index = faiss.read_index('index_file.pkl')
# map each document ID to its index in the original dataframe
id_mapping = np.array(range(0, len(df)))
def search(query: str, k=3):
query_vector = model.encode([query], convert_to_tensor=True)
query_vector = query_vector / query_vector.norm() # normalize for cosine similarity
query_vector_np = query_vector.cpu().numpy()
_, I = index.search(query_vector_np, k)
return df.iloc[id_mapping[I[0]].tolist()]
# return the results as a dictionary
def query(query:str):
results = search(query)
return results[['Title', 'Authors', 'BuyLink']].to_dict('records')
demo = gr.Interface(fn=query, inputs="text", outputs=gr.outputs.JSON(),
title='Suggest a Book',
description='No Titles! No Authors! Pour your heart out ♥')
demo.launch()
# %%
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