app.py

import gradio as gr
from sentence_transformers import SentenceTransformer, util

model_name = 'nq-distilbert-base-v1'
bi_encoder = SentenceTransformer("./")
top_k = 5
sentences = [
 "a happy person is a person how can do what he want with his money",
 "That is a happy dog ho bark alot",
 "Today is a sunny day so that a happy person can walk on the street"
]
# vector embeddings created from dataset
corpus_embeddings = bi_encoder.encode(sentences, convert_to_tensor=True, show_progress_bar=True)

def search(query):
 # Encode the query using the bi-encoder and find potentially relevant passages
 question_embedding = bi_encoder.encode(query)
 hits = util.semantic_search(question_embedding, corpus_embeddings, top_k=top_k)
 hits = hits[0] # Get the hits for the first query

 # Output of top-k hits
 print("Input question:", query)
 print("Results")
 for hit in hits:
 print("\t{:.3f}\t{}".format(hit['score'], sentences[hit['corpus_id']]))
 return hits

def greet(name):
 hittt = search(query=name)
 x=dict()
 for hit in hittt:
 score=hit['score']
 sentence=sentences[hit['corpus_id']]
 buffer={sentence:score}
 x.update(buffer)
 return x
import dill
def greet1(data):
 # pdf=data.get('pdf')
 print(data)
 x=eval(data)
 y=x.get('pdf')
 print(y)
 print(type(y))
 print(type(dill.loads(eval(y))))
 print(dill.loads(eval(y)).read())
 return y
iface = gr.Blocks()
with iface:
 name = gr.Textbox(label="Name")
 output = gr.Textbox(label="Output Box")
 greet_btn = gr.Button("Greet")
 greet_btn.click(fn=greet, inputs=name, outputs=output, api_name="greet")
 greet1_btn = gr.Button("Greet1")
 greet1_btn.click(fn=greet1, inputs=name, outputs=output, api_name="testing")

iface.launch()