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(),"dah el data el file")
 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()