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()