import gradio as gr
from datasets import load_dataset
from sentence_transformers import SentenceTransformer

import os
import requests
os.environ['NO_PROXY'] = 'huggingface.co'

model = SentenceTransformer('clip-ViT-B-32')

# Candidate images.
dataset = load_dataset("sasha/pedro-embeddings")
ds = dataset["train"]
ds.add_faiss_index(column='embeddings')


def query(image, number_to_retrieve=1):
    prompt = model.encode(image)
    scores, retrieved_examples = ds.get_nearest_examples('embeddings', image, k=number_to_retrieve)
    return retrieved_examples


with gr.Blocks() as demo:
	gr.Markdown("# Find my Pedro Pascal")
	gr.Markdown("## Use this Space to find the Pedro Pascal most similar to your input image!")
	with gr.Row():
		with gr.Column(min_width= 900):
			inputs = gr.Image(shape=(800, 1600))
			btn = gr.Button("Find my Pedro!")
			description = gr.Markdown()
			
		with gr.Column():
			outputs=gr.Gallery().style(grid=[1], height="auto")
			
	gr.Markdown("### Image Examples")
	gr.Examples(
	examples=["elton.jpg", "ken.jpg", "gaga.jpg", "taylor.jpg"],
	inputs=inputs,
	outputs=[outputs],
	fn=query,
	cache_examples=True,
	)
	btn.click(query, inputs, [outputs])
	
demo.launch()