Spaces:
Runtime error
Runtime error
File size: 2,395 Bytes
8cf7f66 38e3100 8cf7f66 38e3100 8cf7f66 f0a42ef fef47b0 8cf7f66 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 |
import gradio as gr
from transformers import CLIPProcessor, CLIPModel, CLIPTokenizer
import sentence_transformers
from sentence_transformers import SentenceTransformer, util
import pickle
from PIL import Image
import os
## Define model
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")
#Open the precomputed embeddings
emb_filename = 'unsplash-25k-photos-embeddings.pkl'
with open(emb_filename, 'rb') as fIn:
img_names, img_emb = pickle.load(fIn)
def search_text(query, top_k=1):
"""" Search an image based on the text query.
Args:
query ([string]): [query you want search for]
top_k (int, optional): [Amount of images o return]. Defaults to 1.
Returns:
[list]: [list of images that are related to the query.]
"""
# First, we encode the query.
inputs = tokenizer([query], padding=True, return_tensors="pt")
query_emb = model.get_text_features(**inputs)
# Then, we use the util.semantic_search function, which computes the cosine-similarity
# between the query embedding and all image embeddings.
# It then returns the top_k highest ranked images, which we output
hits = util.semantic_search(query_emb, img_emb, top_k=top_k)[0]
image=[]
for hit in hits:
print(img_names[hit['corpus_id']])
object = Image.open(os.path.join("photos/", img_names[hit['corpus_id']]))
image.append(object)
return image
iface = gr.Interface(
title = "Text to Image using CLIP Model",
description = "Gradio Demo fo CLIP model. \n This demo is based on assessment for the :hugging: Huggingface course 2. \n To use it, simply write which image you are looking for. Read more at the links below.",
fn=search_text,
inputs=[gr.inputs.Textbox(lines=2,
label="Write what you are looking for...",
placeholder="Name Here..."),
gr.inputs.Slider(0, 5, step=1)],
outputs=gr.outputs.Carousel(gr.outputs.Image(type="pil"))
,examples=[[("Dog in the beach"), 2],
[("Paris during night."), 1],
[("A cute kangaroo"), 5],
[("Dois cachorros"), 2],
[("un homme marchant sur le parc"), 3],
[("et høyt fjell"), 2]]
).launch(debug=True) |