Spaces:
Runtime error
Runtime error
File size: 3,903 Bytes
d19fddf |
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 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 |
import gradio as gr
import torch
import pickle
import numpy as np
import pandas as pd
from transformers import CLIPProcessor, CLIPModel
from transformers import VisionTextDualEncoderModel, VisionTextDualEncoderProcessor
from sklearn.metrics.pairwise import cosine_similarity
import csv
from PIL import Image
model_path = "kaveh/rclip"
embeddings_file = './image_embeddings_8_clip14_cxrbert.pkl'
csv_path = "./captions.csv"
def load_image_ids(csv_file):
ids = []
captions = []
with open(csv_file, 'r') as f:
reader = csv.reader(f, delimiter='\t')
for row in reader:
ids.append(row[0])
captions.append(row[1])
return ids, captions
def load_embeddings(embeddings_file):
with open(embeddings_file, 'rb') as f:
image_embeddings = pickle.load(f)
return image_embeddings
def find_similar_images(query_embedding, image_embeddings, k=2):
similarities = cosine_similarity(query_embedding.reshape(1, -1), image_embeddings)
closest_indices = np.argsort(similarities[0])[::-1][:k]
scores = sorted(similarities[0])[::-1][:k]
return closest_indices, scores
def main(query, k=2):
# Load RCLIP model
model = VisionTextDualEncoderModel.from_pretrained(model_path)
processor = VisionTextDualEncoderProcessor.from_pretrained(model_path)
# Load image embeddings
image_embeddings = load_embeddings(embeddings_file)
# Embed the query
inputs = processor(text=query, images=None, return_tensors="pt", padding=True)
with torch.no_grad():
query_embedding = model.get_text_features(**inputs)[0].numpy()
# Get image names
ids, captions = load_image_ids(csv_path)
# Find similar images
similar_image_indices, scores = find_similar_images(query_embedding, image_embeddings, k=int(k))
# Return the results
similar_image_names = [f"./images/{ids[index]}.jpg" for index in similar_image_indices]
similar_image_captions = [captions[index] for index in similar_image_indices]
similar_images = [Image.open(i) for i in similar_image_names]
return similar_images, pd.DataFrame([[t+1 for t in range(k)], similar_image_names, similar_image_captions, scores], index=["#", "path", "caption", "score"]).T
# Define the Gradio interface
examples = [
["Chest X-ray photos",5],
["Orthopantogram (OPG)",5],
["Brain Scan",5],
["tomography",5]
]
title="RCLIP Image Retrieval"
description = "CLIP model fine-tuned on the ROCO dataset"
with gr.Blocks(title=title) as demo:
with gr.Row():
with gr.Column(scale=5):
gr.Markdown("# "+title)
gr.Markdown(description)
gr.HTML(value="<img src=\"https://newresults.co.uk/wp-content/uploads/2022/02/teesside-university-logo.png\" alt=\"teesside logo\" width=\"120\" height=\"70\">", show_label=False,scale=1)
#Image.open("./data/teesside university logo.png"), height=70, show_label=False, container=False)
with gr.Column(variant="compact"):
with gr.Row(variant="compact"):
query = gr.Textbox(label="Enter your query", show_label=False, placeholder= "Enter your query" , scale=5)
btn = gr.Button("Search query", variant="primary", scale=1)
n_s = gr.Slider(2, 10, label='Number of Top Results', value=5, step=1.0, show_label=True)
with gr.Column(variant="compact"):
gr.Markdown("## Results")
gallery = gr.Gallery(label="found images", show_label=True, elem_id="gallery", columns=[2], rows=[4], object_fit="contain", height="auto", preview=True)
gr.Markdown("Information of the found images")
df = gr.DataFrame()
btn.click(main, [query, n_s], [gallery, df])
with gr.Column(variant="compact"):
gr.Markdown("## Examples")
gr.Examples(examples, [query, n_s])
demo.launch(debug='True')
|