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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_rclip = "kaveh/rclip"
embeddings_file_rclip = './image_embeddings_rclip.pkl'
model_path_pubmedclip = "flaviagiammarino/pubmed-clip-vit-base-patch32"
embeddings_file_pubmedclip = './image_embeddings_pubmedclip.pkl'
csv_path = "./captions.txt"
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, model_id="RCLIP", k=2):
if model_id=="RCLIP":
# Load RCLIP model
model = VisionTextDualEncoderModel.from_pretrained(model_path_rclip)
processor = VisionTextDualEncoderProcessor.from_pretrained(model_path_rclip)
# Load image embeddings
image_embeddings = load_embeddings(embeddings_file_rclip)
elif model_id=="PubMedCLIP":
model = CLIPModel.from_pretrained(model_path_pubmedclip)
processor = CLIPProcessor.from_pretrained(model_path_pubmedclip)
# Load image embeddings
image_embeddings = load_embeddings(embeddings_file_pubmedclip)
# 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", "RCLIP", 10],
["Chest X-ray photos", "PubMedCLIP", 10],
["Orthopantogram (OPG)", "RCLIP", 10],
["Brain MRI", "RCLIP", 10],
["Ultrasound", "RCLIP", 10],
]
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)
#Image.open("./data/teesside university logo.png"), height=70, show_label=False, container=False)
with gr.Row(variant="compact"):
query = gr.Textbox(value="Chest X-Ray Photos", label="Enter your query", show_label=False, placeholder= "Enter your query" , scale=5)
btn = gr.Button("Search query", variant="primary", scale=1)
with gr.Row(variant="compact"):
model_id = gr.Dropdown(["RCLIP", "PubMedCLIP"], value="RCLIP", label="Model", type="value", scale=1)
n_s = gr.Slider(2, 10, label='Number of Top Results', value=10, step=1.0, show_label=True, scale=1)
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="400px", preview=True)
gr.Markdown("Information of the found images")
df = gr.DataFrame()
btn.click(main, [query, model_id, n_s], [gallery, df])
with gr.Column(variant="compact"):
gr.Markdown("## Examples")
gr.Examples(examples, [query, model_id, n_s])
demo.launch(debug='True')
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