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import gradio as gr
import numpy as np
import clip
import torch
from PIL import Image
import base64
from io import BytesIO
from decimal import Decimal # Import the Decimal module
# Load the CLIP model
model, preprocess = clip.load("ViT-B/32")
device = "cuda" if torch.cuda.isavailable() else "cpu"
model.to(device).eval()
# Define a function to find similarity
def find_similarity(base64_image, text_input):
# Decode the base64 image to bytes
image_bytes = base64.b64decode(base64_image)
# Convert the bytes to a PIL image
image = Image.open(BytesIO(image_bytes)
# Preprocess the image
image = preprocess(image).unsqueeze(0).to(device)
# Tokenize the text input
text_tokens = clip.tokenize([text_input]).to(device)
# Encode image and text features
with torch.no_grad():
image_features = model.encode_image(image)
text_features = model.encode_text(text_tokens)
# Calculate cosine similarity
similarity = (image_features @ text_features.T).squeeze(0).cpu().numpy()
# Convert the similarity score to a Decimal
similarity_decimal = Decimal(similarity)
return similarity_decimal # Return the similarity score as a Decimal
# Create a Gradio interface
iface = gr.Interface(
fn=find_similarity,
inputs=[
gr.inputs.Textbox(label="Base64 Image", lines=8),
"text"
],
outputs="text", # Set the output type to "text"
live=True,
interpretation="default",
title="CLIP Model Image-Text Cosine Similarity",
description="Upload a base64 image and enter text to find their cosine similarity.",
)
iface.launch()
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