app.py

import gradio as gr
from transformers import CLIPProcessor, CLIPModel

# Load the CLIP model and processor
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")

def clip_inference(input_img, input_text):
    # Split input_text into a list of text entries
    text_entries = [text.strip() for text in input_text.split(",")]

    # Prepare inputs for CLIP model
    inputs = processor(text=text_entries, images=input_img, return_tensors="pt", padding=True)

    # Get similarity scores
    outputs = model(**inputs)
    logits_per_image = outputs.logits_per_image
    probs = logits_per_image.softmax(dim=1)

    # Format the output probabilities as a comma-separated string
    output_prob = ', '.join([str(prob.item()) for prob in probs[0]])

    return output_prob

title = "CLIP OpenAI Model"
description = "Find similarity between images and multiple text entries (separated by commas)."
text_examples = ["a sky with full of stars, painting image",
                 "a dog playing in the garden, a dog sleeping in the garden",
                 "a small girl dancing, a small girl playing guitar",
                 "a small family cooking in the kitchen,family watching the movie",
                 "students inside the class,students playing in the ground ",
                 "a traffic signal, a lot of cars",
                 "a theatre, a football stadium",
                 "group of animals, group of birds",
                 "yellow sunflowers, red roses",
                 "sunset across the lake, sky with full of stars"]

examples = [["examples/images_" + str(i) + ".jpg", text] for i, text in enumerate(text_examples)]

demo = gr.Interface(
    clip_inference,
    inputs=[
        gr.Image(label="Input image"),
        gr.Textbox(placeholder="Input text : Multiple entries separated by commas"),
    ],
    outputs=[gr.Textbox(label="similarity scores")],
    title=title,
    description=description,
    examples=examples
)

demo.launch()