import gradio as gr
from transformers import CLIPModel, CLIPFeatureExtractor, BertTokenizer
from PIL import Image
import torch

# Load model and processors separately
model_name = "jinaai/jina-clip-v1"
model = CLIPModel.from_pretrained(model_name)
feature_extractor = CLIPFeatureExtractor.from_pretrained(model_name)
tokenizer = BertTokenizer.from_pretrained(model_name)

def compute_similarity(image, text):
    image = Image.fromarray(image)  # Convert NumPy array to PIL Image

    # Process image
    image_inputs = feature_extractor(images=image, return_tensors="pt")

    # Process text (Remove `token_type_ids`)
    text_inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
    text_inputs.pop("token_type_ids", None)  # Remove token_type_ids to avoid TypeError

    with torch.no_grad():
        outputs = model(**image_inputs, **text_inputs)
        logits_per_image = outputs.logits_per_image  # Image-to-text similarity
        similarity_score = logits_per_image.item()
    
    return similarity_score

# Gradio UI
demo = gr.Interface(
    fn=compute_similarity,
    inputs=[gr.Image(type="numpy"), gr.Textbox(label="Enter text")],
    outputs=gr.Number(label="Similarity Score"),
    title="JinaAI CLIP Image-Text Similarity",
    description="Upload an image and enter a text prompt to get the similarity score."
)

demo.launch()