Spaces:
Runtime error
Runtime error
File size: 2,387 Bytes
897701e a0727b2 897701e 93fe568 897701e 87045f7 a0727b2 87045f7 a0727b2 897701e dab8972 a0727b2 dab8972 897701e dab8972 897701e dab8972 897701e dab8972 897701e a0727b2 |
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 |
from PIL import Image
from transformers import CLIPProcessor, CLIPModel
import gradio as gr
import torchvision.transforms as transforms
# Initialize CLIP model and processor
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
def image_similarity(image: Image.Image, positive_prompt: str, negative_prompts: str):
# Convert the PIL Image to a tensor and preprocess
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
image_tensor = transform(image).unsqueeze(0) # Add batch dimension
# Split the negative prompts string into a list of prompts
negative_prompts_list = negative_prompts.split(";")
# Combine positive and negative prompts into one list
prompts = [positive_prompt.strip()] + [np.strip() for np in negative_prompts_list]
# Process prompts and image tensor
inputs = processor(
text=prompts,
images=image_tensor,
return_tensors="pt",
padding=True
)
outputs = model(**inputs)
logits_per_image = outputs.logits_per_image
probs = logits_per_image.softmax(dim=1)
# Determine if positive prompt has a higher probability than any of the negative prompts
is_positive_highest = probs[0][0] > max(probs[0][1:])
return bool(is_positive_highest), f"Probability for Positive Prompt: {probs[0][0]:.4f}"
interface = gr.Interface(
fn=image_similarity,
inputs=[
gr.components.Image(type="pil"),
gr.components.Text(label="Enter positive prompt e.g. 'a person drinking a beverage'"),
gr.components.Textbox(label="Enter negative prompts, separated by semicolon e.g. 'an empty scene; person without beverage'", placeholder="negative prompt 1; negative prompt 2; ..."),
],
outputs=[
gr.components.Textbox(label="Result"),
gr.components.Textbox(label="Probability for Positive Prompt")
],
title="Engagify's Image Action Detection",
description="[Author: Ibrahim Hasani] This Method uses CLIP-VIT [Version: BASE-PATCH-16] to determine if an action is being performed in an image or not. (Binary Classifier). It contrasts an Action against multiple negative labels. Ensure the prompts accurately describe the desired
|