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Update Zocket_ImageBind.py
Browse files- Zocket_ImageBind.py +20 -23
Zocket_ImageBind.py
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@@ -2,14 +2,11 @@ from imagebind import data
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import torch
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from imagebind.models import imagebind_model
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from imagebind.models.imagebind_model import ModalityType
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import
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# command = "pip install git+https://github.com/facebookresearch/pytorchvideo.git@28fe037d212663c6a24f373b94cc5d478c8c1a1d timm==0.6.7 ftfy regex einops fvcore decord==0.6.0"
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# process = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE)
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# process.wait()
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# print(process.returncode) # should print 0 if installation was successful
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device = "cuda:0" if torch.cuda.is_available() else "cpu"
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@@ -21,17 +18,25 @@ model.to(device)
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text_list = ["An Advertisement(branding, text, promotions, lifestyle depiction, contextual cues, and visual composition)","Not an Advertisement"]
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image_paths = []
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with gr.Blocks() as demo:
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image = gr.File()
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image_paths.append(image)
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""")
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inputs = {
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@@ -47,12 +52,4 @@ with gr.Blocks() as demo:
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torch.softmax(embeddings[ModalityType.VISION] @ embeddings[ModalityType.TEXT].T, dim=-1),
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gr.Markdown(out)
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demo.launch()
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# Load data
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import torch
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from imagebind.models import imagebind_model
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from imagebind.models.imagebind_model import ModalityType
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import os
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from PIL import Image
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import streamlit as st
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import tempfile
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device = "cuda:0" if torch.cuda.is_available() else "cpu"
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text_list = ["An Advertisement(branding, text, promotions, lifestyle depiction, contextual cues, and visual composition)","Not an Advertisement"]
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image_paths = []
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text = ['Advertisement Creative(Contains Text)', 'Not an Advertisement Creative(Contains No Text)', 'Simple Product Image and not an Advertisement)']
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st.title("Advertisement Detection using CLIP")
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# Upload image
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uploaded_image = st.file_uploader("Choose an image...", type= ["png", "jpg", "jpeg"])
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if uploaded_image is not None:
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temp_dir = tempfile.mkdtemp()
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path = os.path.join(temp_dir, uploaded_image.name)
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with open(path, "wb") as f:
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f.write(uploaded_image.getvalue())
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image_paths.append(path)
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image = Image.open(uploaded_image)
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st.image(image, caption="Uploaded Image.", use_column_width=True)
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inputs = {
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torch.softmax(embeddings[ModalityType.VISION] @ embeddings[ModalityType.TEXT].T, dim=-1),
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st.write(torch.softmax(embeddings[ModalityType.VISION] @ embeddings[ModalityType.TEXT].T, dim=-1))
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