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Update app.py
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app.py
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import streamlit as st
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from PIL import Image
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import requests
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from transformers import BlipProcessor, BlipForConditionalGeneration
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# Load the BLIP model
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processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
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model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")
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# Streamlit app
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st.title("Image Captioning with BLIP")
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# Uploading the image
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uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
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if uploaded_image is not None:
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image = Image.open(uploaded_image).convert('RGB')
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st.image(image, caption='Uploaded Image', use_column_width=True)
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# Perform conditional image captioning
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captioning_mode = st.selectbox("Captioning Mode", ["Conditional", "Unconditional"])
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if captioning_mode == "Conditional":
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text = st.text_input("Provide a condition for the captioning (e.g., 'a photo of', 'an illustration of'): ", "a photo of")
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if text: # Only proceed if the user has provided a text
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inputs = processor(image, text, return_tensors="pt")
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out = model.generate(**inputs)
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caption = processor.decode(out[0], skip_special_tokens=True)
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st.write(f"Generated Caption: {caption}")
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else: # Unconditional captioning
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inputs = processor(image, return_tensors="pt")
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out = model.generate(**inputs)
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caption = processor.decode(out[0], skip_special_tokens=True)
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st.write(f"Generated Caption: {caption}")
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