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Browse files- app.py +27 -0
- requirements.txt +3 -0
app.py
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import gradio as gr
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from transformers import BlipProcessor, BlipForConditionalGeneration
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from PIL import Image
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# Load an advanced image captioning 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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# Function to generate a descriptive caption
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def generate_caption(image):
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inputs = processor(image, return_tensors="pt")
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out = model.generate(**inputs, max_length=50, num_beams=5, repetition_penalty=1.5)
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caption = processor.decode(out[0], skip_special_tokens=True)
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return caption
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# Gradio interface with webcam support
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iface = gr.Interface(
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fn=generate_caption,
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inputs=[gr.Image(type="pil", source="webcam", label="Capture an image")],
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outputs=gr.Textbox(label="Image Description"),
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title="📷 Image Capture & Description App",
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description="Capture an image using your webcam and let AI describe what's happening in the image!",
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live=True,
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)
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if __name__ == "__main__":
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iface.launch()
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requirements.txt
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torch
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transformers
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gradio
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