fix input bugs
Browse files- .DS_Store +0 -0
- BLIP/.DS_Store +0 -0
- README.md +0 -13
- app.py +2 -2
- bike.jpg +0 -0
- surfer.jpg +0 -0
.DS_Store
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Binary file (6.15 kB). View file
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BLIP/.DS_Store
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Binary file (6.15 kB). View file
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README.md
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@@ -1,13 +0,0 @@
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---
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title: FuseCap
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emoji: 📊
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colorFrom: pink
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colorTo: red
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sdk: gradio
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sdk_version: 3.29.0
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app_file: app.py
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pinned: false
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license: mit
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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@@ -26,7 +26,7 @@ def inference(raw_image):
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# raw_image = torch.tensor(raw_image)
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image = transform(raw_image).unsqueeze(0).to(device)
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with torch.no_grad():
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caption = model.generate(image, sample=False, num_beams=
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return caption[0]
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@@ -34,7 +34,7 @@ inputs = [gr.Image(type='pil', interactive=False),]
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outputs = gr.outputs.Textbox(label="Caption")
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description = "Gradio demo for FuseCap: Leveraging Large Language Models to Fuse Visual Data into Enriched Image Captions. This demo features a BLIP-based model, trained using FuseCap."
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examples = [["
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article = "<p style='text-align: center'><a href='google.com' target='_blank'>place holder</a>"
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# raw_image = torch.tensor(raw_image)
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image = transform(raw_image).unsqueeze(0).to(device)
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with torch.no_grad():
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caption = model.generate(image, sample=False, num_beams=3, max_length=60, min_length=5)
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return caption[0]
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outputs = gr.outputs.Textbox(label="Caption")
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description = "Gradio demo for FuseCap: Leveraging Large Language Models to Fuse Visual Data into Enriched Image Captions. This demo features a BLIP-based model, trained using FuseCap."
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examples = [["surfer.jpg"], ["bike.jpg"]]
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article = "<p style='text-align: center'><a href='google.com' target='_blank'>place holder</a>"
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bike.jpg
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surfer.jpg
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