Spaces:
Running
on
Zero
Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
@@ -55,7 +55,7 @@ def rank_images(model_name, images, text):
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results = model_pipe(images=images, texts=[text]).cpu()[:, 0].tolist() # Perform the model inference on all images
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print("Initial results: should be imgs x texts", results)
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ranked_results = sorted(zip(images, results), key=lambda x: x[1], reverse=True) # Rank results
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ranked_images = [img for img, score in ranked_results]
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print("Ranked Results:", ranked_results)
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except RuntimeError as e:
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print(f"RuntimeError during model inference: {e}")
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@@ -86,7 +86,8 @@ demo_vqascore_ranking = gr.Interface(
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], # define the types of inputs
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outputs=gr.Gallery(label="Ranked Images"), # define the type of output
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title="VQAScore Ranking", # title of the app
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description="This model ranks a gallery of images based on their similarity to a text prompt."
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)
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# Combine the demos into a tabbed interface
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results = model_pipe(images=images, texts=[text]).cpu()[:, 0].tolist() # Perform the model inference on all images
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print("Initial results: should be imgs x texts", results)
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ranked_results = sorted(zip(images, results), key=lambda x: x[1], reverse=True) # Rank results
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ranked_images = [(img, f"Rank: {rank + 1} - Score: {score:.2f}") for rank, (img, score) in enumerate(ranked_results)] # Pair images with their scores and rank
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print("Ranked Results:", ranked_results)
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except RuntimeError as e:
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print(f"RuntimeError during model inference: {e}")
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], # define the types of inputs
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outputs=gr.Gallery(label="Ranked Images"), # define the type of output
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title="VQAScore Ranking", # title of the app
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description="This model ranks a gallery of images based on their similarity to a text prompt.",
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allow_flagging='never'
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)
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# Combine the demos into a tabbed interface
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