Spaces:
Sleeping
Sleeping
fixing a few things
Browse files- image2text.py +5 -4
- introduction.md +3 -3
- text2image.py +7 -3
image2text.py
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@@ -13,7 +13,8 @@ def app():
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### π Ciao!
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Here you can find the captions that are most related to a given image.
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π€ Italian mode on! π€
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@@ -30,20 +31,20 @@ def app():
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with col2:
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captions_count = st.selectbox(
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"Number of
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)
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compute = st.button("Compute")
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with col1:
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captions = list()
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for idx in range(min(MAX_CAP, captions_count)):
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captions.append(st.text_input(f"Insert
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if compute:
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captions = [c for c in captions if c != ""]
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if not captions or not filename:
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st.error("Please choose one image and at least one
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else:
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with st.spinner("Computing..."):
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model = get_model()
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### π Ciao!
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Here you can find the captions or the labels that are most related to a given image. It is a zero-shot
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image classification task!
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π€ Italian mode on! π€
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with col2:
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captions_count = st.selectbox(
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"Number of labels", options=range(1, MAX_CAP + 1)
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)
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compute = st.button("Compute")
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with col1:
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captions = list()
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for idx in range(min(MAX_CAP, captions_count)):
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captions.append(st.text_input(f"Insert label {idx+1}"))
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if compute:
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captions = [c for c in captions if c != ""]
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if not captions or not filename:
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st.error("Please choose one image and at least one label")
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else:
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with st.spinner("Computing..."):
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model = get_model()
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introduction.md
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@@ -150,14 +150,14 @@ then there is its (partial) counting ability and finally the ability of understa
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Look at the following - slightly cherry picked (but not even that much) - examples:
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### Colors
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Here's a
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<img src="https://huggingface.co/spaces/clip-italian/clip-italian-demo/raw/main/static/img/fiore_giallo.png" alt="drawing" width="600"/>
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And here's a
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<img src="https://huggingface.co/spaces/clip-italian/clip-italian-demo/raw/main/static/img/fiore_blu.png" alt="drawing" width="600"/>
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### Counting
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What about "one cat"
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<img src="https://huggingface.co/spaces/clip-italian/clip-italian-demo/raw/main/static/img/gatto.png" alt="drawing" width="600"/>
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And what about "two cats"?
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Look at the following - slightly cherry picked (but not even that much) - examples:
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### Colors
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Here's a yellow flower
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<img src="https://huggingface.co/spaces/clip-italian/clip-italian-demo/raw/main/static/img/fiore_giallo.png" alt="drawing" width="600"/>
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And here's a blu flower
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<img src="https://huggingface.co/spaces/clip-italian/clip-italian-demo/raw/main/static/img/fiore_blu.png" alt="drawing" width="600"/>
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### Counting
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What about "one cat"?
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<img src="https://huggingface.co/spaces/clip-italian/clip-italian-demo/raw/main/static/img/gatto.png" alt="drawing" width="600"/>
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And what about "two cats"?
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text2image.py
CHANGED
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@@ -103,7 +103,11 @@ def app():
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### π Ciao!
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Here you can search for images in the Unsplash 25k Photos dataset.
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π€ Italian mode on! π€
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@@ -129,7 +133,7 @@ def app():
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)
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with col4:
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st.button(
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"Un
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col1, col2 = st.beta_columns([3, 1])
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@@ -163,7 +167,7 @@ def app():
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raise ValueError()
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image_paths = utils.find_image(
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query, model, dataset, tokenizer, image_features,
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)
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st.image(image_paths)
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### π Ciao!
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Here you can search for images in the Unsplash 25k Photos dataset and the Conceptual Caption dataset.
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You will see most queries make sense. When you see errors, there might be two possibilities: the model is answering
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in a wrong way or the image you are looking for and the model is giving you the best answer it can get.
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π€ Italian mode on! π€
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)
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with col4:
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st.button(
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"Un gatto sopra una sedia", on_click=update_query, kwargs=dict(value="Un gatto sopra una sedia")
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)
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col1, col2 = st.beta_columns([3, 1])
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raise ValueError()
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image_paths = utils.find_image(
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query, model, dataset, tokenizer, image_features, 1, dataset_name
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)
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st.image(image_paths)
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