Michael Krasa
commited on
Commit
·
d4ccb5d
1
Parent(s):
e9c62c9
fix delete image
Browse files
app.py
CHANGED
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@@ -6,6 +6,7 @@ import io
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import random
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import requests
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search_terms_wikipedia = {
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"blazing star": "https://en.wikipedia.org/wiki/Mentzelia",
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"bristlecone pine": "https://en.wikipedia.org/wiki/Pinus_longaeva",
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@@ -35,7 +36,7 @@ search_terms_wikipedia = {
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"goldfields coreopsis": "https://en.wikipedia.org/wiki/Coreopsis"
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}
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#
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prompt_templates = [
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"A cosmic {flower} blooming in space, with petals made of swirling galaxies and nebulae, glowing softly against a backdrop of distant stars.",
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"An enchanted garden filled with a bioluminescent {flower}, each petal radiating vibrant, otherworldly colors, illuminating the dark, mystical forest around them.",
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@@ -44,20 +45,32 @@ prompt_templates = [
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"An abstract explosion of a {flower}, blending vibrant colors and fluid shapes in a chaotic, dreamlike composition, evoking movement and emotion."
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]
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def on_queue_update(update):
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if isinstance(update, fal_client.InProgress):
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for log in update.logs:
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print(log["message"])
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def process_image(img):
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#
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predicted_class,
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classification_results = dict(zip(learn.dls.vocab, map(float, probs)))
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# Get Wikipedia
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wiki_url = search_terms_wikipedia.get(predicted_class, "No Wikipedia entry found.")
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# Generate
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result = fal_client.subscribe(
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"fal-ai/flux/schnell",
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arguments={
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@@ -68,47 +81,58 @@ def process_image(img):
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on_queue_update=on_queue_update,
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)
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image_url = result['images'][0]['url']
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response = requests.get(image_url)
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generated_image = Image.open(io.BytesIO(response.content))
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return classification_results, generated_image, wiki_url
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#
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learn = load_learner('export.pkl')
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# Create
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with gr.Blocks() as demo:
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with gr.Row():
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input_image = gr.Image(height=192, width=192, label="Upload Image for Classification", type="pil")
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with gr.Row():
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with gr.Column():
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label_output = gr.Label(label="Classification Results")
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wiki_output = gr.Textbox(label="Wikipedia Article Link", lines=1)
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generated_image = gr.Image(label="AI Generated Interpretation")
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#
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examples = [
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'https://www.deserthorizonnursery.com/wp-content/uploads/2024/03/Brittlebush-Encelia-Farinosa-desert-horizon-nursery.jpg',
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'https://cdn.mos.cms.futurecdn.net/VJE7gSuQ9KWbkqEsWgX5zS.jpg',
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'https://www.parksconservancy.org/sites/default/files/styles/basic/public/A_GEN_131213_WTE_109.jpg?itok=9SDtr4b2',
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'https://silverfallsseed.com/wp-content/uploads/2016/01/tidy-tips-_-9.jpg',
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'https://valleywaternews.org/wp-content/uploads/2016/06/ceanothus-clusters.jpg?w=1440',
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'https://cdn11.bigcommerce.com/s-1b9100svju/images/stencil/1280x1280/products/2044/1440/DETA-635__75522.1664817787.jpg?c=1'
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]
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gr.Examples(
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examples=
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inputs=input_image,
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examples_per_page=6,
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fn=process_image,
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outputs=[label_output, generated_image, wiki_output]
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)
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# Set up
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input_image.change(
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fn=process_image,
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inputs=input_image,
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outputs=[label_output, generated_image, wiki_output]
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)
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demo.launch(inline=False)
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import random
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import requests
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# Dictionary of plant names and their Wikipedia links
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search_terms_wikipedia = {
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"blazing star": "https://en.wikipedia.org/wiki/Mentzelia",
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"bristlecone pine": "https://en.wikipedia.org/wiki/Pinus_longaeva",
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"goldfields coreopsis": "https://en.wikipedia.org/wiki/Coreopsis"
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}
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# Templates for AI image generation
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prompt_templates = [
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"A cosmic {flower} blooming in space, with petals made of swirling galaxies and nebulae, glowing softly against a backdrop of distant stars.",
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"An enchanted garden filled with a bioluminescent {flower}, each petal radiating vibrant, otherworldly colors, illuminating the dark, mystical forest around them.",
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"An abstract explosion of a {flower}, blending vibrant colors and fluid shapes in a chaotic, dreamlike composition, evoking movement and emotion."
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]
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# Example images for the interface
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example_images = [
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'https://www.deserthorizonnursery.com/wp-content/uploads/2024/03/Brittlebush-Encelia-Farinosa-desert-horizon-nursery.jpg',
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'https://cdn.mos.cms.futurecdn.net/VJE7gSuQ9KWbkqEsWgX5zS.jpg',
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'https://www.parksconservancy.org/sites/default/files/styles/basic/public/A_GEN_131213_WTE_109.jpg?itok=9SDtr4b2',
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'https://silverfallsseed.com/wp-content/uploads/2016/01/tidy-tips-_-9.jpg',
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'https://valleywaternews.org/wp-content/uploads/2016/06/ceanothus-clusters.jpg?w=1440',
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'https://cdn11.bigcommerce.com/s-1b9100svju/images/stencil/1280x1280/products/2044/1440/DETA-635__75522.1664817787.jpg?c=1'
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]
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# Function to handle AI generation progress updates
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def on_queue_update(update):
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if isinstance(update, fal_client.InProgress):
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for log in update.logs:
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print(log["message"])
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# Main function to process the uploaded image
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def process_image(img):
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# Classify the image
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predicted_class, _, probs = learn.predict(img)
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classification_results = dict(zip(learn.dls.vocab, map(float, probs)))
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# Get Wikipedia link
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wiki_url = search_terms_wikipedia.get(predicted_class, "No Wikipedia entry found.")
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# Generate artistic interpretation by calling the Flux API
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result = fal_client.subscribe(
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"fal-ai/flux/schnell",
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arguments={
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on_queue_update=on_queue_update,
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)
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# Get the generated image
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image_url = result['images'][0]['url']
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response = requests.get(image_url)
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generated_image = Image.open(io.BytesIO(response.content))
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return classification_results, generated_image, wiki_url
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# Function to clear all outputs
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def clear_outputs():
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return {
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label_output: None,
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generated_image: None,
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wiki_output: None
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}
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# Load the AI model
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learn = load_learner('export.pkl')
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# Create the web interface
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with gr.Blocks() as demo:
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# Input section
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with gr.Row():
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input_image = gr.Image(height=192, width=192, label="Upload Image for Classification", type="pil")
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# Output section
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with gr.Row():
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with gr.Column():
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label_output = gr.Label(label="Classification Results")
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wiki_output = gr.Textbox(label="Wikipedia Article Link", lines=1)
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generated_image = gr.Image(label="AI Generated Interpretation")
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# Add example images
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gr.Examples(
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examples=example_images,
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inputs=input_image,
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examples_per_page=6,
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fn=process_image,
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outputs=[label_output, generated_image, wiki_output]
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)
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# Set up what happens when an image is uploaded or removed
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input_image.change(
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fn=process_image,
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inputs=input_image,
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outputs=[label_output, generated_image, wiki_output]
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)
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input_image.clear(
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fn=clear_outputs,
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inputs=[],
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outputs=[label_output, generated_image, wiki_output]
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)
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# Start the application
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demo.launch(inline=False)
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