Spaces:
Sleeping
Sleeping
File size: 4,190 Bytes
e8746a9 9cca2b0 69de931 e8746a9 9cca2b0 e8746a9 69de931 e8746a9 69de931 5c93b6c 69de931 e8746a9 69de931 9cca2b0 69de931 e8746a9 69de931 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 |
from fastai.vision.all import *
import gradio as gr
import fal_client
from PIL import Image
import io
import base64
search_terms_wikipedia = {
"blazing star": "https://en.wikipedia.org/wiki/Mentzelia",
"bristlecone pine": "https://en.wikipedia.org/wiki/Pinus_longaeva",
"california bluebell": "https://en.wikipedia.org/wiki/Phacelia_minor",
"california buckeye": "https://en.wikipedia.org/wiki/Aesculus_californica",
"california buckwheat": "https://en.wikipedia.org/wiki/Eriogonum_fasciculatum",
"california fuchsia": "https://en.wikipedia.org/wiki/Epilobium_canum",
"california checkerbloom": "https://en.wikipedia.org/wiki/Sidalcea_malviflora",
"california lilac": "https://en.wikipedia.org/wiki/Ceanothus",
"california poppy": "https://en.wikipedia.org/wiki/Eschscholzia_californica",
"california sagebrush": "https://en.wikipedia.org/wiki/Artemisia_californica",
"california wild grape": "https://en.wikipedia.org/wiki/Vitis_californica",
"california wild rose": "https://en.wikipedia.org/wiki/Rosa_californica",
"coyote mint": "https://en.wikipedia.org/wiki/Monardella",
"elegant clarkia": "https://en.wikipedia.org/wiki/Clarkia_unguiculata",
"baby blue eyes": "https://en.wikipedia.org/wiki/Nemophila_menziesii",
"hummingbird sage": "https://en.wikipedia.org/wiki/Salvia_spathacea",
"delphiniumr": "https://en.wikipedia.org/wiki/Delphinium",
"matilija poppy": "https://en.wikipedia.org/wiki/Romneya_coulteri",
"blue-eyed grass": "https://en.wikipedia.org/wiki/Sisyrinchium_bellum",
"penstemon spectabilis": "https://en.wikipedia.org/wiki/Penstemon_spectabilis",
"seaside daisy": "https://en.wikipedia.org/wiki/Erigeron_glaucus",
"sticky monkeyflower": "https://en.wikipedia.org/wiki/Diplacus_aurantiacus",
"tidy tips": "https://en.wikipedia.org/wiki/Layia_platyglossa",
"wild cucumber": "https://en.wikipedia.org/wiki/Marah_(plant)",
"douglas iris": "https://en.wikipedia.org/wiki/Iris_douglasiana",
"goldfields coreopsis": "https://en.wikipedia.org/wiki/Coreopsis"
}
def on_queue_update(update):
if isinstance(update, fal_client.InProgress):
for log in update.logs:
print(log["message"])
def process_image(img):
# First do the classification
pred, idx, probs = learn.predict(img)
classification_results = dict(zip(search_terms_wikipedia.keys(), map(float, probs)))
# Get Wikipedia URL for the predicted class
predicted_class = max(classification_results.items(), key=lambda x: x[1])[0]
wiki_url = search_terms_wikipedia.get(predicted_class, "No Wikipedia entry found.")
# Generate FLUX image
result = fal_client.subscribe(
"fal-ai/flux/schnell",
arguments={
"prompt": f"A detailed, artistic interpretation of {predicted_class} flower in natural setting",
"image_size": "256x256" # Low res for testing
},
with_logs=True,
on_queue_update=on_queue_update,
)
# Convert the image data
image_data = base64.b64decode(result['image'])
generated_image = Image.open(io.BytesIO(image_data))
return classification_results, generated_image, wiki_url
# Load the learner
learn = load_learner('export.pkl')
# Create Gradio interface
with gr.Blocks() as demo:
with gr.Row():
input_image = gr.Image(height=192, width=192, label="Upload Image for Classification")
with gr.Row():
with gr.Column():
label_output = gr.Label(label="Classification Results")
wiki_output = gr.Textbox(label="Wikipedia Article Link", lines=1)
generated_image = gr.Image(label="AI Generated Interpretation")
# Example images
examples = [
'https://www.deserthorizonnursery.com/wp-content/uploads/2024/03/Brittlebush-Encelia-Farinosa-desert-horizon-nursery.jpg',
'https://cdn.mos.cms.futurecdn.net/VJE7gSuQ9KWbkqEsWgX5zS.jpg'
]
gr.Examples(examples, input_image)
# Set up event handler
input_image.change(
fn=process_image,
inputs=input_image,
outputs=[label_output, generated_image, wiki_output]
)
demo.launch(inline=False) |