app.py

from fastai.vision.all import *
import gradio as gr
import fal_client
from PIL import Image
import io
import base64
import random
import requests

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"
}

# Update prompt templates
prompt_templates = [
    "A cosmic {flower} blooming in space, with petals made of swirling galaxies and nebulae, glowing softly against a backdrop of distant stars.",
    "An enchanted garden filled with a bioluminescent {flower}, each petal radiating vibrant, otherworldly colors, illuminating the dark, mystical forest around them.",
    "A mechanical {flower} with petals made of polished metal and intricate gears, unfolding in a steampunk-inspired futuristic landscape.",
    "A surreal pot of a {flower} where each bloom is a miniature landscape, showing tiny mountains, rivers, and clouds nestled within the petals.",
    "An abstract explosion of a {flower}, blending vibrant colors and fluid shapes in a chaotic, dreamlike composition, evoking movement and emotion."
]

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": random.choice(prompt_templates).format(flower=predicted_class),
            "image_size": "square"
        },
        with_logs=True,
        on_queue_update=on_queue_update,
    )
    
    image_url = result['images'][0]['url']
    response = requests.get(image_url)
    generated_image = Image.open(io.BytesIO(response.content))
    
    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", type="pil")
    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=examples,
        inputs=input_image,
        examples_per_page=5,
        fn=process_image,
        outputs=[label_output, generated_image, wiki_output]
    )
    
    # Set up event handler
    input_image.change(
        fn=process_image,
        inputs=input_image,
        outputs=[label_output, generated_image, wiki_output]
    )

demo.launch(inline=False)