app.py

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

# Load model
learn = load_learner('resnet50_30_categories.pkl')

# Wikipedia links dictionary
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",
    "delphinium": "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"
}

# Prompt templates for AI generation
prompt_templates = [
    "A dreamy watercolor scene of a {flower} on a misty morning trail...",
    "A loose, expressive watercolor sketch of a {flower} in a wild meadow...",
    "An artist's nature journal page featuring a detailed {flower} study...",
    "A vibrant plein air painting of a {flower} patch along a coastal trail...",
    "A whimsical mixed-media scene of a {flower} garden at sunrise..."
]

# Local example image paths
example_images = [
    str(Path("example_images/example_1.jpg")),
    str(Path("example_images/example_2.jpg")),
    str(Path("example_images/example_3.jpg")),
    str(Path("example_images/example_4.jpg")),
    str(Path("example_images/example_5.jpg")),
]

# Logging for FAL client
def on_queue_update(update):
    if isinstance(update, fal_client.InProgress):
        for log in update.logs:
            print(log["message"])

# Process image and return classification + AI-generated artwork + Wiki URL
def process_image(img):
    predicted_class, _, probs = learn.predict(img)
    classification_results = dict(zip(learn.dls.vocab, map(float, probs)))
    wiki_url = search_terms_wikipedia.get(predicted_class.lower(), "No Wikipedia entry found.")

    # Generate image via FAL API
    result = fal_client.subscribe(
        "fal-ai/flux/schnell",
        arguments={
            "prompt": random.choice(prompt_templates).format(flower=predicted_class),
            "image_size": "portrait_4_3"
        },
        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, str(wiki_url)

# Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# 🌼 California Native Plant Classifier & AI Art Generator")

    with gr.Row():
        input_image = gr.Image(type="pil", label="Upload a Photo", height=250)

    with gr.Row():
        with gr.Column():
            label_output = gr.Label(label="Classification Results")
            wiki_output = gr.Textbox(label="Wikipedia Link")
        generated_image = gr.Image(label="AI-Generated Artistic Interpretation")

    # Submit button to trigger image processing
    submit_btn = gr.Button("Submit")
    submit_btn.click(fn=process_image, inputs=input_image, outputs=[label_output, generated_image, wiki_output])

    # Examples
    gr.Examples(
        examples=example_images,
        inputs=input_image,
        examples_per_page=6
    )

demo.launch()