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

# Dictionary of plant names and their Wikipedia links
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"
}

# Templates for AI image generation
prompt_templates = [
    "A dreamy watercolor scene of a {flower} on a misty morning trail, with golden sunbeams filtering through towering redwoods, and a curious hummingbird hovering nearby.",
    "A loose, expressive watercolor sketch of a {flower} in a wild meadow, surrounded by dancing butterflies and morning dew drops sparkling like diamonds in the dawn light.",
    "An artist's nature journal page featuring a detailed {flower} study, with delicate ink lines and soft watercolor washes, complete with small sketches of bees and field notes in the margins.",
    "A vibrant plein air painting of a {flower} patch along a coastal hiking trail, with crashing waves and rugged cliffs in the background, painted in bold, energetic brushstrokes.",
    "A whimsical mixed-media scene of a {flower} garden at sunrise, combining loose watercolor washes with detailed botanical illustrations, featuring hidden wildlife and morning fog rolling through the valley."
]

# Example images (using local 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')),
    str(Path('example_images/example_6.jpg'))
]

# Function to handle AI generation progress updates
def on_queue_update(update):
    if isinstance(update, fal_client.InProgress):
        for log in update.logs:
           print(log["message"])

# Main function to process the uploaded image
def process_image(img):
    # Classify the image
    predicted_class, _, probs = learn.predict(img)
    classification_results = dict(zip(learn.dls.vocab, map(float, probs)))
    
    # Get Wikipedia link
    wiki_url = search_terms_wikipedia.get(predicted_class, "No Wikipedia entry found.")
    
    # Generate artistic interpretation by calling the Flux 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,
    )
    
    # Get the generated image
    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

# Function to clear all outputs
def clear_outputs():
    return {
        label_output: None,
        generated_image: None,
        wiki_output: None
    }

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

# Create the web interface
with gr.Blocks() as demo:
    # Input section
    with gr.Row():
        input_image = gr.Image(height=230, width=230, label="Upload Image for Classification", type="pil")
    
    # Output section
    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")
    
    # Add example images using local paths
    gr.Examples(
        examples=example_images,
        inputs=input_image,
        examples_per_page=6,
        fn=process_image,
        outputs=[label_output, generated_image, wiki_output]
    )
    
    # Set up what happens when an image is uploaded or removed
    input_image.change(
        fn=process_image,
        inputs=input_image,
        outputs=[label_output, generated_image, wiki_output]
    )
    
    input_image.clear(
        fn=clear_outputs,
        inputs=[],
        outputs=[label_output, generated_image, wiki_output]
    )

# Start the application
demo.launch(inline=False)