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" } flowers_endangerment = { "Blazing Star": "Not considered endangered.", "Bristlecone Pine": "Least Concern (stable population).", "California Bluebell": "Not listed as endangered or threatened.", "California Buckeye": "Not endangered.", "California Buckwheat": "Generally secure.", "California Fuchsia": "Not endangered overall; some subspecies at risk.", "California Checkerbloom": "Not generally endangered; some subspecies critically imperiled.", "California Lilac": "Most species not endangered; some species are endangered.", "California Poppy": "Generally secure; some subspecies face threats.", "California Sagebrush": "Considered secure (G4-G5).", "California Wild Grape": "Apparently secure (G4).", "California Wild Rose": "Secure (G4).", "Coyote Mint": "Varies by species; some federally listed as endangered.", "Elegant Clarkia": "Secure (G5).", "Baby Blue Eyes": "Secure.", "Hummingbird Sage": "Apparently secure (G4).", "Delphinium": "Varies by species; some are endangered.", "Matilija Poppy": "Not currently endangered.", "Blue-Eyed Grass": "Not endangered.", "Penstemon Spectabilis": "Not endangered.", "Seaside Daisy": "Not endangered.", "Sticky Monkeyflower": "Not endangered.", "Tidy Tips": "Generally not endangered; some subspecies may be at risk.", "Wild Cucumber": "Generally not endangered.", "Douglas Iris": "Not endangered.", "Goldfields Coreopsis": "Varies by species; many not endangered." } def get_status(flower_name): """Return the endangerment status of a given flower name.""" return flowers_endangerment.get(flower_name, "Flower not found in database.") # 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')) ] # 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"]) def get_status(flower_name): """Return the endangerment status of a given flower name.""" # Normalize input for dictionary lookup normalized_name = flower_name.title() return flowers_endangerment.get(normalized_name, "Flower not found in database.") # Main function to process the uploaded image def process_image(img): print("Starting prediction...") predicted_class, _, probs = learn.predict(img) print(f"Prediction complete: {predicted_class}") 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.") # Get endangerment status endangerment_status = get_status(predicted_class) print(f"Status found: {endangerment_status}") # Generate artistic interpretation print("Sending request to 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, ) print("FAL API responded") # Retrieve image image_url = result['images'][0]['url'] print(f"Image URL: {image_url}") response = requests.get(image_url) generated_image = Image.open(io.BytesIO(response.content)) print("Image retrieved and ready to return") return classification_results, generated_image, wiki_url, endangerment_status # Function to clear all outputs def clear_outputs(): return { label_output: None, generated_image: None, wiki_output: None, status_output: None # ← NEW } # 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 # 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) status_output = gr.Textbox(label="Endangerment Status", lines=1) # ← NEW 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, status_output] # ← UPDATED ) input_image.change( fn=process_image, inputs=input_image, outputs=[label_output, generated_image, wiki_output, status_output] # ← UPDATED ) input_image.clear( fn=clear_outputs, inputs=[], outputs=[label_output, generated_image, wiki_output, status_output] # ← UPDATED ) # Start the application demo.launch(inline=False)