import gradio as gr from transformers import pipeline import requests from bs4 import BeautifulSoup # Initialize models classification_model = pipeline("text-classification", model="models/text_classification_model", tokenizer="models/text_classification_model") mask_model = pipeline("fill-mask", model="models/fill_mask_model", tokenizer="models/fill_mask_model", top_k=100) def return_habitat_image(habitat_label): floraveg_url = f"https://floraveg.eu/habitat/overview/{habitat_label}" response = requests.get(floraveg_url) if response.status_code == 200: soup = BeautifulSoup(response.text, 'html.parser') img_tag = soup.find('img', src=lambda x: x and x.startswith("https://files.ibot.cas.cz/cevs/images/syntaxa/thumbs/")) if img_tag: image_url = img_tag['src'] else: image_url = "https://www.salonlfc.com/wp-content/uploads/2018/01/image-not-found-scaled-1150x647.png" else: image_url = "https://www.salonlfc.com/wp-content/uploads/2018/01/image-not-found-scaled-1150x647.png" image_url = "https://www.commissionoceanindien.org/wp-content/uploads/2018/07/plantnet.jpg" # While we don't have the rights image = gr.Image(value=image_url) return image def return_species_image(species): species = species.capitalize() floraveg_url = f"https://floraveg.eu/taxon/overview/{species}" response = requests.get(floraveg_url) if response.status_code == 200: soup = BeautifulSoup(response.text, 'html.parser') img_tag = soup.find('img', src=lambda x: x and x.startswith("https://files.ibot.cas.cz/cevs/images/taxa/large/")) if img_tag: image_url = img_tag['src'] else: image_url = "https://www.salonlfc.com/wp-content/uploads/2018/01/image-not-found-scaled-1150x647.png" else: image_url = "https://www.salonlfc.com/wp-content/uploads/2018/01/image-not-found-scaled-1150x647.png" image_url = "https://www.commissionoceanindien.org/wp-content/uploads/2018/07/plantnet.jpg" image = gr.Image(value=image_url) return image def gbif_normalization(text): base = "https://api.gbif.org/v1" api = "species" function = "match" parameter = "name" url = f"{base}/{api}/{function}?{parameter}=" all_species = text.split(',') all_species = [species.strip() for species in all_species] species_gbif = [] for species in all_species: url = url.replace(url.partition('name')[2], f'={species}') r = requests.get(url) r = r.json() if 'species' in r: r = r["species"] else: r = species species_gbif.append(r) text = ", ".join(species_gbif) text = text.lower() return text def classification(text): text = gbif_normalization(text) result = classification_model(text) habitat_label = result[0]['label'] text = f"This vegetation plot belongs to the habitat {habitat_label}." image_output = return_habitat_image(habitat_label) return text, image_output def masking(text): text = gbif_normalization(text) max_score = 0 best_prediction = None best_position = None best_sentence = None # Case for the first position masked_text = "[MASK], " + ', '.join(text.split(', ')) i = 0 while True: prediction = mask_model(masked_text)[i] species = prediction['token_str'] if species in text.split(', '): i+=1 else: break score = prediction['score'] sentence = prediction['sequence'] if score > max_score: max_score = score best_prediction = species best_position = 0 best_sentence = sentence # Loop through each position in the middle of the sentence for i in range(1, len(text.split(', '))): masked_text = ', '.join(text.split(', ')[:i]) + ', [MASK], ' + ', '.join(text.split(', ')[i:]) i = 0 while True: prediction = mask_model(masked_text)[i] species = prediction['token_str'] if species in text.split(', '): i+=1 else: break score = prediction['score'] sentence = prediction['sequence'] # Update best prediction and position if score is higher if score > max_score: max_score = score best_prediction = species best_position = i best_sentence = sentence # Case for the last position masked_text = ', '.join(text.split(', ')) + ', [MASK]' i = 0 while True: prediction = mask_model(masked_text)[i] species = prediction['token_str'] if species in text.split(', '): i+=1 else: break score = prediction['score'] sentence = prediction['sequence'] if score > max_score: max_score = score best_prediction = species best_position = len(text.split(', ')) best_sentence = sentence text = f"The most likely missing species is {best_prediction} (position {best_position}).\nThe new vegetation plot is {best_sentence}." image = return_species_image(best_prediction) return text, image with gr.Blocks() as demo: gr.Markdown("""

Pl@ntBERT

""") with gr.Tab("Vegetation plot classification"): gr.Markdown("""

Classification of vegetation plots!

""") with gr.Row(): with gr.Column(): species = gr.Textbox(lines=2, label="Species", placeholder="Enter a list of comma-separated binomial names here.") top_k = gr.Slider(1, 5, value=1, label="Top-k", info="Choose between 1 and 5.") with gr.Column(): text_output_1 = gr.Textbox() text_output_2 = gr.Image() text_button = gr.Button("Classify") gr.Markdown("""
An example of input
""") gr.Examples([["sparganium erectum, calystegia sepium, persicaria amphibia", 1]], [species, top_k], [text_output_1, text_output_2], classification, True) with gr.Tab("Missing species finding"): gr.Markdown("""

Finding the missing species!

""") with gr.Row(): species_2 = gr.Textbox(lines=2, label="Species", placeholder="Enter a list of comma-separated binomial names here.") with gr.Column(): image_output_1 = gr.Textbox() image_output_2 = gr.Image() image_button = gr.Button("Find") gr.Markdown("""
An example of input
""") gr.Examples([["vaccinium myrtillus, dryopteris dilatata, molinia caerulea"]], [species_2], [image_output_1, image_output_2], masking, True) text_button.click(classification, inputs=[species], outputs=[text_output_1, text_output_2]) image_button.click(masking, inputs=[species_2], outputs=[image_output_1, image_output_2]) demo.launch()