app.py

import gradio as gr
from transformers import pipeline
from datasets import load_dataset
import requests
from bs4 import BeautifulSoup

classification_model = pipeline("text-classification", model="CesarLeblanc/test_model")
mask_model = pipeline("fill-mask", model="CesarLeblanc/fill_mask_model")

dataset = load_dataset("CesarLeblanc/text_classification_dataset")

def return_text(habitat_label, habitat_score, confidence):
    if habitat_score*100 > confidence:
        text = f"This vegetation plot belongs to the habitat {habitat_label} with the probability {habitat_score*100:.2f}%."
    else:
        text = f"We can't assign an habitat to this vegetation plot with a confidence of at least {confidence}%."
    return text
    
def return_habitat_image(habitat_label, habitat_score, confidence):
    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"
    if habitat_score*100 < confidence:
        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 return_species_image(species):
    species = species[0].capitalize() + species[1:]
    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, typology, confidence):
    text = gbif_normalization(text)
    result = classification_model(text)
    habitat_label = result[0]['label']
    habitat_label = dataset['train'].features['label'].names[int(habitat_label.split('_')[1])]
    habitat_score = result[0]['score']
    formatted_output = return_text(habitat_label, habitat_score, confidence)
    image_output = return_habitat_image(habitat_label, habitat_score, confidence)
    return formatted_output, image_output

def masking(text):
    text = gbif_normalization(text)
    masked_text = text + ', [MASK] [MASK]'
    pred = mask_model(masked_text, top_k=1)
    new_species = [pred[i][0]['token_str'] for i in range(len(pred))]
    new_species = ' '.join(new_species)
    text = f"The last species from this vegetation plot is probably {new_species}."
    image = return_species_image(new_species)
    return text, image

with gr.Blocks() as demo:
    gr.Markdown("""<h1 style="text-align: center;">Pl@ntBERT</h1>""")
    
    with gr.Tab("Vegetation plot classification"):
        gr.Markdown("""<h3 style="text-align: center;">Classification of vegetation plots!</h3>""")
        with gr.Row():
            with gr.Column():
                species = gr.Textbox(lines=2, label="Species", placeholder="Enter a list of comma-separated binomial names here.")
                typology = gr.Dropdown(["EUNIS"], value="EUNIS", label="Typology", info="Will add more typologies later!")
                confidence = gr.Slider(0, 100, value=90, label="Confidence", info="Choose the level of confidence for the prediction.")
            with gr.Column():
                text_output_1 = gr.Textbox()
                text_output_2 = gr.Image()
        text_button = gr.Button("Classify")
        gr.Markdown("""<h5 style="text-align: center;">An example of input</h5>""")
        gr.Examples([["sparganium erectum, calystegia sepium, persicaria amphibia", "EUNIS", 90]], [species, typology, confidence], [text_output_1, text_output_2], classification, True)
        
    with gr.Tab("Missing species finding"):
        gr.Markdown("""<h3 style="text-align: center;">Finding the missing species!</h3>""")
        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("""<h5 style="text-align: center;">An example of input</h5>""")
        gr.Examples([["vaccinium myrtillus, dryopteris dilatata, molinia caerulea"]], [species_2], [image_output_1, image_output_2], masking, True)

    text_button.click(classification, inputs=[species, typology, confidence], outputs=[text_output_1, text_output_2])
    image_button.click(masking, inputs=[species_2], outputs=[image_output_1, image_output_2])

demo.launch()