app.py

import gradio as gr
from transformers import pipeline
import requests
from bs4 import BeautifulSoup
import random

classification_model = pipeline("text-classification", model="plantbert_text_classification_model", tokenizer="plantbert_text_classification_model")
mask_model = pipeline("fill-mask", model="plantbert_fill_mask_model", tokenizer="plantbert_fill_mask_model", top_k=14189)

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.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, typology, confidence):
    text = gbif_normalization(text)
    result = classification_model(text)
    habitat_label = result[0]['label']
    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)
    
    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} at 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("""<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()