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

classifier = pipeline("text-classification", model="CesarLeblanc/test_model")
dataset = load_dataset("CesarLeblanc/text_classification_dataset")

def text_classification(text):
    result = classifier(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 = f"This vegetation plot belongs to the habitat {habitat_label} with the probability {habitat_score*100:.2f}%"
    floraveg_url = f"https://floraveg.eu/habitat/overview/{habitat_label}"
    response = requests.get(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_output = gr.Image(value=image_url)
    return formatted_output, image_output

examples=["sparganium erectum, calystegia sepium, persicaria amphibia", "thinopyrum junceum, cakile maritima"]

io = gr.Interface(fn=text_classification, 
                         inputs= gr.Textbox(lines=2, label="List of comma-separated binomial names of species (see examples)", placeholder="Enter species here..."), 
                         outputs=[gr.Textbox(lines=2, label="Vegetation Plot Classification Result"), "image"],
                         title="Pl@ntBERT",
                         description="Vegetation Plot Classification: enter the species found in a vegetation plot and see its EUNIS habitat!",
                         examples=examples)

io.launch()