from typing import Dict, Union
from gliner import GLiNER
import gradio as gr

model = GLiNER.from_pretrained("knowledgator/gliner-multitask-large-v0.5").to('cpu')

text1 = """
"I recently purchased the Sony WH-1000XM4 Wireless Noise-Canceling Headphones from Amazon and I must say, I'm thoroughly impressed. The package arrived in New York within 2 days, thanks to Amazon Prime's expedited shipping.

The headphones themselves are remarkable. The noise-canceling feature works like a charm in the bustling city environment, and the 30-hour battery life means I don't have to charge them every day. Connecting them to my Samsung Galaxy S21 was a breeze, and the sound quality is second to none.

I also appreciated the customer service from Amazon when I had a question about the warranty. They responded within an hour and provided all the information I needed.

However, the headphones did not come with a hard case, which was listed in the product description. I contacted Amazon, and they offered a 10% discount on my next purchase as an apology.

Overall, I'd give these headphones a 4.5/5 rating and highly recommend them to anyone looking for top-notch quality in both product and service."""



open_ie_examples = [
    [
        f"Extract all brands, please",
        text1,
        0.5,
        False
    ]]

def merge_entities(entities):
    if not entities:
        return []
    merged = []
    current = entities[0]
    for next_entity in entities[1:]:
        if next_entity['entity'] == current['entity'] and (next_entity['start'] == current['end'] + 1 or next_entity['start'] == current['end']):
            current['word'] += ' ' + next_entity['word']
            current['end'] = next_entity['end']
        else:
            merged.append(current)
            current = next_entity
    merged.append(current)
    return merged

def process(
    prompt:str, text, threshold: float, nested_ner: bool, labels: str = ["match"]
) -> Dict[str, Union[str, int, float]]:
    text = prompt + "\n" + text
    r = {
        "text": text,
        "entities": [
            {
                "entity": entity["label"],
                "word": entity["text"],
                "start": entity["start"],
                "end": entity["end"],
                "score": 0,
            }
            for entity in model.predict_entities(
                text, labels, flat_ner=not nested_ner, threshold=threshold
            )
        ],
    }
    r["entities"] =  merge_entities(r["entities"])
    return r

with gr.Blocks(title="Open Information Extracting") as open_ie_interface:
    prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here")
    input_text = gr.Textbox(label="Text input", placeholder="Enter your text here")
    threshold = gr.Slider(0, 1, value=0.3, step=0.01, label="Threshold", info="Lower the threshold to increase how many entities get predicted.")
    nested_ner = gr.Checkbox(label="Nested NER", info="Allow for nested NER?")
    output = gr.HighlightedText(label="Predicted Entities")
    submit_btn = gr.Button("Submit")
    
    theme=gr.themes.Base()

    input_text.submit(fn=process, inputs=[prompt, input_text, threshold, nested_ner], outputs=output)
    prompt.submit(fn=process, inputs=[prompt, input_text, threshold, nested_ner], outputs=output)
    threshold.release(fn=process, inputs=[prompt, input_text, threshold, nested_ner], outputs=output)
    submit_btn.click(fn=process, inputs=[prompt, input_text, threshold, nested_ner], outputs=output)
    nested_ner.change(fn=process, inputs=[prompt, input_text, threshold, nested_ner], outputs=output)


if __name__ == "__main__":
    
    open_ie_interface.launch()