import datasets
from transformers import pipeline
from transformers.pipelines.pt_utils import KeyDataset
from tqdm.auto import tqdm

pipe = pipe = pipeline("token-classification", model="erdometo/xlm-roberta-base-finetuned-TQuad2")
dataset = datasets.load_dataset("superb", name="asr", split="test")

for out in tqdm(pipe(KeyDataset(dataset, "file"))):
    print(out)

    

import gradio as gr
from transformers import pipeline, AutoModelForQuestionAnswering, AutoTokenizer, AutoModelForTokenClassification

# Load your custom model and tokenizer
qa_model_name = "erdometo/xlm-roberta-base-finetuned-TQuad2"
token_classification_model_name = "FacebookAI/xlm-roberta-large-finetuned-conll03-german"

qa_model = AutoModelForQuestionAnswering.from_pretrained(qa_model_name)
qa_tokenizer = AutoTokenizer.from_pretrained(qa_model_name)

token_classification_model = AutoModelForTokenClassification.from_pretrained(token_classification_model_name)
token_classification_tokenizer = AutoTokenizer.from_pretrained(token_classification_model_name)
# Define a function for inference based on pipeline type
def predict(pipeline_type, question, context):
    if pipeline_type == "question-answering":
        qa_pipeline = pipeline("question-answering", model=qa_model, tokenizer=qa_tokenizer)
        result = qa_pipeline(question=question, context=context)
        response = [(result['answer'], result['score'])]
        return response
    elif pipeline_type == "token-classification":
        token_classification_pipeline = pipeline("token-classification", model=token_classification_model, tokenizer=token_classification_tokenizer)
        result = token_classification_pipeline(context)
        highlighted_text = {"text": context, "entities": result} 
        return gr.HighlightedText(highlighted_text)

# Create a Gradio Interface with dropdown and two text inputs
iface = gr.Interface(
    fn=predict,
    inputs=[
        gr.Dropdown(choices=["question-answering", "token-classification"], label="Choose Pipeline"),
        "text",
        "text"
    ],
    outputs=gr.Highlight()
)

# Launch the interface
iface.launch()