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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 = "akdeniz27/convbert-base-turkish-cased-ner"
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)
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() |