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import gradio as gr
import pandas as pd
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 tabulazier(output):
output_comb = []
for ind, entity in enumerate(output):
if ind == 0:
output_comb.append(entity)
elif output[ind]["start"] == output[ind-1]["end"] and output[ind]["entity_group"] == output[ind-1]["entity_group"]:
output_comb[-1]["word"] = output_comb[-1]["word"] + output[ind]["word"]
output_comb[-1]["end"] = output[ind]["end"]
else:
output_comb.append(entity)
df = pd.DataFrame(output_comb)
df['word'] = df['word'].str.replace('#', '')
return df
# 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.get('score', None))]
return [response, response]
elif pipeline_type == "token-classification":
token_classification_pipeline = pipeline("ner", model=token_classification_model, tokenizer=token_classification_tokenizer, aggregation_strategy="simple")
result = token_classification_pipeline(context)
highlighted_text = {"text": context, "entities": result}
table=tabulazier(result)
return [gr.HighlightedText(highlighted_text), table]
# 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(), gr.Dataframe()]
)
# Launch the interface
iface.launch(debug=False)
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