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import gradio as gr |
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import pandas as pd |
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from transformers import pipeline, AutoModelForQuestionAnswering, AutoTokenizer, AutoModelForTokenClassification |
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qa_model_name = "erdometo/xlm-roberta-base-finetuned-TQuad2" |
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token_classification_model_name = "akdeniz27/convbert-base-turkish-cased-ner" |
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qa_model = AutoModelForQuestionAnswering.from_pretrained(qa_model_name) |
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qa_tokenizer = AutoTokenizer.from_pretrained(qa_model_name) |
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token_classification_model = AutoModelForTokenClassification.from_pretrained(token_classification_model_name) |
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token_classification_tokenizer = AutoTokenizer.from_pretrained(token_classification_model_name) |
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def tabulazier(output): |
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output_comb = [] |
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for ind, entity in enumerate(output): |
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if ind == 0: |
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output_comb.append(entity) |
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elif output[ind]["start"] == output[ind-1]["end"] and output[ind]["entity_group"] == output[ind-1]["entity_group"]: |
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output_comb[-1]["word"] = output_comb[-1]["word"] + output[ind]["word"] |
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output_comb[-1]["end"] = output[ind]["end"] |
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else: |
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output_comb.append(entity) |
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df = pd.DataFrame(output_comb) |
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df['word'] = df['word'].str.replace('#', '') |
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return df |
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def predict(pipeline_type, question, context): |
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if pipeline_type == "question-answering": |
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qa_pipeline = pipeline("question-answering", model=qa_model, tokenizer=qa_tokenizer) |
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result = qa_pipeline(question=question, context=context) |
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response = [(result['answer'], result.get('score', None))] |
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return [response, response] |
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elif pipeline_type == "token-classification": |
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token_classification_pipeline = pipeline("ner", model=token_classification_model, tokenizer=token_classification_tokenizer, aggregation_strategy="simple") |
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result = token_classification_pipeline(context) |
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highlighted_text = {"text": context, "entities": result} |
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table=tabulazier(result) |
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return [gr.HighlightedText(highlighted_text), table] |
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iface = gr.Interface( |
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fn=predict, |
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inputs=[ |
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gr.Dropdown(choices=["question-answering", "token-classification"], label="Choose Pipeline"), |
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"text", |
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"text" |
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], |
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outputs=[gr.Highlight(), gr.Dataframe()] |
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) |
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iface.launch(debug=False) |
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