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| import gradio as gr | |
| from haystack.nodes import FARMReader, PreProcessor, PDFToTextConverter, TfidfRetriever | |
| from haystack.document_stores import InMemoryDocumentStore | |
| from haystack.pipelines import ExtractiveQAPipeline | |
| document_store = InMemoryDocumentStore() | |
| reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2") | |
| preprocessor = PreProcessor( | |
| clean_empty_lines=True, | |
| clean_whitespace=True, | |
| clean_header_footer=True, | |
| split_by="word", | |
| split_length=100, | |
| split_respect_sentence_boundary=True, | |
| split_overlap=3 | |
| ) | |
| def print_answers(results): | |
| fields = ["answer", "score"] # "context", | |
| answers = results["answers"] | |
| filtered_answers = [] | |
| for ans in answers: | |
| filtered_ans = { | |
| field: getattr(ans, field) | |
| for field in fields | |
| if getattr(ans, field) is not None | |
| } | |
| filtered_answers.append(filtered_ans) | |
| return filtered_answers | |
| def pdf_to_document_store(pdf_file): | |
| document_store.delete_documents() | |
| converter = PDFToTextConverter( | |
| remove_numeric_tables=True, valid_languages=["en"]) | |
| documents = [converter.convert(file_path=pdf_file.name, meta=None)[0]] | |
| preprocessed_docs = preprocessor.process(documents) | |
| document_store.write_documents(preprocessed_docs) | |
| return None | |
| def predict(question, pdf_file): | |
| pdf_to_document_store(pdf_file) | |
| retriever = TfidfRetriever(document_store=document_store) | |
| pipe = ExtractiveQAPipeline(reader, retriever) | |
| result = pipe.run(query=question, params={"Retriever": { | |
| "top_k": 5}, "Reader": {"top_k": 3}}) | |
| answers = print_answers(result) | |
| return answers | |
| title = "Search PDF Business Reports with Sparse Passage Retrieval" | |
| iface = gr.Interface(fn=predict, | |
| inputs=[gr.inputs.Textbox(lines=3, label='Ask an open question starting with ... What/When/Where/Who?'), | |
| gr.inputs.File(file_count="single", | |
| type="file", label="Upload a pdf"), | |
| ], | |
| outputs="text", | |
| title=title, | |
| theme="huggingface", | |
| examples=[['What are the strategic initiatives?', | |
| 'samples/walmart-10k.pdf'], | |
| ['What efforts are being made in regards to digital experiences?', | |
| 'samples/walmart-10k.pdf'], | |
| ["How much does the company grow?", | |
| 'samples/walmart-10k.pdf'], | |
| ['What are the strategic initiatives?', | |
| 'samples/pfizer-10k.pdf'], | |
| ['What efforts are being made in regards to digital experiences?', | |
| 'samples/pfizer-10k.pdf'], | |
| ["How much does the company grow?", | |
| 'samples/pfizer-10k.pdf'], ] | |
| ) | |
| iface.launch() | |