app.py

from haystack.nodes import TextConverter, PDFToTextConverter, DocxToTextConverter, PreProcessor
import gradio as gr
pdf_converter = PDFToTextConverter(remove_numeric_tables=True, valid_languages=["en"])
converted = pdf_converter.convert(file_path="statistics-for-machine-learning.pdf", meta

from haystack.nodes import PreProcessor
preprocessor = PreProcessor(
 split_by="word",
 split_length=200,
 split_overlap=10,
 )
preprocessed = preprocessor.process(converted)   

from haystack.document_stores.faiss import FAISSDocumentStore

document_store = FAISSDocumentStore(faiss_index_factory_str="Flat", return_embedding=True)
document_store.delete_all_documents()
document_store.write_documents(preprocessed)

from haystack.nodes import DensePassageRetriever
from haystack.nodes import FARMReader
retriever = DensePassageRetriever(document_store=document_store)
reader = FARMReader(model_name_or_path='deepset/roberta-base-squad2-distilled', use_gpu=False)
document_store.update_embeddings(retriever)

from haystack.pipelines import ExtractiveQAPipeline
pipeline = ExtractiveQAPipeline(reader, retriever)

questions = [ 'What is linear regression?',
 'What is machine learning?',
 'What are the steps in machine learning model development and deployment?',
 'What is classification?'
 ]
answers = []
for question in questions:
    prediction = pipeline.run(query=question)

    answers.append(prediction)

for answer in answers:
 print('Q:', answer['query'])
 print('A:', answer['answers'][0].answer)
 print('Context: ', answer['answers'][0].context)
 print('score: ',answer['answers'][0].score)
 print('\n')

def correct(question):
    prediction = pipeline.run(query=question)

    return answers.append(prediction)

app_inputs = gr.inputs.File()

interface = gr.Interface(fn=correct, 
                        inputs=[app_inputs,gr.inputs.Textbox(lines=10)],
                         outputs=gr.inputs.Textbox(lines=20), 
                        title='PDF QA system')
interface.launch(share=True)