| import streamlit as st
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| import pdfplumber
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| from transformers import pipeline, RagTokenizer, RagRetriever, RagSequenceForGeneration
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|
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| def preprocess_text(text):
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| text = text.replace('\n', ' ').replace('\r', '')
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| text = ' '.join(text.split())
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| return text
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|
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| st.title("Chat with Your PDF")
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| uploaded_file = st.file_uploader("Choose a PDF file", type="pdf")
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| if uploaded_file is not None:
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| with st.spinner('Reading PDF...'):
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| with pdfplumber.open(uploaded_file) as pdf:
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| text = ""
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| for page in pdf.pages:
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| text += page.extract_text()
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|
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| text = preprocess_text(text)
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| st.success('PDF successfully read and preprocessed!')
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| st.text_area("Extracted Text", text[:1000], height=300)
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| tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
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| retriever = RagRetriever.from_pretrained("facebook/rag-token-nq", use_dummy_dataset=True)
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| rag_model = RagSequenceForGeneration.from_pretrained("facebook/rag-token-nq")
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| input_texts = text.split('. ')
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| input_ids = tokenizer(input_texts, return_tensors="pt", padding=True, truncation=True, max_length=512)
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| context_input_ids = retriever(input_ids.input_ids, input_ids.input_ids, num_beams=2)
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| question = st.text_input("Ask a question about the PDF:")
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| if question:
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| with st.spinner('Searching for answer...'):
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| question_ids = tokenizer(question, return_tensors="pt")['input_ids']
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| generated = rag_model.generate(input_ids=context_input_ids.input_ids, context_input_ids=question_ids, num_beams=2)
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| rag_answer = tokenizer.decode(generated[0], skip_special_tokens=True)
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| st.write(rag_answer)
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