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import streamlit as st |
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import docx |
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import PyPDF2 |
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from transformers import pipeline |
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import tempfile |
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@st.cache_resource |
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def load_pipeline(): |
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return pipeline("question-answering", model="deepset/roberta-base-squad2") |
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qa_pipeline = load_pipeline() |
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def read_pdf(file): |
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text = "" |
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pdf_reader = PyPDF2.PdfReader(file) |
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for page in pdf_reader.pages: |
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text += page.extract_text() + "\n" |
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return text |
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def read_word(file): |
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doc = docx.Document(file) |
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text = "" |
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for para in doc.paragraphs: |
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text += para.text + "\n" |
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return text |
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def extract_text(uploaded_file): |
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file_type = uploaded_file.name.split('.')[-1].lower() |
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if file_type == 'pdf': |
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text = read_pdf(uploaded_file) |
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elif file_type == 'docx': |
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text = read_word(uploaded_file) |
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else: |
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st.error("Unsupported file type. Please upload a PDF or Word file.") |
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text = None |
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return text |
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def main(): |
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st.title("📄 File Reader & Hugging Face Q&A Application") |
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st.write("Upload a PDF or Word file and ask questions based on its content.") |
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uploaded_file = st.file_uploader("Choose a PDF or Word file", type=["pdf", "docx"]) |
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if uploaded_file is not None: |
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with tempfile.NamedTemporaryFile(delete=False) as temp_file: |
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temp_file.write(uploaded_file.read()) |
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temp_file_path = temp_file.name |
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file_text = extract_text(temp_file_path) |
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if file_text: |
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st.text_area("File Content", file_text[:1000] + "... (truncated for display)") |
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question = st.text_input("Ask a question based on the file content:") |
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if st.button("Get Answer"): |
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if question.strip(): |
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try: |
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result = qa_pipeline(question=question, context=file_text) |
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st.success(f"Answer: {result['answer']}") |
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except Exception as e: |
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st.error(f"Error generating answer: {str(e)}") |
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else: |
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st.warning("Please enter a question.") |
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if __name__ == "__main__": |
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main() |
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