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import streamlit as st |
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import os |
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import PyPDF2 |
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import nltk |
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nltk.download('punkt') |
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from nltk.tokenize import sent_tokenize |
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from sentence_transformers import SentenceTransformer |
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import numpy as np |
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from numpy.linalg import norm |
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url = "https://www.independentschoolparent.com/wp-content/uploads/2018/01/AI.jpg" |
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st.title("AIP-S³") |
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st.write("AI Powered Smart Search System") |
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st.image(url) |
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st.markdown('_Welcome to Question Answering System 🧠 🤖_') |
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a = st.sidebar.radio("SELECT -", ['PDF', 'Website']) |
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model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') |
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class PDFProcessor: |
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def __init__(self): |
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self.filt1_list = [] |
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def process_files(self, uploaded_files): |
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for i in uploaded_files: |
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if i.type == "application/pdf" : |
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reader = PyPDF2.PdfReader(i) |
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text_ext = [] |
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for i in range(len(reader.pages)): |
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pageObj = reader.pages[i] |
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text_ext.append(pageObj.extract_text()) |
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sent_toks = [] |
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for i in text_ext: |
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sent_toks.append(sent_tokenize(i)) |
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concat_list = [j for i in sent_toks for j in i] |
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for i in concat_list: |
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a = (i.replace('\n', ' ')) |
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self.filt1_list.append(a) |
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if a == 'PDF': |
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pdf_processor = PDFProcessor() |
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uploaded_files = st.file_uploader("Upload files - ", accept_multiple_files=True, type=['pdf']) |
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if st.button("Process!"): |
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pdf_processor.process_files(uploaded_files) |
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filt1_list = pdf_processor.filt1_list |
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st.write("Process Completed") |
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query = st.text_input('Ask me anything!', placeholder = 'Type.....') |
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if st.button("Confirm!"): |
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model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') |
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query_embedding = model.encode(query) |
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cosine_lis = [] |
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for i in range(len(pdf_processor.filt1_list)): |
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cosine = np.dot(query_embedding , embeddings[i])/ (norm(query_embedding)*norm(embeddings[i])) |
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cosine_lis.append(cosine) |
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N = 3 |
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list1 = cosine_lis |
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indexes_final= sorted(range(len(list1)), key=lambda i: list1[i], reverse=True)[:N] |
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for i in indexes_final: |
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st.write(filt1_list[i]) |
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st.write("") |
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