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76e5922 e45bc04 76e5922 e45bc04 2251097 e45bc04 2251097 e45bc04 2251097 e45bc04 2251097 e45bc04 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 | import streamlit as st
from PyPDF2 import PdfReader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
import os
from langchain_groq import ChatGroq
from langchain.chains.question_answering import load_qa_chain
os.environ["GROQ_API_KEY"] = "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"
# Page config
st.set_page_config(page_title="📄 Chat with PDF", layout="wide")
#theme
bg_color = "#f0f2f6"
app_bg = "rgba(255, 255, 255, 0.6)"
sidebar_bg = "rgba(255, 255, 255, 0.9)"
text_color = "#000000"
input_bg = "rgba(240, 240, 240, 0.9)"
accent_color = "#2AA198"
# CSS
st.markdown(f"""
<style>
body {{
background: {bg_color};
font-family: 'Segoe UI', sans-serif;
}}
.stApp {{
background: {app_bg};
backdrop-filter: blur(15px);
padding: 2rem;
border-radius: 16px;
margin: 2rem auto;
max-width: 1000px;
color: {text_color};
}}
h1, h2, h3, h4, .stRadio label, label {{
color: {text_color} !important;
font-weight: 600;
}}
[data-testid="stSidebar"] {{
background: {sidebar_bg} !important;
color: {text_color} !important;
backdrop-filter: blur(10px);
border-right: 1px solid rgba(0,0,0,0.1);
}}
[data-testid="stSidebar"] * {{
color: {text_color} !important;
}}
.stTextInput input {{
background-color: {input_bg};
color: {text_color} !important;
border: 1px solid #ccc;
border-radius: 8px;
padding: 0.5rem;
}}
.stTextInput input::placeholder {{
color: #777777;
}}
.stMarkdown, .stText, .stSubheader, .stHeader, p {{
color: {text_color} !important;
}}
.stSuccess {{
background-color: rgba(0, 0, 0, 0.03) !important;
border-left: 6px solid {accent_color};
padding: 1rem;
border-radius: 12px;
color: {text_color} !important;
font-size: 1.05rem;
}}
button {{
background-color: {accent_color} !important;
color: white !important;
border-radius: 8px;
padding: 0.5rem 1rem;
border: none;
}}
.stRadio > label {{
font-size: 1rem;
font-weight: 600;
color: {text_color} !important;
}}
.stRadio div[role="radiogroup"] > label {{
color: {text_color} !important;
font-weight: 500;
}}
</style>
""", unsafe_allow_html=True)
# Sidebar UI
with st.sidebar:
st.markdown("### 📁 Upload PDF")
file = st.file_uploader("Upload your PDF", type="pdf")
st.write("Then ask your question below!")
# Main UI
st.markdown(f"<h1 style='text-align:center;'>✨ Chat With Your PDF</h1>", unsafe_allow_html=True)
if file is not None:
pdf_pages = PdfReader(file)
text = ""
for page in pdf_pages.pages:
text += page.extract_text()
# Split into chunks
text_splitter = RecursiveCharacterTextSplitter(
separators=["\n"],
chunk_size=1000,
chunk_overlap=150,
length_function=len
)
chunks = text_splitter.split_text(text)
# Generate Embeddings
model_name = "sentence-transformers/all-mpnet-base-v2"
embeddings = HuggingFaceEmbeddings(model_name=model_name)
# Create vector store
vector_store = FAISS.from_texts(chunks, embeddings)
# Query input
user_query = st.text_input("Ask a question about the file")
if user_query:
match = vector_store.similarity_search(user_query)
llm = ChatGroq(
model_name="llama-3.1-8b-instant",
temperature=0.0,
max_retries=2
)
chain = load_qa_chain(llm, chain_type="stuff")
response = chain.run(input_documents=match, question=user_query)
st.subheader("✅ Response")
st.markdown(f"<div style='color:{text_color};'>{response}</div>", unsafe_allow_html=True)
else:
st.info("Please upload a PDF file to get started.") |