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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 switcher radio
theme = st.radio("🌓 Select Theme", ["Dark", "Light"], horizontal=True)
# Define colors based on theme
if theme == "Dark":
bg_color = "rgba(0, 0, 0, 0.85)"
app_bg = "rgba(20, 20, 20, 0.6)"
sidebar_bg = "rgba(10, 10, 10, 0.9)"
text_color = "#FFFFFF"
input_bg = "rgba(50, 50, 50, 0.5)"
accent_color = "#2AA198"
else:
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"
# Inject CSS styles
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(255,255,255,0.1);
}}
[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: #bbbbbb;
}}
.stMarkdown, .stText {{
color: {text_color} !important;
}}
.stSuccess {{
background-color: rgba(255, 255, 255, 0.07) !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() # Note: was missing += in your original code
# Split the text 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.")
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