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import os
import streamlit as st
import PyPDF2
import docx
from sentence_transformers import SentenceTransformer
from groq import Groq
from transformers import pipeline
from langchain.text_splitter import RecursiveCharacterTextSplitter
# Set up Groq API
client = Groq(api_key=os.environ.get("Groq_Api"))
# Load embedding model
embedder = SentenceTransformer("all-MiniLM-L6-v2")
# Title and UI
st.set_page_config(page_title="A&Q From a File", page_icon="📖")
st.title("📖 A&Q From a File")
# File Upload
uploaded_file = st.file_uploader("Upload a PDF or DOCX file", type=["pdf", "docx"])
if uploaded_file:
text = ""
# Extract text from PDF
if uploaded_file.type == "application/pdf":
pdf_reader = PyPDF2.PdfReader(uploaded_file)
for page in pdf_reader.pages:
text += page.extract_text() + "\n"
# Extract text from DOCX
elif uploaded_file.type == "application/vnd.openxmlformats-officedocument.wordprocessingml.document":
doc = docx.Document(uploaded_file)
for para in doc.paragraphs:
text += para.text + "\n"
# Chunking the text
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=500, chunk_overlap=50
)
chunks = text_splitter.split_text(text)
# Embed chunks
embeddings = embedder.encode(chunks, convert_to_tensor=True)
# Query Input
user_query = st.text_input("Ask a question about the file:")
if user_query:
# Query Groq API
chat_completion = client.chat.completions.create(
messages=[
{"role": "user", "content": f"Answer this question based on the uploaded document: {user_query}"}
],
model="llama-3.3-70b-versatile",
)
# Display answer
st.subheader("Answer:")
st.write(chat_completion.choices[0].message.content)
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