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import streamlit as st
import PyPDF2
from transformers import AutoTokenizer, AutoModel
import torch
import numpy as np
import faiss
from gtts import gTTS
import os
# Initialize the model and tokenizer
model_name = "sentence-transformers/all-MiniLM-L6-v2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)
# Function to get embeddings
def get_embedding(text):
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
embeddings = model(**inputs).last_hidden_state.mean(dim=1).numpy()
return embeddings
# Initialize FAISS index
embeddings_dimension = 384 # for MiniLM
index = faiss.IndexFlatL2(embeddings_dimension)
# Title of the app
st.title("Study Assistant for Grade 9")
# File uploader widget
uploaded_file = st.file_uploader("Upload your PDF file", type=["pdf"])
if uploaded_file is not None:
# Read the uploaded PDF file
pdf_reader = PyPDF2.PdfReader(uploaded_file)
text = ""
# Extract text from each page
for page in pdf_reader.pages:
text += page.extract_text() if page.extract_text() else ""
st.subheader("Extracted Text:")
st.write(text)
# Generate embedding for the extracted text
embeddings = get_embedding(text)
index.add(embeddings) # Add embedding to the FAISS index
st.success("Text extracted and embeddings generated!")
# Subject selection and query input
subject = st.selectbox("Select Subject", ["Math", "Science", "English"])
query = st.text_input("Type your query")
if st.button("Submit"):
if query:
# Get embedding for the query
query_embedding = get_embedding(query)
# Search for the nearest neighbors in the FAISS index
D, I = index.search(query_embedding, k=5) # Retrieve top 5 matches
st.subheader("Top Matches:")
for idx in I[0]:
if idx < len(embeddings): # Ensure index is valid
st.write(f"Match Index: {idx}, Distance: {D[0][idx]}") # Display match details
# Convert response to speech
response_text = f"You asked about '{query}' in {subject}. Here are your top matches."
tts = gTTS(text=response_text, lang='en')
tts.save("response.mp3")
# Display audio controls (Streamlit doesn't support direct playback)
st.audio("response.mp3")
st.success("Response generated!")
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