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b1d31ca | 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 | import streamlit as st
from query import query
from extract import extractor
from pdf_to_image import pdf_to_image
from embed import embed
from store import store, load_collection
st.title("Study Notes Tutor")
if "history" not in st.session_state:
st.session_state["history"] = []
question = st.text_input("Ask a question about your study notes:")
if st.button("Ask"):
if question.strip() != "":
history = ""
for item in st.session_state["history"][-5:]: #include last 5 interactions in the history
history += f"User: {item['question']}\nAnswer: {item['answer']}\n\n"
with st.spinner("Thinking..."):
answer = query(question, history)
st.session_state["history"].append({
"question": question,
"answer": answer
})
for item in reversed(st.session_state["history"]):
st.write("**You:**", item["question"])
st.write("**Answer:**")
st.write(item["answer"])
st.write("---")
#sidebar
with st.sidebar:
st.title("Extract Text from PDF")
folder_path = st.file_uploader("Upload a folder of PDFs:", type=["pdf"]) #streamlit doesn't support folder upload, so we will use file uploader for now. User can upload one pdf at a time.
subject = st.text_input("Enter the subject of your notes (e.g. Math, Physics):(It's crucial!)")
if st.button("Extract"):
if folder_path is not None:
with st.spinner("Extracting text from pdf..."):
pdf_to_image(folder_path) #convert pdf to images
with st.spinner("Extracting text from images..."):
extractor() #extract text from images
with st.spinner("Embedding text into vector database..."):
embed() #embed the extracted text into vector database
with st.spinner("Storing embedded vectors in chromadb..."):
store(subject) #store the embedded vectors in chromadb
st.success("Extraction complete! You can now ask questions about your notes.")
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