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4f7f1a1 | 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 | import streamlit as st
from app import get_pdf_text
from app import get_text_chunks
from app import get_vector_store
from app import user_input
from paper_reading import get_pdf_files
from paper_reading import get_txt_from_pdf
from paper_reading import model_2
def mof_synthesis_processing(df):
"""Placeholder for MOF synthesis processing - replace with actual implementation"""
# This is a simplified placeholder - you'll need to implement the actual processing
st.warning("Full MOF processing implementation needed")
return model_2(df)
def main():
st.set_page_config("Multi-Functional PDF Tool")
# Sidebar for mode selection
app_mode = st.sidebar.selectbox("Choose Application Mode",
["Chat with PDF", "MOF Synthesis Paper Processing"])
if app_mode == "Chat with PDF":
st.header("Chat with PDF using Groq and HuggingFace Embeddings💁")
user_question = st.text_input("Ask a Question from the PDF Files")
if user_question:
user_input(user_question)
with st.sidebar:
st.title("Upload PDFs:")
pdf_docs = st.file_uploader("Upload your PDF Files", accept_multiple_files=True)
if st.button("Submit & Process"):
with st.spinner("Processing..."):
raw_text = get_pdf_text(pdf_docs)
text_chunks = get_text_chunks(raw_text)
get_vector_store(text_chunks)
st.success("PDFs Processed Successfully")
elif app_mode == "MOF Synthesis Paper Processing":
st.title("MOF Synthesis Paper Processing")
# Folder input for PDF processing
folder = st.text_input("Enter the full path to the folder containing PDF files")
if folder:
try:
folder_path = get_pdf_files(folder)
if st.button("Process MOF Synthesis Papers"):
with st.spinner("Extracting text from PDFs..."):
# Extract text from PDFs
pdf_dataframe = get_txt_from_pdf(folder_path)
if not pdf_dataframe.empty:
st.write(f"Extracted text from {len(pdf_dataframe['file name'].unique())} PDFs")
st.write(f"Total pages processed: {len(pdf_dataframe)}")
# Process MOF synthesis papers
with st.spinner("Analyzing MOF Synthesis Papers..."):
processed_df = mof_synthesis_processing(pdf_dataframe)
if not processed_df.empty:
# Display results
st.success("MOF Synthesis Papers Processed Successfully!")
# Option to download processed data
csv = processed_df.to_csv(index=False)
st.download_button(
label="Download Processed Data",
data=csv,
file_name="mof_synthesis_data.csv",
mime="text/csv"
)
# Display first few rows
st.dataframe(processed_df)
else:
st.warning("No MOF synthesis data was extracted.")
else:
st.error("No PDFs found or error in extracting text")
except Exception as e:
st.error(f"An error occurred: {e}")
if __name__ == "__main__":
main() |