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| import streamlit as st | |
| import llm_part | |
| import os | |
| from langchain_groq import ChatGroq | |
| # Sidebar to select the LLM model | |
| st.sidebar.title("LLM Model Selector") | |
| llm_model = st.sidebar.selectbox("Select LLM Model", ("Google Gemini", "Llama")) | |
| # Define Llama-specific configurations | |
| if llm_model == "Google Gemini": # Check if "Google Gemini" is selected | |
| llm = llm_part.llm_1 # Assign Google Gemini to llm | |
| else: | |
| llm = llm_part.llm_2 # Use the Llama model | |
| # Main app | |
| st.title("Jony's Custom Research Notes Extracted from PDFs Using " + llm_model) | |
| option = st.selectbox("Select PDF Source:", ("Enter URL", "Upload Local File")) | |
| document_text = "" | |
| if option == "Enter URL": | |
| pdf_url = st.text_input("Enter the PDF URL:") | |
| if pdf_url: | |
| try: | |
| with st.spinner("Processing PDF from URL..."): | |
| local_pdf_path = "downloaded_paper.pdf" | |
| llm_part.download_pdf_from_url(pdf_url, local_pdf_path) | |
| document_text = llm_part.extract_text_from_pdf(local_pdf_path) | |
| os.remove(local_pdf_path) | |
| except Exception as e: | |
| st.error(f"Error processing PDF from URL: {e}") | |
| elif option == "Upload Local File": | |
| uploaded_file = st.file_uploader("Choose a PDF file", type="pdf") | |
| if uploaded_file is not None: | |
| try: | |
| with st.spinner("Processing uploaded PDF..."): | |
| local_pdf_path = "uploaded_paper.pdf" | |
| with open(local_pdf_path, "wb") as f: | |
| f.write(uploaded_file.read()) | |
| document_text = llm_part.extract_text_from_pdf(local_pdf_path) | |
| os.remove(local_pdf_path) | |
| except Exception as e: | |
| st.error(f"Error processing uploaded PDF: {e}") | |
| if document_text: | |
| with st.spinner("Generating the summary..."): | |
| query = llm_part.prompt.format(document_text=document_text[:20000]) | |
| result = llm.invoke(query) | |
| st.write("### Summary in Table Format:") | |
| st.write(result.content) | |
| lines = result.content.split('\n') | |
| paragraph_output = [] | |
| for line in lines[2:]: | |
| if "|" not in line or not line.strip(): | |
| continue | |
| parts = [part.strip() for part in line.split("|") if part.strip()] | |
| if len(parts) == 2: | |
| _, details = parts | |
| if "Not specified" in details or "Not mentioned" in details: | |
| continue | |
| details_clean = llm_part.clean_html_tags(details) | |
| paragraph_output.append(details_clean) | |
| paragraph_output = ". ".join(paragraph_output) + "." | |
| paragraph_output = paragraph_output.replace(" ,", ",").replace(" .", ".") | |
| paragraph_output = paragraph_output.replace(". CNN", ". In this approach, CNN").replace("Federated learning (FL)", "The use of Federated Learning (FL)") | |
| paragraph_output = paragraph_output.replace("The use of Federated Learning", "The study explores the use of Federated Learning") | |
| paragraph_output = paragraph_output.replace("In this approach, CNN", "In this approach, a combination of CNN models was used to enhance performance") | |
| paragraph_output = paragraph_output.replace("achieved", "yielded results indicating") | |
| paragraph_output = paragraph_output.replace("slightly lower", "only marginally lower") | |
| query2 = llm_part.prompt2.format(paragraph=paragraph_output) | |
| result2 = llm.invoke(query2) | |
| st.write("### Answer in Paragraph Style:") | |
| st.markdown(""" | |
| <style> | |
| .justified-text { | |
| text-align: justify; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| st.markdown(f"<div class='justified-text'>{result2.content}</div>", unsafe_allow_html=True) | |
| #pip install -r requirements.txt | |