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| import streamlit as st | |
| from PyPDF2 import PdfReader | |
| from langchain.embeddings.openai import OpenAIEmbeddings | |
| from langchain.text_splitter import CharacterTextSplitter | |
| from langchain.vectorstores import FAISS | |
| from langchain.chains.question_answering import load_qa_chain | |
| from langchain.llms import OpenAI | |
| # Define a function to load PDF and perform processing | |
| def process_pdf(pdf_path): | |
| pdfreader = PdfReader(pdf_path) | |
| raw_text = '' | |
| for page in pdfreader.pages: | |
| content = page.extract_text() | |
| if content: | |
| raw_text += content | |
| text_splitter = CharacterTextSplitter( | |
| separator="\n", | |
| chunk_size=800, | |
| chunk_overlap=100, | |
| length_function=len, | |
| ) | |
| texts = text_splitter.split_text(raw_text) | |
| embeddings = OpenAIEmbeddings() | |
| document_search = FAISS.from_texts(texts, embeddings) | |
| chain = load_qa_chain(OpenAI(), chain_type="stuff") | |
| return document_search, chain | |
| # Function to get yes/no emoji based on answer content | |
| def get_answer_emoji(answer): | |
| answer = answer.lower() | |
| if "yes" in answer: | |
| return "✅" | |
| elif "no" in answer: | |
| return "❌" | |
| else: | |
| return "🟡" | |
| # Streamlit UI | |
| st.title("OrangePro.AI LLM Output Testing") | |
| # Upload a PDF file | |
| uploaded_pdf_file = st.file_uploader("Upload a PDF file for analysis", type=["pdf"]) | |
| uploaded_text_file = st.file_uploader("Upload a text file with questions (if available)", type=["txt"]) | |
| if uploaded_pdf_file: | |
| st.subheader("Selected PDF Content") | |
| # Display the content of the PDF | |
| pdf_reader, qa_chain = process_pdf(uploaded_pdf_file) | |
| st.write("PDF Content:") | |
| st.text(pdf_reader) | |
| if uploaded_text_file: | |
| st.warning("Questions will be extracted from the uploaded text file. Disabling question input below.") | |
| text_content = uploaded_text_file.read().decode('utf-8') # Decode bytes to string | |
| questions = text_content.splitlines() | |
| else: | |
| # Allow the user to enter a list of questions | |
| questions = st.text_area("Enter a list of questions (one per line):").split('\n') | |
| if st.button("Analyze Questions"): | |
| # Perform question answering for each question | |
| st.subheader("Answers:") | |
| answer_summary = [] | |
| yes_count = 0 | |
| total_questions = len(questions) | |
| for question in questions: | |
| if question.strip() == "": | |
| continue | |
| docs = pdf_reader.similarity_search(question) | |
| answer = qa_chain.run(input_documents=docs, question=question) | |
| emoji = get_answer_emoji(answer) | |
| answer_summary.append([question, answer, emoji]) | |
| if emoji == "✅": | |
| yes_count += 1 | |
| # Calculate and display the percentage of "yes" answers | |
| if total_questions > 0: | |
| yes_percentage = (yes_count / total_questions) * 100 | |
| else: | |
| yes_percentage = 0 | |
| answer_summary.append(["Output Quality (✅/Total Questions)", f"{yes_percentage:.2f}%", ""]) | |
| # Display the summary in a table | |
| st.table(answer_summary) | |
| # About section | |
| st.sidebar.title("About OrangePro AI") | |
| st.sidebar.info( | |
| "OrangePro AI is an artificial intelligence testing and benchmarking platform for large language models (LLMs). It scores model performance based on real-world scenarios, allowing corporate clients such as Fortune 500 companies to choose the best model for their specific use cases." | |
| "\n\n" | |
| "The platform automates scoring, ranking model performance in real-world scenarios and key criteria like hallucinations and safety. OrangePro AI also automatically generates adversarial test suites at a large scale and benchmarks models to help customers identify the best model for specific use cases." | |
| ) | |
| # Footer | |
| st.sidebar.text("Powered by Streamlit and Langchain") | |