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| #import pydantic | |
| #module_file_path = pydantic.__file__ | |
| #module_file_path = module_file_path.split('pydantic')[0] + 'haystack' | |
| #import os | |
| #import fileinput | |
| #def replace_string_in_files(folder_path, old_str, new_str): | |
| # for subdir, dirs, files in os.walk(folder_path): | |
| # for file in files: | |
| # file_path = os.path.join(subdir, file) | |
| # Check if the file is a text file (you can modify this condition based on your needs) | |
| # if file.endswith(".txt") or file.endswith(".py"): | |
| # # Open the file in place for editing | |
| # with fileinput.FileInput(file_path, inplace=True) as f: | |
| # for line in f: | |
| # # Replace the old string with the new string | |
| # print(line.replace(old_str, new_str), end='') | |
| #with open('change_log.txt','r') as f: | |
| # status = f.readlines() | |
| #if status[-1] != 'changed': | |
| # replace_string_in_files(module_file_path, 'from pydantic', 'from pydantic.v1') | |
| # with open('change_log.txt','w'): | |
| # f.write('changed') | |
| from operator import index | |
| import streamlit as st | |
| import logging | |
| import os | |
| from annotated_text import annotation | |
| from json import JSONDecodeError | |
| from markdown import markdown | |
| from utils.config import parser | |
| from utils.haystack import start_document_store, query, initialize_pipeline, start_preprocessor_node, start_retriever, start_reader | |
| from utils.ui import reset_results, set_initial_state | |
| import pandas as pd | |
| import haystack | |
| # Whether the file upload should be enabled or not | |
| DISABLE_FILE_UPLOAD = bool(os.getenv("DISABLE_FILE_UPLOAD")) | |
| # Define a function to handle file uploads | |
| def upload_files(): | |
| uploaded_files = st.sidebar.file_uploader( | |
| "upload", type=["pdf", "txt", "docx"], accept_multiple_files=True, label_visibility="hidden" | |
| ) | |
| return uploaded_files | |
| # Define a function to process a single file | |
| def process_file(data_file, preprocesor, document_store): | |
| # read file and add content | |
| file_contents = data_file.read().decode("utf-8") | |
| docs = [{ | |
| 'content': str(file_contents), | |
| 'meta': {'name': str(data_file.name)} | |
| }] | |
| try: | |
| names = [item.meta.get('name') for item in document_store.get_all_documents()] | |
| #if args.store == 'inmemory': | |
| # doc = converter.convert(file_path=files, meta=None) | |
| if data_file.name in names: | |
| print(f"{data_file.name} already processed") | |
| else: | |
| print(f'preprocessing uploaded doc {data_file.name}.......') | |
| #print(data_file.read().decode("utf-8")) | |
| preprocessed_docs = preprocesor.process(docs) | |
| print('writing to document store.......') | |
| document_store.write_documents(preprocessed_docs) | |
| print('updating emebdding.......') | |
| document_store.update_embeddings(retriever) | |
| except Exception as e: | |
| print(e) | |
| try: | |
| args = parser.parse_args() | |
| preprocesor = start_preprocessor_node() | |
| document_store = start_document_store(type=args.store) | |
| retriever = start_retriever(document_store) | |
| reader = start_reader() | |
| st.set_page_config( | |
| page_title="MLReplySearch", | |
| layout="centered", | |
| page_icon=":shark:", | |
| menu_items={ | |
| 'Get Help': 'https://www.extremelycoolapp.com/help', | |
| 'Report a bug': "https://www.extremelycoolapp.com/bug", | |
| 'About': "# This is a header. This is an *extremely* cool app!" | |
| } | |
| ) | |
| st.sidebar.image("ml_logo.png", use_column_width=True) | |
| # Sidebar for Task Selection | |
| st.sidebar.header('Options:') | |
| # OpenAI Key Input | |
| openai_key = st.sidebar.text_input("Enter OpenAI Key:", type="password") | |
| if openai_key: | |
| task_options = ['Extractive', 'Generative'] | |
| else: | |
| task_options = ['Extractive'] | |
| task_selection = st.sidebar.radio('Select the task:', task_options) | |
| # Check the task and initialize pipeline accordingly | |
| if task_selection == 'Extractive': | |
| pipeline_extractive = initialize_pipeline("extractive", document_store, retriever, reader) | |
| elif task_selection == 'Generative' and openai_key: # Check for openai_key to ensure user has entered it | |
| pipeline_rag = initialize_pipeline("rag", document_store, retriever, reader, openai_key=openai_key) | |
| set_initial_state() | |
| st.write('# ' + args.name) | |
| # File upload block | |
| if not DISABLE_FILE_UPLOAD: | |
| st.sidebar.write("## File Upload:") | |
| #data_files = st.sidebar.file_uploader( | |
| # "upload", type=["pdf", "txt", "docx"], accept_multiple_files=True, label_visibility="hidden" | |
| #) | |
| data_files = upload_files() | |
| if data_files is not None: | |
| for data_file in data_files: | |
| # Upload file | |
| if data_file: | |
| try: | |
| #raw_json = upload_doc(data_file) | |
| # Call the process_file function for each uploaded file | |
| if args.store == 'inmemory': | |
| processed_data = process_file(data_file, preprocesor, document_store) | |
| st.sidebar.write(str(data_file.name) + " ✅ ") | |
| except Exception as e: | |
| st.sidebar.write(str(data_file.name) + " ❌ ") | |
| st.sidebar.write("_This file could not be parsed, see the logs for more information._") | |
| if "question" not in st.session_state: | |
| st.session_state.question = "" | |
| # Search bar | |
| question = st.text_input("", value=st.session_state.question, max_chars=100, on_change=reset_results) | |
| run_pressed = st.button("Run") | |
| run_query = ( | |
| run_pressed or question != st.session_state.question #or task_selection != st.session_state.task | |
| ) | |
| # Get results for query | |
| if run_query and question: | |
| if task_selection == 'Extractive': | |
| reset_results() | |
| st.session_state.question = question | |
| with st.spinner("🔎 Running your pipeline"): | |
| try: | |
| st.session_state.results_extractive = query(pipeline_extractive, question) | |
| st.session_state.task = task_selection | |
| except JSONDecodeError as je: | |
| st.error( | |
| "👓 An error occurred reading the results. Is the document store working?" | |
| ) | |
| except Exception as e: | |
| logging.exception(e) | |
| st.error("🐞 An error occurred during the request.") | |
| elif task_selection == 'Generative': | |
| reset_results() | |
| st.session_state.question = question | |
| with st.spinner("🔎 Running your pipeline"): | |
| try: | |
| st.session_state.results_generative = query(pipeline_rag, question) | |
| st.session_state.task = task_selection | |
| except JSONDecodeError as je: | |
| st.error( | |
| "👓 An error occurred reading the results. Is the document store working?" | |
| ) | |
| except Exception as e: | |
| if "API key is invalid" in str(e): | |
| logging.exception(e) | |
| st.error("🐞 incorrect API key provided. You can find your API key at https://platform.openai.com/account/api-keys.") | |
| else: | |
| logging.exception(e) | |
| st.error("🐞 An error occurred during the request.") | |
| # Display results | |
| if (st.session_state.results_extractive or st.session_state.results_generative) and run_query: | |
| # Handle Extractive Answers | |
| if task_selection == 'Extractive': | |
| results = st.session_state.results_extractive | |
| st.subheader("Extracted Answers:") | |
| if 'answers' in results: | |
| answers = results['answers'] | |
| treshold = 0.2 | |
| higher_then_treshold = any(ans.score > treshold for ans in answers) | |
| if not higher_then_treshold: | |
| st.markdown(f"<span style='color:red'>Please note none of the answers achieved a score higher then {int(treshold) * 100}%. Which probably means that the desired answer is not in the searched documents.</span>", unsafe_allow_html=True) | |
| for count, answer in enumerate(answers): | |
| if answer.answer: | |
| text, context = answer.answer, answer.context | |
| start_idx = context.find(text) | |
| end_idx = start_idx + len(text) | |
| score = round(answer.score, 3) | |
| st.markdown(f"**Answer {count + 1}:**") | |
| st.markdown( | |
| context[:start_idx] + str(annotation(body=text, label=f'SCORE {score}', background='#964448', color='#ffffff')) + context[end_idx:], | |
| unsafe_allow_html=True, | |
| ) | |
| else: | |
| st.info( | |
| "🤔 Haystack is unsure whether any of the documents contain an answer to your question. Try to reformulate it!" | |
| ) | |
| # Handle Generative Answers | |
| elif task_selection == 'Generative': | |
| results = st.session_state.results_generative | |
| st.subheader("Generated Answer:") | |
| if 'results' in results: | |
| st.markdown("**Answer:**") | |
| st.write(results['results'][0]) | |
| # Handle Retrieved Documents | |
| if 'documents' in results: | |
| retrieved_documents = results['documents'] | |
| st.subheader("Retriever Results:") | |
| data = [] | |
| for i, document in enumerate(retrieved_documents): | |
| # Truncate the content | |
| truncated_content = (document.content[:150] + '...') if len(document.content) > 150 else document.content | |
| data.append([i + 1, document.meta['name'], truncated_content]) | |
| # Convert data to DataFrame and display using Streamlit | |
| df = pd.DataFrame(data, columns=['Ranked Context', 'Document Name', 'Content']) | |
| st.table(df) | |
| except SystemExit as e: | |
| os._exit(e.code) | |