Delete app.py

app.py

@@ -1,284 +0,0 @@
-from utils.check_pydantic_version import use_pydantic_v1
-use_pydantic_v1() #This function has to be run before importing haystack. as haystack requires pydantic v1 to run
-
-
-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
-
-from datetime import datetime
-import streamlit.components.v1 as components
-import streamlit_authenticator as stauth
-import pickle
-
-from streamlit_modal import Modal
-import numpy as np
-
-
-
-names = ['mlreply']
-usernames = ['docwhiz']
-with open('hashed_password.pkl','rb') as f:
-    hashed_passwords = pickle.load(f)
-
-
-
-# Whether the file upload should be enabled or not
-DISABLE_FILE_UPLOAD = bool(os.getenv("DISABLE_FILE_UPLOAD"))
-
-
-def show_documents_list(retrieved_documents):
-    data = []
-    for i, document in enumerate(retrieved_documents):
-        data.append([document.meta['name']])
-    df = pd.DataFrame(data, columns=['Uploaded Document Name'])
-    df.drop_duplicates(subset=['Uploaded Document Name'], inplace=True)
-    df.index = np.arange(1, len(df) + 1)
-    return df
-
-# Define a function to handle file uploads
-def upload_files():
-    uploaded_files = upload_container.file_uploader(
-            "upload", type=["pdf", "txt", "docx"], accept_multiple_files=True, label_visibility="hidden", key=1
-        )
-    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)
-
-
-# Define a function to upload the documents to haystack document store
-def upload_document():
-    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)
-                    #upload_container.write(str(data_file.name) + "    ✅ ")
-                except Exception as e:
-                    upload_container.write(str(data_file.name) + "    ❌ ")
-                    upload_container.write("_This file could not be parsed, see the logs for more information._")
-
-# Define a function to reset the documents in haystack document store
-def reset_documents():
-    print('\nReseting documents list at ' + str(datetime.now()) + '\n')
-    st.session_state.data_files = None
-    document_store.delete_documents()
-
-try:
-    args = parser.parse_args()
-    preprocesor = start_preprocessor_node()
-    document_store = start_document_store(type=args.store)
-    document_store.get_all_documents()
-    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)
-
-    authenticator = stauth.Authenticate(names, usernames, hashed_passwords, "document_search", "random_text", cookie_expiry_days=1)
-
-    name, authentication_status, username = authenticator.login("Login", "main")
-
-    if authentication_status == False:
-        st.error("Username/Password is incorrect")
-
-    if authentication_status == None:
-        st.warning("Please enter your username and password")
-
-    if authentication_status:
-
-        # Sidebar for Task Selection
-        st.sidebar.header('Options:')
-
-        # OpenAI Key Input
-        openai_key = st.sidebar.text_input("Enter LLM-authorization 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()
-
-        modal = Modal("Manage Files", key="demo-modal")
-        open_modal = st.sidebar.button("Manage Files", use_container_width=True)
-        if open_modal:
-            modal.open()
-
-        st.write('# ' + args.name)
-        if modal.is_open():
-            with modal.container():
-                if not DISABLE_FILE_UPLOAD:
-                    upload_container = st.container()
-                    data_files = upload_files()
-                    upload_document()
-                    st.session_state.sidebar_state = 'collapsed'
-                st.table(show_documents_list(document_store.get_all_documents()))
-        
-        # File upload block
-       # if not DISABLE_FILE_UPLOAD:
-        #    upload_container = st.sidebar.container()
-         #   upload_container.write("## File Upload:")
-          #  data_files = upload_files()
-            # Button to update files in the documentStore
-           # upload_container.button('Upload Files', on_click=upload_document, args=())
-
-        # Button to reset the documents in DocumentStore
-        st.sidebar.button("Reset documents", on_click=reset_documents, args=(), use_container_width=True)
-
-        if "question" not in st.session_state:
-            st.session_state.question = ""
-        # Search bar
-        question = st.text_input("Question", value=st.session_state.question, max_chars=100, on_change=reset_results, label_visibility="hidden")
-
-        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(
-                                "🤔 &nbsp;&nbsp; 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)