Spaces:
Runtime error
Runtime error
| #------------------------------------------------------------------------------ Importing necessary libraries and loading those variables | |
| import streamlit as st | |
| from streamlit_lottie import st_lottie, st_lottie_spinner | |
| import os | |
| from pathlib import Path | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| st.set_page_config(layout="wide", | |
| page_title="RAG With Llama 3", | |
| page_icon="Lottie Animations/LlamaIcon.jpeg") | |
| #------------------------------------------------------------------------------ Importing Backend Functions from _helper_functions py file | |
| from _helper_functions import loadLottieFile | |
| from _helper_functions import initialRampUp | |
| from _helper_functions import navigationBar | |
| from _helper_functions import removedOrAdded | |
| from _helper_functions import buildVectorDatabase | |
| from _helper_functions import RetrievalChainGenerator | |
| from _helper_functions import loadItOnce | |
| #------------------------------------------------------------------------------ Importing Frontend Functions from _helper_functions py file | |
| from _helper_functions import display_main_title | |
| from _helper_functions import display_alert_note | |
| from _helper_functions import display_attention_text | |
| from _helper_functions import display_custom_arrow | |
| from _helper_functions import display_heading_box | |
| from _helper_functions import display_error_message | |
| from _helper_functions import display_small_text | |
| from _helper_functions import display_response_message | |
| from _helper_functions import display_question_box | |
| from _helper_functions import display_allCitations | |
| #------------------------------------------------------------------------------ Loading all lottie animations to showcase progress | |
| cwd = Path.cwd() | |
| filePath = cwd / "Lottie Animations" | |
| llama3 = loadLottieFile(filePath / "llama3.json") | |
| finetuning = loadLottieFile(filePath / "finetuning.json") | |
| forecasting = loadLottieFile(filePath / "forecasting.json") | |
| buildingDatabase = loadLottieFile(filePath / "buildingDatabase.json") | |
| fancyload = loadLottieFile(filePath / "fancyloading.json") | |
| citations = loadLottieFile(filePath / "citations.json") | |
| vdbList = loadLottieFile(filePath / "knowledgeBase1.json") | |
| #------------------------------------------------------------------------------ Declaring Session variables for the app life | |
| if 'initialRampUp' not in st.session_state: | |
| st.session_state.initialRampUp = True | |
| #------------------------------------------------------------------------------ Intro Title | |
| display_main_title("Let's Chat With Llama 3!!!", st) | |
| #------------------------------------------------------------------------------ Navigation Bar | |
| selected_option = navigationBar() | |
| #------------------------------------------------------------------------------ Divider | |
| st.divider() | |
| #------------------------------------------------------------------------------ First Page: Retrieval Augmented Generation | |
| if selected_option == "Retrieval Augmented Generation": | |
| #------------------------------------------------------------------------------ And create a fresh one on Refresh | |
| if st.session_state.initialRampUp: | |
| initialRampUp(llamaAnimation=llama3) | |
| st.session_state.initialRampUp = False | |
| #------------------------------------------------------------------------------ MAIN CONTAINER | |
| with st.container(): | |
| leftCol, upld, rightCol = st.columns((3,4,3)) | |
| #------------------------------------------------------------------------------ Heading in the right column of Parent Container | |
| display_heading_box(message = "Knowledge Base Contents", container= rightCol) | |
| loadItOnce(container=rightCol, animation=vdbList, height=200, quality='low') | |
| #------------------------------------------------------------------------------ Heading in the left column of Parent Container | |
| display_heading_box(message = "Citations for responses", container= leftCol) | |
| loadItOnce(container=leftCol, animation=citations, height=200, quality='low') | |
| upld.markdown("#") | |
| upld.markdown("#") | |
| #------------------------------------------------------------------------------ Alert Message in the Middle Column of parent container | |
| display_alert_note(message="Note: \ | |
| Multiple Files with same names will be considered unique while constructing Vector Embeddings \ | |
| It takes a little bit of time for Vector Embeddings to be built, BE PATIENT!", container= upld) | |
| upld.markdown("#") | |
| upld.markdown("#") | |
| #------------------------------------------------------------------------------ Build Message in the Middle Column of parent container | |
| display_attention_text(text="Build your Knowledge Base (Vector DB)!", container=upld) | |
| with upld.container(): | |
| #------------------------------------------------------------------------------ Divide the middle parent container into yet 3 more columns (Child Container 1) | |
| upldLeft, upldcenter, upldRight = st.columns((1,5,1)) | |
| #------------------------------------------------------------------------------ Displaying right arrow on left column of child container 1 | |
| upldLeft.markdown("###") | |
| upldLeft.markdown("###") | |
| display_custom_arrow(direction="right", container=upldLeft) | |
| #------------------------------------------------------------------------------ Upload option in the middle column of child container 1 | |
| uploadedFiles = upldcenter.file_uploader(label= "Upload or Add Documents", | |
| type=['pdf', 'txt'], | |
| accept_multiple_files=True, | |
| key="fileUpload" | |
| ) | |
| #------------------------------------------------------------------------------ Displaying left arrow on right child container 1 | |
| upldRight.markdown("###") | |
| upldRight.markdown("###") | |
| display_custom_arrow(direction="left", container=upldRight) | |
| #------------------------------------------------------------------------------ Query Inputs for Wikipedia | |
| queryInputs = upldcenter.text_input(label="Type keywords to enhance augmented search & generation! Separate each keyword with ';'", | |
| placeholder="Domain Information will be scrapped from wikipedia based on key words you enter", | |
| ) | |
| #------------------------------------------------------------------------------ Removed any files that were removed from upload options | |
| # removedOrAdded() returns a list of files that have been removed from Upload Component | |
| rem = removedOrAdded(uploadedFiles) | |
| st.session_state.filesUploadedRecords = uploadedFiles | |
| # If there is a file that has been removed, delete its pertaining content from Vector Database and from state variable 'vdbBuilt' as well | |
| if len(rem) > 0: | |
| fileName = st.session_state.vdbBuilt.pop(list(rem.keys())[0], None) | |
| if fileName is not None: | |
| # addOrRemove=False indicates you want to delete the file or query passed | |
| # You can pass file and query both at the same time | |
| # You can pass file as str to delete but keep query as list | |
| # You can pass query as list to delete but keep files as None | |
| buildVectorDatabase(files=str(fileName), addOrRemove=False, query= []) | |
| with upld.container(): | |
| #------------------------------------------------------------------------------ Divide the middle parent container into yet 3 more columns (Child Container 2) | |
| _, crtOrAd, _ = st.columns((2,2,2)) | |
| #------------------------------------------------------------------------------ Button to create Vector Databases in center Child Container 2 | |
| createOrAdd = crtOrAd.button("Create/Add to the knowledge base") | |
| #------------------------------------------------------------------------------ If Button to create clicked, execute creation of Vector Databases | |
| if createOrAdd: | |
| with st_lottie_spinner(buildingDatabase , height=700): | |
| fileNames = [] | |
| wikiQueries = [] | |
| # Check if there are any keywords that user entered | |
| if queryInputs: | |
| # If keywords entered, iterate through those and check if there is already a Vector Database pertaining to those Key words | |
| # If not, create a new entry in state variable 'vdbBuilt' with key as keyword and value as None for now | |
| # Value is the sources from metadat that will be entered later when its loaded from wikipedia | |
| for w in queryInputs.split(';'): | |
| if 'Keyword ; ' + w.strip() not in st.session_state.vdbBuilt.keys() and len(w.strip()) > 0: | |
| st.session_state.vdbBuilt['Keyword ; ' + w.strip()] = None | |
| wikiQueries.append(w.strip()) | |
| # See if there any new files that have been uploaded | |
| # If there are: | |
| # Combine its name and upload id to create unique name for the file | |
| # Append it fileNames list | |
| # Also, add in state variable 'vdbBuilt', file_id as key and combined file name as value | |
| for file in uploadedFiles: | |
| if file.file_id not in st.session_state.vdbBuilt.keys(): | |
| fileName = "Dump/" + file.file_id + "---" + file.name | |
| with open(fileName, "wb") as f: | |
| f.write(file.getvalue()) | |
| fileNames.append(fileName) | |
| st.session_state.vdbBuilt[file.file_id] = fileName | |
| # If there are any new files or new Queries for which vectorDatabases do not exists already, build it | |
| if len(fileNames) > 0 or len(wikiQueries) > 0: | |
| buildVectorDatabase(files= fileNames, addOrRemove=True, query = wikiQueries) | |
| # Otherwise prompt the user, there's nothing new on which we can build | |
| else: | |
| #------------------------------------------------------------------------------ Display Error Message if there are no files/new files to create vector databases | |
| display_error_message(message= "You have no new files or Keywords to create/add vector databases!", container=upld) | |
| #------------------------------------------------------------------------------ Choice to remove certain Keywords based Knowledge base! | |
| keyWordOptions = [key.split(';')[-1].strip() for key, _ in st.session_state.vdbBuilt.items() if key.startswith("Keyword ; ")] | |
| optionsMessage = "You can cross off certain keywords from below if you'd prefer to remove contents relevant to your entered keywords, be removed from knowledge base" if len(keyWordOptions)>0 \ | |
| else "Your Knowledge Base does not have any keywords based contents scrapped from internet" | |
| upldcenter.multiselect( | |
| label= optionsMessage, | |
| options=keyWordOptions, | |
| default=keyWordOptions, | |
| key='keywordsDBOptions') | |
| popKeys = None | |
| for key, value in st.session_state.vdbBuilt.items(): | |
| val = key.split(";")[-1].strip() if key.startswith('Keyword ; ') else None | |
| if val is not None and val not in st.session_state.keywordsDBOptions: | |
| popKeys = key | |
| break | |
| if popKeys: | |
| buildVectorDatabase(files=None, addOrRemove=False, query=st.session_state.vdbBuilt[popKeys]) | |
| _ = st.session_state.vdbBuilt.pop(popKeys, None) | |
| st.rerun() | |
| #------------------------------------------------------------------------------ Display List of Files for Vector Databases available in right column of Parent Container | |
| for key, value in st.session_state.vdbBuilt.items(): | |
| val = key.split(";")[-1].strip() if key.startswith('Keyword ; ') else value.split("---")[-1].strip() | |
| display_small_text(val, rightCol) | |
| with upld.container(): | |
| st.markdown("###") | |
| st.divider() | |
| st.markdown("###") | |
| _, c1,c2, _ = st.columns((2,1,2,2)) | |
| loadItOnce(container=c1, animation=llama3, height=150, quality='low') | |
| display_question_box(c2) | |
| #------------------------------------------------------------------------------ Display Chat Input in center column of Parent Container if Vector Databases are available | |
| if len(st.session_state.vdbBuilt) == 0: | |
| st.chat_input(placeholder="Type your query here once you have built vector databases!", | |
| disabled=True) | |
| else: | |
| query = st.chat_input(placeholder="Type your query here once you have built vector databases!", | |
| disabled=False) | |
| generatorLlama3_8b = RetrievalChainGenerator(model_name=os.environ['LLAMA3MODEL8B'], vectorStore=st.session_state.vectorDatabase) | |
| if query: | |
| with st.container(): | |
| with st_lottie_spinner(fancyload, height=400): | |
| response = generatorLlama3_8b.chain.invoke({"input": query}) | |
| #------------------------------------------------------------------------------ Display response from AI in center column of Parent Container | |
| display_response_message(response['answer'], upld) | |
| #------------------------------------------------------------------------------ Display citations of responses from AI in left column of Parent Container | |
| display_allCitations(response, leftCol) | |
| #------------------------------------------------------------------------------ Second Page: Fine Tuning | |
| elif selected_option == "Fine Tuning LLMs (Coming Soon)": | |
| st_lottie(finetuning, quality='medium', height=700) | |
| #------------------------------------------------------------------------------ Third Page: Forecasting | |
| elif selected_option == "Forecasting LLMs (Coming Soon)": | |
| st_lottie(forecasting, quality='high', height=700) | |
| else: | |
| pass | |