diff --git a/.github/workflows/python-tests.yml b/.github/workflows/python-tests.yml index 8f47d86..db5c984 100644 --- a/.github/workflows/python-tests.yml +++ b/.github/workflows/python-tests.yml @@ -33,7 +33,7 @@ jobs: run: | # python -m pip install --upgrade pip setuptools wheel # python -m pip install poetry - poetry install --without torch-cuda + poetry install --with dev - name: Lint with flake8 working-directory: ./backend run: | diff --git a/Dockerfile b/Dockerfile index e2c8245..8bc6ff0 100644 --- a/Dockerfile +++ b/Dockerfile @@ -40,8 +40,9 @@ ENV CUDA_DOCKER_ARCH=all \ # Set the uvicorn env ENVIRONMENT=prod \ ########################################################## - # Build llama-cpp-python with cuda support + # # Build llama-cpp-python with cuda support # CMAKE_ARGS="-DLLAMA_CUBLAS=on" + ########################################################## # Build llama-cpp-python with openblas support on CPU CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" ########################################################## @@ -62,7 +63,7 @@ COPY --chown=user ./backend/pyproject.toml ./backend/poetry.lock $HOME/app/ COPY --chown=user ./backend $HOME/app # Install the dependencies -RUN poetry install --without dev,torch-cpu && \ +RUN poetry install --with torch-cuda && \ rm -rf /tmp/poetry_cache # Change to the package directory diff --git a/README.md b/README.md index a655e70..8f11fa7 100644 --- a/README.md +++ b/README.md @@ -50,8 +50,6 @@ pinned: false Smart Retrieval is a platform for efficient and streamlined information retrieval, especially in the realm of legal and compliance documents. With the power of Open-Source Large Language Models (LLM) and Retrieval Augmented Generation (RAG), it aims to enhance user experiences at JTC by addressing key challenges such as manual search inefficiencies and rigid file naming conventions, revolutionizing the way JTC employees access and comprehend crucial documents -Project files bootstrapped with [`create-llama`](https://github.com/run-llama/LlamaIndexTS/tree/main/packages/create-llama). - ## 🏁 Getting Started These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See [deployment](#deployment) for notes on how to deploy the project on a live system. @@ -76,6 +74,7 @@ For more information, see the [DEPLOYMENT.md](./DEPLOYMENT.md). - [Python](https://python.org/) - Backend Server Environment - [FastAPI](https://fastapi.tiangolo.com/) - Backend API Web Framework - [LlamaIndex](https://www.llamaindex.ai/) - Data Framework for LLM +- [`create-llama`](https://github.com/run-llama/LlamaIndexTS/tree/main/packages/create-llama) - LlamaIndex Application Bootstrap Tool ## 📑 Contributing diff --git a/backend/README.md b/backend/README.md index f4c32c3..8c1acb7 100644 --- a/backend/README.md +++ b/backend/README.md @@ -2,6 +2,8 @@ The backend is built using Python & [FastAPI](https://fastapi.tiangolo.com/) bootstrapped with [`create-llama`](https://github.com/run-llama/LlamaIndexTS/tree/main/packages/create-llama). +To get started, you must first install the required dependencies in `Requirements` section below, then follow the `Getting Started` section. + ## Requirements 1. Python >= 3.11 @@ -21,9 +23,14 @@ The backend is built using Python & [FastAPI](https://fastapi.tiangolo.com/) boo ## Getting Started -First, ensure if you want to use the cuda version of pytorch, you have the correct version `cuda > 12.1` of cuda installed. You can check this by running `nvcc --version or nvidia-smi` in your terminal. If you do not have cuda installed, you can install it from [here](https://developer.nvidia.com/cuda-downloads). +First, ensure if you want to use the cuda version of pytorch, you have the correct version `cuda > 12.1` of cuda installed. You can check this by running `nvcc --version or nvidia-smi` in your terminal, nvcc --version should correctly chow whether you have installed cuda properly or not. If you do not have cuda installed, you can install it from [here](https://developer.nvidia.com/cuda-downloads). + +- You may need to add cuda to your path, which can be found online. + +Ensure you have followed the steps in the `requirements` section above. -Ensure you have followed the steps in the requirements section above. +- If on windows, make sure you are running the commands in powershell. +- Add conda to your path, which can be found [here](https://stackoverflow.com/questions/64149680/how-can-i-activate-a-conda-environment-from-powershell) Then activate the conda environment: @@ -33,24 +40,29 @@ conda activate SmartRetrieval Second, setup the environment: -```bash -# Only run one of the following commands: +```powershell +# Only choose one of the options below depending on if you have CUDA enabled GPU or not: +# If running on windows, make sure you are running the commands in powershell. ----------------------------------------------- # Install dependencies and torch (cpu version) +# Go to the backend directory and edit the pyproject.toml file to uncomment the `torch-cpu` poetry section +----------------------------------------------- # Windows: Set env for llama-cpp-python with openblas support on cpu $env:CMAKE_ARGS = "-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" # Linux: Set env for llama-cpp-python with openblas support on cpu CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" # Then: -poetry install --without torch-cuda +poetry install --with torch-cpu ----------------------------------------------- # Install dependencies and torch (cuda version) +# Installing torch with cuda support on a system without cuda support is also possible. +----------------------------------------------- # Windows: Set env for llama-cpp-python with cuda support on gpu $env:CMAKE_ARGS = "-DLLAMA_CUBLAS=on" # Linux: Set env for llama-cpp-python with cuda support on gpu CMAKE_ARGS="-DLLAMA_CUBLAS=on" # Then: -poetry install --without torch-cpu +poetry install --with torch-cuda ``` ```bash diff --git a/backend/backend/app/utils/contants.py b/backend/backend/app/utils/contants.py index ef166b7..65263f3 100644 --- a/backend/backend/app/utils/contants.py +++ b/backend/backend/app/utils/contants.py @@ -7,7 +7,7 @@ # Model Constants MAX_NEW_TOKENS = 4096 -CONTEXT_SIZE = MAX_NEW_TOKENS +CONTEXT_SIZE = 3900 # llama2 has a context window of 4096 tokens, but we set it lower to allow for some wiggle room DEVICE_TYPE = "cuda" if is_cuda_available() else "cpu" # Get the current directory @@ -18,6 +18,7 @@ # LLM Model Constants LLM_MODEL_URL = "https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/resolve/main/llama-2-7b-chat.Q4_K_M.gguf" +LLM_TEMPERATURE = 0.1 # Model Kwargs # set to at least 1 to use GPU, adjust according to your GPU memory, but must be able to fit the model MODEL_KWARGS = {"n_gpu_layers": 100} if DEVICE_TYPE == "cuda" else {} diff --git a/backend/backend/app/utils/index.py b/backend/backend/app/utils/index.py index 7c59767..c47ed6e 100644 --- a/backend/backend/app/utils/index.py +++ b/backend/backend/app/utils/index.py @@ -28,6 +28,7 @@ EMBED_MODEL_NAME, EMBED_POOLING, LLM_MODEL_URL, + LLM_TEMPERATURE, MAX_NEW_TOKENS, MODEL_KWARGS, NUM_OUTPUT, @@ -36,12 +37,11 @@ llm = LlamaCPP( model_url=LLM_MODEL_URL, - temperature=0.1, + temperature=LLM_TEMPERATURE, max_new_tokens=MAX_NEW_TOKENS, - # llama2 has a context window of 4096 tokens, but we set it lower to allow for some wiggle room context_window=CONTEXT_SIZE, # kwargs to pass to __call__() - # generate_kwargs={}, + generate_kwargs={}, # kwargs to pass to __init__() model_kwargs=MODEL_KWARGS, # transform inputs into Llama2 format @@ -50,14 +50,6 @@ verbose=True, ) -# define prompt helper -# set maximum input size -max_input_size = 4096 -# set number of output tokens -num_output = 256 -# set maximum chunk overlap -max_chunk_overlap = 0.2 - embed_model = HuggingFaceEmbedding( model_name=EMBED_MODEL_NAME, pooling=EMBED_POOLING, diff --git a/backend/backend/get_PSSCOC_docs.py b/backend/backend/get_PSSCOC_docs.py new file mode 100644 index 0000000..9310fa8 --- /dev/null +++ b/backend/backend/get_PSSCOC_docs.py @@ -0,0 +1,186 @@ +import json +import os + +import requests +from bs4 import BeautifulSoup +from doc2docx import convert as convert_doc2docx + +""" +A web scraping script to download the Public Sector Standard Conditions of Contract (PSSCOC) documents from the BCA website. +The script will create a folder for each category and download the documents into the respective category folder. +The script will also convert doc files to docx files. +The script will also get the About PSSCOC page info and saves to a json file. +""" + +# Website URL +base_url = "https://www1.bca.gov.sg" +docs_endpoint = "/procurement/post-tender-stage/public-sector-standard-conditions-of-contract-psscoc" + +# Source Documents Folder +source_doc_dir = "data" +# PSSCOC Page Info Folder +psscoc_page_info_dir = "About PSSCOC" + + +# Get the PSSCOC documents +def get_psscoc_docs(): + """ + Downloads the PSSCOC documents. + """ + # Send a GET request to the website URL + response = requests.get(base_url + docs_endpoint, timeout=10) + response.raise_for_status() # Check for HTTP errors + + # Parse the HTML content of the response + soup = BeautifulSoup(response.content, "html.parser") + + # Find the table element that contains the Conditions of Contract & Downloads link + table = soup.find("table") + + # Loop through each row of the table element, skipping the first row + for row in table.find_all("tr")[1:]: + # Find the first cell of the row that contains the category name + first_cell = row.find("td", attrs={"scope": "row"}) + category_name = first_cell.find("strong").text.strip() + print("Category:", category_name) + + # Create a folder for each category if it doesn't exist + category_folder = os.path.join(source_doc_dir, category_name) + if not os.path.exists(category_folder): + os.makedirs(category_folder) + + # Find the second cell of the row that contains the href links + second_cell = row.find("ol") + # Loop through each list item in the second cell + for li in second_cell.find_all("li"): + # Find the href link + href_link = li.find("a")["href"] + # if starts with /docs, then it is a relative link + if href_link.startswith("/docs"): + href_link = base_url + href_link + + # Get the filename from the href link + filename = os.path.basename(href_link).split("?")[0] + print("Downloading:", filename) + # Send a GET request to the href link + response = requests.get(href_link, timeout=10) + # Write the response content to a file + with open(os.path.join(category_folder, filename), "wb") as f: + f.write(response.content) + print("Saved to:", os.path.join(category_folder, filename)) + # convert doc to docx + if filename.endswith(".doc"): + print("Converting to docx...") + convert_doc2docx(os.path.join(category_folder, filename)) + print( + "Converted to:", + os.path.join(category_folder, filename + "x"), + ) + # remove the original doc file + os.remove(os.path.join(category_folder, filename)) + # line break + print("-" * 100) + + +def get_psscoc_page_info(): + """ + Get the About PSSCOC page info and saves to a json file. + """ + print("Getting PSSCOC Page Info...") + # Send a GET request to the website URL + response = requests.get(base_url + docs_endpoint, timeout=10) + response.raise_for_status() # Check for HTTP errors + + # Parse the HTML content of the response + soup = BeautifulSoup(response.content, "html.parser") + + # Extract the necessary HTML elements + mid_body = soup.find("div", attrs={"class": "mid"}) + + cleaned_results = {} + + # Extract title from the mid_body + title = mid_body.find("div", attrs={"class": "title"}).text.strip() + cleaned_results["Title"] = title + + # Extract the flow content from the mid_body + flow_content = mid_body.find("ul", attrs={"class": "rsmFlow rsmLevel rsmOneLevel"}) + flow = [li.text.strip() for li in flow_content.find_all("li")] + cleaned_results["Stage of PSSCOC"] = " > ".join(flow[1:]) + + # Extract sfContentBlock from the mid_body + sf_content_block = mid_body.find("div", attrs={"class": "sfContentBlock"}) + + # Extract all the paragraphs from the sfContentBlock but not nested paragraphs + paragraphs = sf_content_block.find_all("p", recursive=False) + paragraphs_text = [p.text.strip() for p in paragraphs] + cleaned_results["About"] = paragraphs_text[0] + more_about = {} + more_about[paragraphs_text[1]] = ( + paragraphs_text[2] + .replace("\n", " ") + .replace("\r", " ") + .replace("\u00a0", " ") + .strip() + ) + + # Extract ul from the sfContentBlock + ul_content = sf_content_block.find("ul") + ul_li = [li.text.strip() for li in ul_content.find_all("li")] + more_about[paragraphs_text[3]] = ul_li # title for 3rd paragraph + + cleaned_results["More About"] = more_about + + # Extract the table from the sfContentBlock + table = sf_content_block.find("table") + table_rows = table.find_all("tr") + header_row = [td.text.strip() for td in table_rows[0].find_all("td")] + header_row = ["Category Name", "Category File Names"] + table_data_list = [] + for row in table_rows[1:]: + row_data = [td.text.strip() for td in row.find_all("td")] + row_data[-1] = row_data[-1].split("\n") + # remove empty string + row_data[-1] = [x.strip() for x in row_data[-1] if x] + table_data = dict(zip(header_row, row_data)) + table_data_list.append(table_data) + cleaned_results["Categories of PSSCOC"] = table_data_list + + # Extract all the divs from the sfContentBlock but not nested divs + # divs = sf_content_block.find_all("div", recursive=False) + + # Save the json results content to a file + page_info_folder = os.path.join(source_doc_dir, psscoc_page_info_dir) + file_name = "About PSSCOC.json" + # Create a folder for page info if it doesn't exist + if not os.path.exists(page_info_folder): + os.makedirs(page_info_folder) + # Save the results content to a file + with open(os.path.join(page_info_folder, file_name), "wb") as f: + f.write(json.dumps(cleaned_results, indent=4).encode("utf-8")) + print( + "Saved to:", + os.path.join(page_info_folder, file_name), + ) + + +# Main function +def main(): + """ + Main function. + """ + try: + # Get the PSSCOC documents + get_psscoc_docs() + # Get the About PSSCOC page info + get_psscoc_page_info() + except requests.exceptions.RequestException as e: + print("Error: Failed to make a request to the website.") + print(e) + except Exception as e: + print("An unexpected error occurred:") + print(e) + + +if __name__ == "__main__": + main() diff --git a/backend/backend/main.py b/backend/backend/main.py index 6f87bf9..ad432a5 100644 --- a/backend/backend/main.py +++ b/backend/backend/main.py @@ -4,6 +4,7 @@ from dotenv import load_dotenv from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware +from fastapi.responses import RedirectResponse from torch.cuda import is_available as is_cuda_available from backend.app.api.routers.chat import chat_router @@ -55,3 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hash = "sha256:928cecb0ef9d5a7946eb6ff58417ad2fe9375762382f1bf5c55e61645f2c43ad"}, + {file = "yarl-1.9.4.tar.gz", hash = "sha256:566db86717cf8080b99b58b083b773a908ae40f06681e87e589a976faf8246bf"}, ] [package.dependencies] @@ -2843,4 +3108,4 @@ multidict = ">=4.0" [metadata] lock-version = "2.0" python-versions = "^3.11,<3.12" -content-hash = "4a40c3668c981baeda73805b148b5d3cd052f100304f19826ae8640693a3b628" +content-hash = "8f5faa76a4729efe1e76ed3724e43ba837ef11e5e63bd0078a97eadf097b057e" diff --git a/backend/pyproject.toml b/backend/pyproject.toml index 912b1b6..e3b9801 100644 --- a/backend/pyproject.toml +++ b/backend/pyproject.toml @@ -10,26 +10,34 @@ packages = [{ include = "backend" }] python = "^3.11,<3.12" fastapi = "^0.104.1" uvicorn = { extras = ["standard"], version = "^0.23.2" } -llama-index = "^0.9.4" -pypdf = "^3.17.0" +llama-index = "^0.9.48" +pypdf = "^3.17.4" python-dotenv = "^1.0.0" -llama-cpp-python = "^0.2.18" -transformers = "^4.35.2" +llama-cpp-python = "^0.2.52" +transformers = "^4.38.1" docx2txt = "^0.8" +doc2docx = "^0.2.4" -# Dev Dependencies here +[tool.poetry.group.dev] +optional = true [tool.poetry.group.dev.dependencies] +# Dev Dependencies here flake8 = "^7.0.0" -pytest = "^7.4.4" +pytest = "^8.0.2" # For CPU torch version: Windows and Linux -[tool.poetry.group.torch-cpu.dependencies] -torch = [ - { url = "https://download.pytorch.org/whl/cpu/torch-2.1.1%2Bcpu-cp311-cp311-win_amd64.whl", markers = "sys_platform == 'win32'" }, - { url = "https://download.pytorch.org/whl/cpu/torch-2.1.1%2Bcpu-cp311-cp311-linux_x86_64.whl", markers = "sys_platform == 'linux'" }, -] +# NOTE: To uncomment out the following lines, should you need to use the CPU version of torch +# [tool.poetry.group.torch-cpu] +# optional = true +# [tool.poetry.group.torch-cpu.dependencies] +# torch = [ +# { url = "https://download.pytorch.org/whl/cpu/torch-2.1.1%2Bcpu-cp311-cp311-win_amd64.whl", markers = "sys_platform == 'win32'" }, +# { url = "https://download.pytorch.org/whl/cpu/torch-2.1.1%2Bcpu-cp311-cp311-linux_x86_64.whl", markers = "sys_platform == 'linux'" }, +# ] -## For Cuda torch version: Windows and Linux +# For Cuda torch version: Windows and Linux +[tool.poetry.group.torch-cuda] +optional = true [tool.poetry.group.torch-cuda.dependencies] torch = [ { url = "https://download.pytorch.org/whl/cu121/torch-2.1.1%2Bcu121-cp311-cp311-win_amd64.whl", markers = "sys_platform == 'win32'" }, diff --git a/frontend/app/components/chat-section.tsx b/frontend/app/components/chat-section.tsx index 50bbfe3..8d948cf 100644 --- a/frontend/app/components/chat-section.tsx +++ b/frontend/app/components/chat-section.tsx @@ -2,6 +2,7 @@ import { useChat } from "ai/react"; import { ChatInput, ChatMessages } from "@/app/components/ui/chat"; +import AutofillQuestion from "@/app/components/ui/autofill-prompt/autofill-prompt-dialog"; export default function ChatSection() { const { @@ -15,13 +16,20 @@ export default function ChatSection() { } = useChat({ api: process.env.NEXT_PUBLIC_CHAT_API }); return ( -
+
+ { + const { data, error: apiError, isLoading } = useSWR(healthcheck_api, async (url) => { try { // Fetch the data const response = await fetch(url, { @@ -170,15 +171,18 @@ export default function Header() { API:
- {apiError ? ( - - 🔴 - - ) : ( - - 🟢 - - )} + {isLoading ? ( + + ) : + apiError ? ( + + 🔴 + + ) : ( + + 🟢 + + )}
| @@ -198,10 +202,11 @@ export default function Header() { )}
-
+ {/* Mobile menu component */} - setMobileMenuOpen(false)} logoSrc={logo} items={MobileMenuItems} /> - - + < MobileMenu isOpen={isMobileMenuOpen} onClose={() => setMobileMenuOpen(false) + } logoSrc={logo} items={MobileMenuItems} /> + + ); } diff --git a/frontend/app/components/query-section.tsx b/frontend/app/components/query-section.tsx index bda4cf2..b9615ce 100644 --- a/frontend/app/components/query-section.tsx +++ b/frontend/app/components/query-section.tsx @@ -2,6 +2,7 @@ import { useChat } from "ai/react"; import { ChatInput, ChatMessages } from "@/app/components/ui/chat"; +import AutofillQuestion from "@/app/components/ui/autofill-prompt/autofill-prompt-dialog"; export default function QuerySection() { const { @@ -22,6 +23,13 @@ export default function QuerySection() { reload={reload} stop={stop} /> + { const [query, setQuery] = useState(""); const { searchResults, isLoading, handleSearch } = useSearch(); + const [searchButtonPressed, setSearchButtonPressed] = useState(false); + const handleInputChange = (e: ChangeEvent) => { setQuery(e.target.value); + setSearchButtonPressed(false); }; const handleSearchSubmit = (e: FormEvent) => { e.preventDefault(); + setSearchButtonPressed(true); handleSearch(query); }; @@ -24,10 +29,18 @@ const SearchSection: React.FC = () => { + - + ); }; diff --git a/frontend/app/components/ui/autofill-prompt/autofill-prompt-dialog.tsx b/frontend/app/components/ui/autofill-prompt/autofill-prompt-dialog.tsx new file mode 100644 index 0000000..704d40c --- /dev/null +++ b/frontend/app/components/ui/autofill-prompt/autofill-prompt-dialog.tsx @@ -0,0 +1,95 @@ +import { useEffect, useState } from "react"; +import { QuestionsBankProp, questionsBank } from "@/app/components/ui/autofill-prompt/autofill-prompt.interface"; +import { ChatHandler } from "@/app/components/ui/chat/chat.interface"; + +export default function AutofillQuestion( + props: Pick< + ChatHandler, + "messages" | "isLoading" | "handleSubmit" | "handleInputChange" | "input" + >, +) { + // Keep track of whether to show the overlay + const [showOverlay, setShowOverlay] = useState(true); + // Randomly select a subset of questions + const [randomQuestions, setRandomQuestions] = useState([]); + // Keep track of the current question index + const [currentQuestionIndex, setCurrentQuestionIndex] = useState(0); + + // Shuffle the array using Fisher-Yates algorithm + function shuffleArray(array: any[]) { + for (let i = array.length - 1; i > 0; i--) { + const j = Math.floor(Math.random() * (i + 1)); + [array[i], array[j]] = [array[j], array[i]]; + } + return array; + } + + // TODO: To load the questionsbank from a database in the future + + // Randomly select a subset of 3-4 questions + useEffect(() => { + // Shuffle the questionsBank array + const shuffledQuestions = shuffleArray(questionsBank); + // Get a random subset of 3-4 questions + const subsetSize = Math.floor(Math.random() * 2) + 3; // Randomly choose between 3 and 4 + const selectedQuestions = shuffledQuestions.slice(0, subsetSize); + // Do a short delay before setting the state to show the animation + setTimeout(() => { + setRandomQuestions(selectedQuestions); + }, 300); + }, []); + + + // Hide overlay when there are messages + useEffect(() => { + if (props.messages.length > 0) { + setShowOverlay(false); + } + else { + setShowOverlay(true); + } + }, [props.messages, props.input]); + + // Automatically advance to the next question after a delay + useEffect(() => { + const timer = setInterval(() => { + if (currentQuestionIndex < randomQuestions.length - 1) { + setCurrentQuestionIndex((prevIndex) => prevIndex + 1); + } + else { + clearInterval(timer); // Stop the timer when all questions have been displayed + } + }, 100); // Adjust the delay time as needed (e.g., 5000 milliseconds = 5 seconds) + + return () => clearInterval(timer); // Cleanup the timer on component unmount + }, [currentQuestionIndex, randomQuestions]); + + // Handle autofill questions click + const handleAutofillQuestionClick = (questionInput: string) => { + props.handleInputChange({ target: { name: "message", value: questionInput } } as React.ChangeEvent); + }; + + return ( + <> + {showOverlay && ( +
+
+

How can I help you today?

+ {randomQuestions.map((question, index) => ( +
    +
  • + +
    • +
+ ))} +
+
+ )} + + ); +} diff --git a/frontend/app/components/ui/autofill-prompt/autofill-prompt.interface.tsx b/frontend/app/components/ui/autofill-prompt/autofill-prompt.interface.tsx new file mode 100644 index 0000000..c9478bb --- /dev/null +++ b/frontend/app/components/ui/autofill-prompt/autofill-prompt.interface.tsx @@ -0,0 +1,14 @@ +export interface QuestionsBankProp { + title: string; +} + +export const questionsBank: QuestionsBankProp[] = [ + { title: "Under PSSCOC, what are the differences between the role of the SO Rep and SO?" }, + { title: "Who has the authority to appoint SO Rep assistants and what can the SO Rep assistants be authorized to do?" }, + { title: "What are the general obligations of the contractor and consultant?" }, + { title: "In which situations can the client claim for liquidated damages?" }, + { title: "What are the requirements for the contractor to claim for extension of time?" }, + { title: "What are the requirements for the contractor to claim for loss and expense?" }, + { title: "Under PSSCOC, briefly describe the proper payment claim process prescribed." }, + // Add more common questions as needed +]; \ No newline at end of file diff --git a/frontend/app/components/ui/autofill-prompt/autofill-search-prompt-dialog.tsx b/frontend/app/components/ui/autofill-prompt/autofill-search-prompt-dialog.tsx new file mode 100644 index 0000000..9313c2b --- /dev/null +++ b/frontend/app/components/ui/autofill-prompt/autofill-search-prompt-dialog.tsx @@ -0,0 +1,97 @@ +import { useEffect, useState } from "react"; +import { QuestionsBankProp, questionsBank } from "@/app/components/ui/autofill-prompt/autofill-prompt.interface"; +import { SearchHandler } from "@/app/components/ui/search/search.interface"; + +export default function AutofillSearchQuery( + props: Pick< + SearchHandler, + "query" | "isLoading" | "onSearchSubmit" | "onInputChange" | "results" | "searchButtonPressed" + >, +) { + // Keep track of whether to show the overlay + const [showOverlay, setShowOverlay] = useState(true); + // Randomly select a subset of questions + const [randomQuestions, setRandomQuestions] = useState([]); + // Keep track of the current question index + const [currentQuestionIndex, setCurrentQuestionIndex] = useState(0); + + // Shuffle the array using Fisher-Yates algorithm + function shuffleArray(array: any[]) { + for (let i = array.length - 1; i > 0; i--) { + const j = Math.floor(Math.random() * (i + 1)); + [array[i], array[j]] = [array[j], array[i]]; + } + return array; + } + + // TODO: To load the questionsbank from a database in the future + + // Randomly select a subset of 3-4 questions + useEffect(() => { + // Shuffle the questionsBank array + const shuffledQuestions = shuffleArray(questionsBank); + // Get a random subset of 3-4 questions + const subsetSize = Math.floor(Math.random() * 2) + 3; // Randomly choose between 3 and 4 + const selectedQuestions = shuffledQuestions.slice(0, subsetSize); + // Do a short delay before setting the state to show the animation + setTimeout(() => { + setRandomQuestions(selectedQuestions); + }, 300); + }, []); + + + // Hide overlay when there are query + useEffect(() => { + if (props.query.length > 0) { + setShowOverlay(false); + } + else { + setShowOverlay(true); + } + }, [props.results, props.query]); + + // Automatically advance to the next question after a delay + useEffect(() => { + const timer = setInterval(() => { + if (currentQuestionIndex < randomQuestions.length - 1) { + setCurrentQuestionIndex((prevIndex) => prevIndex + 1); + } + else { + clearInterval(timer); // Stop the timer when all questions have been displayed + } + }, 100); // Adjust the delay time as needed (e.g., 5000 milliseconds = 5 seconds) + + return () => clearInterval(timer); // Cleanup the timer on component unmount + }, [currentQuestionIndex, randomQuestions]); + + // Handle autofill questions click + const handleAutofillQuestionClick = (questionInput: string) => { + if (props.onInputChange) { + props.onInputChange({ target: { name: "message", value: questionInput } } as React.ChangeEvent); + } + }; + + return ( + <> + {showOverlay && ( +
+
+

How can I help you today?

+ {randomQuestions.map((question, index) => ( +
    +
  • + +
    • +
+ ))} +
+
+ )} + + ); +} diff --git a/frontend/app/components/ui/chat/chat-input.tsx b/frontend/app/components/ui/chat/chat-input.tsx index f0e33a4..8c04f82 100644 --- a/frontend/app/components/ui/chat/chat-input.tsx +++ b/frontend/app/components/ui/chat/chat-input.tsx @@ -1,6 +1,6 @@ -import { Button } from "../button"; -import { Input } from "../input"; -import { ChatHandler } from "./chat.interface"; +import { Button } from "@/app/components/ui/button"; +import { Input } from "@/app/components/ui/input"; +import { ChatHandler } from "@/app/components/ui/chat/chat.interface"; import { Send } from "lucide-react"; export default function ChatInput( @@ -9,6 +9,7 @@ export default function ChatInput( "isLoading" | "handleSubmit" | "handleInputChange" | "input" >, ) { + return (
@@ -27,7 +28,7 @@ export default function ChatInput( ); diff --git a/frontend/app/components/login-buttons.tsx b/frontend/app/components/ui/login-buttons.tsx similarity index 89% rename from frontend/app/components/login-buttons.tsx rename to frontend/app/components/ui/login-buttons.tsx index ceabaca..ace7e7d 100644 --- a/frontend/app/components/login-buttons.tsx +++ b/frontend/app/components/ui/login-buttons.tsx @@ -1,8 +1,7 @@ 'use client' -import { useState, useEffect } from 'react' +import { useState } from 'react' import { signIn } from 'next-auth/react' - import { cn } from '@/app/components/ui/lib/utils' import { Button, type ButtonProps } from '@/app/components/ui/button' import { IconGoogle, IconSGid, IconSpinner } from '@/app/components/ui/icons' @@ -20,20 +19,16 @@ function GoogleLoginButton({ }: LoginButtonProps) { const [isLoading, setIsLoading] = useState(false); - useEffect(() => { - setIsLoading(false); - }, []); - return (