import gradio as gr import pandas as pd import numpy as np import os import time import re import json from auditqa.sample_questions import QUESTIONS from auditqa.reports import POSSIBLE_REPORTS from auditqa.engine.prompts import audience_prompts, answer_prompt_template, llama_prompt from auditqa.doc_process import process_pdf from langchain_core.prompts import ChatPromptTemplate from langchain_core.output_parsers import StrOutputParser from langchain.llms import HuggingFaceEndpoint from dotenv import load_dotenv load_dotenv() HF_token = os.environ["HF_TOKEN"] vectorstores = process_pdf() async def chat(query,history,sources,reports): """taking a query and a message history, use a pipeline (reformulation, retriever, answering) to yield a tuple of: (messages in gradio format, messages in langchain format, source documents)""" print(f">> NEW QUESTION : {query}") print(f"history:{history}") #print(f"audience:{audience}") print(f"sources:{sources}") print(f"reports:{reports}") docs_html = "" output_query = "" output_language = "English" audience = "Experts" if audience == "Children": audience_prompt = audience_prompts["children"] elif audience == "General public": audience_prompt = audience_prompts["general"] elif audience == "Experts": audience_prompt = audience_prompts["experts"] else: audience_prompt = audience_prompts["experts"] # Prepare default values if len(sources) == 0: sources = ["Consolidated Reports"] if len(reports) == 0: reports = [] if sources == "Ministry": vectorstore = vectorstores["MWTS"] else: vectorstore = vectorstores["Consolidated"] # get context context_retrieved_lst = [] question_lst= [query] for question in question_lst: retriever = vectorstore.as_retriever( search_type="similarity_score_threshold", search_kwargs={"score_threshold": 0.6, "k": 3}) context_retrieved = retriever.invoke(question) def format_docs(docs): return "\n\n".join(doc.page_content for doc in docs) context_retrieved_formatted = format_docs(context_retrieved) context_retrieved_lst.append(context_retrieved_formatted) # get prompt prompt = ChatPromptTemplate.from_template(llama_prompt) # get llm # llm_qa = HuggingFaceEndpoint( # endpoint_url= "https://mnczdhmrf7lkfd9d.eu-west-1.aws.endpoints.huggingface.cloud", # task="text-generation", # huggingfacehub_api_token=HF_token, # model_kwargs={}) # trying llm new-prompt adapted for llama-3 # https://stackoverflow.com/questions/78429932/langchain-ollama-and-llama-3-prompt-and-response # https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.model_kwargs # https://huggingface.co/blog/llama3#how-to-prompt-llama-3 llm_qa = HuggingFaceEndpoint( endpoint_url= "https://mnczdhmrf7lkfd9d.eu-west-1.aws.endpoints.huggingface.cloud", task="text-generation", huggingfacehub_api_token=HF_token) # create rag chain chain = prompt | llm_qa | StrOutputParser() # get answers answer_lst = [] for question, context in zip(question_lst , context_retrieved_lst): answer = chain.invoke({"context": context, "question": question,'audience':audience_prompt, 'language':'english'}) answer_lst.append(answer) docs_html = [] for i, d in enumerate(context_retrieved, 1): docs_html.append(make_html_source(d, i)) docs_html = "".join(docs_html) previous_answer = history[-1][1] previous_answer = previous_answer if previous_answer is not None else "" answer_yet = previous_answer + answer_lst[0] answer_yet = parse_output_llm_with_sources(answer_yet) history[-1] = (query,answer_yet) history = [tuple(x) for x in history] yield history,docs_html,output_query,output_language def make_html_source(source,i): meta = source.metadata # content = source.page_content.split(":",1)[1].strip() content = source.page_content.strip() name = meta['source'] card = f"""

Doc {i} - {meta['file_path']} - Page {int(meta['page'])}

{content}

{name} 🔗
""" return card def parse_output_llm_with_sources(output): # Split the content into a list of text and "[Doc X]" references content_parts = re.split(r'\[(Doc\s?\d+(?:,\s?Doc\s?\d+)*)\]', output) parts = [] for part in content_parts: if part.startswith("Doc"): subparts = part.split(",") subparts = [subpart.lower().replace("doc","").strip() for subpart in subparts] subparts = [f"""{subpart}""" for subpart in subparts] parts.append("".join(subparts)) else: parts.append(part) content_parts = "".join(parts) return content_parts # -------------------------------------------------------------------- # Gradio # -------------------------------------------------------------------- # Set up Gradio Theme theme = gr.themes.Base( primary_hue="blue", secondary_hue="red", font=[gr.themes.GoogleFont("Poppins"), "ui-sans-serif", "system-ui", "sans-serif"], ) init_prompt = """ Hello, I am Audit Q&A, a conversational assistant designed to help you understand audit Reports. I will answer your questions by **crawling through the Audit reports publishsed by Auditor General Office**. ❓ How to use - **Examples**(tab on right): If this is first time for you using this app, then we have curated some example questions.Select a particular question from category fo questions. - **Reports**(tab on right): You can choose to search or address your question to either specific report or a collection of reportlike Consolidated Annual Report,District or Department focused reports. If you dont select then the Consolidated report is relied upon to answer your question. - **Sources**(tab on right): This tab will display the relied upon paragraphs from the report, to help you in assessing or fact checking if the answer provided by Audit Q&A assitant is correct or not. ⚠️ Limitations - *Please note that the AI is not perfect and may sometimes give irrelevant answers. If you are not satisfied with the answer, please ask a more specific question or report your feedback to help us improve the system.* - Audit Q&A assistant is a Generative AI, and therefore is not deterministic, so there might be change in answer to same question. What do you want to learn ? """ # Setting Tabs with gr.Blocks(title="Audit Q&A", css="style.css", theme=theme,elem_id = "main-component") as demo: # user_id_state = gr.State([user_id]) with gr.Tab("AuditQ&A"): with gr.Row(elem_id="chatbot-row"): with gr.Column(scale=2): # state = gr.State([system_template]) chatbot = gr.Chatbot( value=[(None,init_prompt)], show_copy_button=True,show_label = False,elem_id="chatbot",layout = "panel", avatar_images = (None,"data-collection.png"), )#,avatar_images = ("assets/logo4.png",None)) # bot.like(vote,None,None) with gr.Row(elem_id = "input-message"): textbox=gr.Textbox(placeholder="Ask me anything here!",show_label=False,scale=7,lines = 1,interactive = True,elem_id="input-textbox") # submit = gr.Button("",elem_id = "submit-button",scale = 1,interactive = True,icon = "https://static-00.iconduck.com/assets.00/settings-icon-2048x2046-cw28eevx.png") with gr.Column(scale=1, variant="panel",elem_id = "right-panel"): with gr.Tabs() as tabs: with gr.TabItem("Examples",elem_id = "tab-examples",id = 0): examples_hidden = gr.Textbox(visible = False) first_key = list(QUESTIONS.keys())[0] dropdown_samples = gr.Dropdown(QUESTIONS.keys(),value = first_key,interactive = True,show_label = True,label = "Select a category of sample questions",elem_id = "dropdown-samples") samples = [] for i,key in enumerate(QUESTIONS.keys()): examples_visible = True if i == 0 else False with gr.Row(visible = examples_visible) as group_examples: examples_questions = gr.Examples( QUESTIONS[key], [examples_hidden], examples_per_page=8, run_on_click=False, elem_id=f"examples{i}", api_name=f"examples{i}", # label = "Click on the example question or enter your own", # cache_examples=True, ) samples.append(group_examples) with gr.Tab("Reports",elem_id = "tab-config",id = 2): gr.Markdown("Reminder: To get better results select the specific report/reports") dropdown_sources = gr.Dropdown( ["Consolidated Reports", "District","Ministry"], label="Select source", value=["Ministry"], interactive=True, ) dropdown_reports = gr.Dropdown( POSSIBLE_REPORTS, label="Or select specific reports", multiselect=True, value=None, interactive=True, ) #dropdown_audience = "Experts" #dropdown_audience = gr.Dropdown( # ["Children","General public","Experts"], # label="Select audience", # value="Experts", # interactive=True, #) output_query = gr.Textbox(label="Query used for retrieval",show_label = True,elem_id = "reformulated-query",lines = 2,interactive = False) #output_language = gr.Textbox(label="Language",show_label = True,elem_id = "language",lines = 1,interactive = False) with gr.Tab("Sources",elem_id = "tab-citations",id = 1): sources_textbox = gr.HTML(show_label=False, elem_id="sources-textbox") docs_textbox = gr.State("") # with Modal(visible = False) as config_modal: with gr.Tab("About",elem_classes = "max-height other-tabs"): with gr.Row(): with gr.Column(scale=1): gr.Markdown("See more info at [https://www.oag.go.ug/](https://www.oag.go.ug/welcome)") def start_chat(query,history): history = history + [(query,None)] history = [tuple(x) for x in history] return (gr.update(interactive = False),gr.update(selected=1),history) def finish_chat(): return (gr.update(interactive = True,value = "")) (textbox .submit(start_chat, [textbox,chatbot], [textbox,tabs,chatbot],queue = False,api_name = "start_chat_textbox") .then(chat, [textbox,chatbot, dropdown_sources,dropdown_reports], [chatbot,sources_textbox,output_query],concurrency_limit = 8,api_name = "chat_textbox") .then(finish_chat, None, [textbox],api_name = "finish_chat_textbox") ) (examples_hidden .change(start_chat, [examples_hidden,chatbot], [textbox,tabs,chatbot],queue = False,api_name = "start_chat_examples") .then(chat, [examples_hidden,chatbot, dropdown_sources,dropdown_reports], [chatbot,sources_textbox,output_query],concurrency_limit = 8,api_name = "chat_examples") .then(finish_chat, None, [textbox],api_name = "finish_chat_examples") ) def change_sample_questions(key): index = list(QUESTIONS.keys()).index(key) visible_bools = [False] * len(samples) visible_bools[index] = True return [gr.update(visible=visible_bools[i]) for i in range(len(samples))] dropdown_samples.change(change_sample_questions,dropdown_samples,samples) demo.queue() demo.launch()