app.py

from climateqa.engine.embeddings import get_embeddings_function
embeddings_function = get_embeddings_function()

from climateqa.knowledge.openalex import OpenAlex
from sentence_transformers import CrossEncoder

# reranker = CrossEncoder("mixedbread-ai/mxbai-rerank-xsmall-v1")
oa = OpenAlex()

import gradio as gr
from gradio_modal import Modal
import pandas as pd
import numpy as np
import os
import time
import re
import json

from gradio import ChatMessage

# from gradio_modal import Modal

from io import BytesIO
import base64

from datetime import datetime
from azure.storage.fileshare import ShareServiceClient

from utils import create_user_id

from gradio_modal import Modal

from PIL import Image

from langchain_core.runnables.schema import StreamEvent

# ClimateQ&A imports
from climateqa.engine.llm import get_llm
from climateqa.engine.vectorstore import get_pinecone_vectorstore
# from climateqa.knowledge.retriever import ClimateQARetriever
from climateqa.engine.reranker import get_reranker
from climateqa.engine.embeddings import get_embeddings_function
from climateqa.engine.chains.prompts import audience_prompts
from climateqa.sample_questions import QUESTIONS
from climateqa.constants import POSSIBLE_REPORTS, OWID_CATEGORIES
from climateqa.utils import get_image_from_azure_blob_storage
from climateqa.engine.keywords import make_keywords_chain
from climateqa.engine.chains.answer_rag import make_rag_papers_chain
from climateqa.engine.graph import make_graph_agent
from climateqa.engine.embeddings import get_embeddings_function

from front.utils import serialize_docs,process_figures,make_html_df

from climateqa.event_handler import init_audience, handle_retrieved_documents, stream_answer,handle_retrieved_owid_graphs 

# Load environment variables in local mode
try:
    from dotenv import load_dotenv
    load_dotenv()
except Exception as e:
    pass


# 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 = ""

system_template = {
    "role": "system",
    "content": init_prompt,
}

account_key = os.environ["BLOB_ACCOUNT_KEY"]
if len(account_key) == 86:
    account_key += "=="

credential = {
    "account_key": account_key,
    "account_name": os.environ["BLOB_ACCOUNT_NAME"],
}

account_url = os.environ["BLOB_ACCOUNT_URL"]
file_share_name = "climateqa"
service = ShareServiceClient(account_url=account_url, credential=credential)
share_client = service.get_share_client(file_share_name)

user_id = create_user_id()


CITATION_LABEL = "BibTeX citation for ClimateQ&A"
CITATION_TEXT = r"""@misc{climateqa,
    author={Théo Alves Da Costa, Timothée Bohe},
    title={ClimateQ&A, AI-powered conversational assistant for climate change and biodiversity loss},
    year={2024},
    howpublished= {\url{https://climateqa.com}},
}
@software{climateqa,
    author = {Théo Alves Da Costa, Timothée Bohe},
    publisher = {ClimateQ&A},
    title = {ClimateQ&A, AI-powered conversational assistant for climate change and biodiversity loss},
}
"""



# Create vectorstore and retriever
vectorstore = get_pinecone_vectorstore(embeddings_function, index_name = os.getenv("PINECONE_API_INDEX"))
vectorstore_graphs = get_pinecone_vectorstore(embeddings_function, index_name = os.getenv("PINECONE_API_INDEX_OWID"), text_key="title")

llm = get_llm(provider="openai",max_tokens = 1024,temperature = 0.0)
reranker = get_reranker("nano")

agent = make_graph_agent(llm=llm, vectorstore_ipcc=vectorstore, vectorstore_graphs=vectorstore_graphs, reranker=reranker)


async def chat(query, history, audience, sources, reports, relevant_content_sources):
    """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)"""

    date_now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    print(f">> NEW QUESTION ({date_now}) : {query}")

    audience_prompt = init_audience(audience)

    # Prepare default values
    if sources is None or len(sources) == 0:
        sources = ["IPCC", "IPBES", "IPOS"]

    if reports is None or len(reports) == 0:
        reports = []
    
    inputs = {"user_input": query,"audience": audience_prompt,"sources_input":sources, "relevant_content_sources" : relevant_content_sources}
    result = agent.astream_events(inputs,version = "v1") 


    docs = []
    used_figures=[]
    related_contents = []
    docs_html = ""
    output_query = ""
    output_language = ""
    output_keywords = ""
    start_streaming = False
    graphs_html = ""    
    figures = '<div class="figures-container"><p></p> </div>'

    steps_display = {
        "categorize_intent":("🔄️ Analyzing user message",True),
        "transform_query":("🔄️ Thinking step by step to answer the question",True),
        "retrieve_documents":("🔄️ Searching in the knowledge base",False),
    }
    
    used_documents = []
    answer_message_content = ""
    try:
        async for event in result:
            if "langgraph_node" in event["metadata"]:
                node = event["metadata"]["langgraph_node"]

                if event["event"] == "on_chain_end" and event["name"] == "retrieve_documents" :# when documents are retrieved
                    docs, docs_html, history, used_documents, related_contents = handle_retrieved_documents(event, history, used_documents)    
                
                elif event["event"] == "on_chain_end" and node == "categorize_intent" and event["name"] == "_write": # when the query is transformed
                    
                    intent = event["data"]["output"]["intent"]
                    if "language" in event["data"]["output"]:
                        output_language = event["data"]["output"]["language"]
                    else :
                        output_language = "English"
                    history[-1].content = f"Language identified : {output_language} \n Intent identified : {intent}"
                    
                    
                elif event["name"] in steps_display.keys() and event["event"] == "on_chain_start": #display steps
                    event_description, display_output = steps_display[node]
                    if not hasattr(history[-1], 'metadata') or history[-1].metadata["title"] != event_description: # if a new step begins
                        history.append(ChatMessage(role="assistant", content = "", metadata={'title' :event_description}))
 
                elif event["name"] != "transform_query" and event["event"] == "on_chat_model_stream" and node in ["answer_rag", "answer_search","answer_chitchat"]:# if streaming answer
                    history, start_streaming, answer_message_content = stream_answer(history, event, start_streaming, answer_message_content)

                elif event["name"] in ["retrieve_graphs", "retrieve_graphs_ai"] and event["event"] == "on_chain_end":
                    graphs_html = handle_retrieved_owid_graphs(event, graphs_html)


                if event["name"] == "transform_query" and event["event"] =="on_chain_end":
                    if hasattr(history[-1],"content"):
                        history[-1].content += "Decompose question into sub-questions: \n\n - " + "\n - ".join([q["question"] for q in event["data"]["output"]["remaining_questions"]])
                        
                if event["name"] == "categorize_intent" and event["event"] == "on_chain_start":
                    print("X")
            
            yield history, docs_html, output_query, output_language, related_contents , graphs_html,  #,output_query,output_keywords
 
    except Exception as e:
        print(event, "has failed")
        raise gr.Error(f"{e}")


    try:
        # Log answer on Azure Blob Storage
        if os.getenv("GRADIO_ENV") != "local":
            timestamp = str(datetime.now().timestamp())
            file = timestamp + ".json"
            prompt = history[1]["content"]
            logs = {
                "user_id": str(user_id),
                "prompt": prompt,
                "query": prompt,
                "question":output_query,
                "sources":sources,
                "docs":serialize_docs(docs),
                "answer": history[-1].content,
                "time": timestamp,
            }
            log_on_azure(file, logs, share_client)
    except Exception as e:
        print(f"Error logging on Azure Blob Storage: {e}")
        raise gr.Error(f"ClimateQ&A Error: {str(e)[:100]} - The error has been noted, try another question and if the error remains, you can contact us :)")

    yield history, docs_html, output_query, output_language, related_contents, graphs_html 


def save_feedback(feed: str, user_id):
    if len(feed) > 1:
        timestamp = str(datetime.now().timestamp())
        file = user_id + timestamp + ".json"
        logs = {
            "user_id": user_id,
            "feedback": feed,
            "time": timestamp,
        }
        log_on_azure(file, logs, share_client)
        return "Feedback submitted, thank you!"




def log_on_azure(file, logs, share_client):
    logs = json.dumps(logs)
    file_client = share_client.get_file_client(file)
    file_client.upload_file(logs)


def generate_keywords(query):
    chain = make_keywords_chain(llm)
    keywords = chain.invoke(query)
    keywords = " AND ".join(keywords["keywords"])
    return keywords



papers_cols_widths = {
    "id":100,
    "title":300,
    "doi":100,
    "publication_year":100,
    "abstract":500,
    "is_oa":50,
}

papers_cols = list(papers_cols_widths.keys())
papers_cols_widths = list(papers_cols_widths.values())


async def find_papers(query,after, relevant_content_sources):
    if "OpenAlex" in relevant_content_sources:
        summary = ""
        keywords = generate_keywords(query)
        df_works = oa.search(keywords,after = after)
        df_works = df_works.dropna(subset=["abstract"])
        df_works = oa.rerank(query,df_works,reranker)
        df_works = df_works.sort_values("rerank_score",ascending=False)
        docs_html = []
        for i in range(10):
            docs_html.append(make_html_df(df_works, i))
        docs_html = "".join(docs_html)
        print(docs_html)
        G = oa.make_network(df_works)

        height = "750px"
        network = oa.show_network(G,color_by = "rerank_score",notebook=False,height = height)
        network_html = network.generate_html()

        network_html = network_html.replace("'", "\"")
        css_to_inject = "<style>#mynetwork { border: none !important; } .card { border: none !important; }</style>"
        network_html = network_html + css_to_inject

        
        network_html = f"""<iframe style="width: 100%; height: {height};margin:0 auto" name="result" allow="midi; geolocation; microphone; camera; 
        display-capture; encrypted-media;" sandbox="allow-modals allow-forms 
        allow-scripts allow-same-origin allow-popups 
        allow-top-navigation-by-user-activation allow-downloads" allowfullscreen="" 
        allowpaymentrequest="" frameborder="0" srcdoc='{network_html}'></iframe>"""


        docs = df_works["content"].head(10).tolist()

        df_works = df_works.reset_index(drop = True).reset_index().rename(columns = {"index":"doc"})
        df_works["doc"] = df_works["doc"] + 1
        df_works = df_works[papers_cols]

        yield docs_html, network_html, summary 

        chain = make_rag_papers_chain(llm)
        result = chain.astream_log({"question": query,"docs": docs,"language":"English"})
        path_answer = "/logs/StrOutputParser/streamed_output/-"

        async for op in result:

            op = op.ops[0]

            if op['path'] == path_answer: # reforulated question
                new_token = op['value'] # str
                summary += new_token
            else:
                continue
            yield docs_html, network_html, summary
    


# --------------------------------------------------------------------
# Gradio
# --------------------------------------------------------------------


init_prompt = """
Hello, I am ClimateQ&A, a conversational assistant designed to help you understand climate change and biodiversity loss. I will answer your questions by **sifting through the IPCC and IPBES scientific reports**.

❓ How to use
- **Language**: You can ask me your questions in any language. 
- **Audience**: You can specify your audience (children, general public, experts) to get a more adapted answer.
- **Sources**: You can choose to search in the IPCC or IPBES reports, or both.

⚠️ 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.*

🛈 Information
Please note that we log your questions for meta-analysis purposes, so avoid sharing any sensitive or personal information.


What do you want to learn ?
"""


def vote(data: gr.LikeData):
    if data.liked:
        print(data.value)
    else:
        print(data)

def save_graph(saved_graphs_state, embedding, category):
    print(f"\nCategory:\n{saved_graphs_state}\n")
    if category not in saved_graphs_state:
        saved_graphs_state[category] = []
    if embedding not in saved_graphs_state[category]:
        saved_graphs_state[category].append(embedding)
    return saved_graphs_state, gr.Button("Graph Saved")



with gr.Blocks(title="Climate Q&A", css_paths=os.getcwd()+ "/style.css", theme=theme,elem_id = "main-component") as demo:
    chat_completed_state = gr.State(0)
    current_graphs = gr.State([])
    saved_graphs = gr.State({})

    
    with gr.Tab("ClimateQ&A"):

        with gr.Row(elem_id="chatbot-row"):
            with gr.Column(scale=2):
                chatbot = gr.Chatbot(
                    value = [ChatMessage(role="assistant", content=init_prompt)],
                    type = "messages",
                    show_copy_button=True,
                    show_label = False,
                    elem_id="chatbot",
                    layout = "panel",
                    avatar_images = (None,"https://i.ibb.co/YNyd5W2/logo4.png"),
                    max_height="80vh",
                    height="100vh"
                )
                
                # 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")
                 

            with gr.Column(scale=2, 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("Sources",elem_id = "tab-sources",id = 1) as tab_sources:
                        sources_textbox = gr.HTML(show_label=False, elem_id="sources-textbox")
                        docs_textbox = gr.State("")
                        
                            
                    
                    with gr.Tab("Figures",elem_id = "tab-figures",id = 3) as tab_figures:
                        sources_raw = gr.State()
                        
                        with Modal(visible=False, elem_id="modal_figure_galery") as modal:
                            gallery_component = gr.Gallery(object_fit='scale-down',elem_id="gallery-component", height="80vh")
                            
                        show_full_size_figures = gr.Button("Show figures in full size",elem_id="show-figures",interactive=True)    
                        show_full_size_figures.click(lambda : Modal(visible=True),None,modal)

                        figures_cards = gr.HTML(show_label=False, elem_id="sources-figures")



                    with gr.Tab("Papers",elem_id = "tab-citations",id = 5) as tab_papers:
                        btn_summary = gr.Button("Summary")
                         # Fenêtre simulée pour le Summary
                        with gr.Group(visible=False, elem_id="papers-summary-popup") as summary_popup:
                            papers_summary = gr.Markdown("### Summary Content", visible=True, elem_id="papers-summary")

                        btn_relevant_papers = gr.Button("Relevant papers")
                        # Fenêtre simulée pour les Relevant Papers
                        with gr.Group(visible=False, elem_id="papers-relevant-popup") as relevant_popup:
                            papers_html = gr.HTML(show_label=False, elem_id="sources-textbox")
                            docs_textbox = gr.State("")

                        btn_citations_network = gr.Button("Citations network")
                        # Fenêtre simulée pour le Citations Network
                        with Modal(visible=False) as modal:
                            citations_network = gr.HTML("<h3>Citations Network Graph</h3>", visible=True, elem_id="papers-citations-network")
                        btn_citations_network.click(lambda: Modal(visible=True), None, modal)
                    

                        
                    with gr.Tab("Recommended content", elem_id="tab-recommended_content", id=4) as tab_recommended_content:
                        graphs_container = gr.HTML("<h2>There are no graphs to be displayed at the moment. Try asking another question.</h2>")
                        current_graphs.change(lambda x : x, inputs=[current_graphs], outputs=[graphs_container])

    

   
    with gr.Tab("Configuration") as tab_config:
        gr.Markdown("Reminders: You can talk in any language, ClimateQ&A is multi-lingual!")


        dropdown_sources = gr.CheckboxGroup(
            ["IPCC", "IPBES","IPOS"],
            label="Select source",
            value=["IPCC"],
            interactive=True,
        )
        dropdown_external_sources = gr.CheckboxGroup(
            ["IPCC figures","OpenAlex", "OurWorldInData"],
            label="Select database to search for relevant content",
            value=["IPCC figures"],
            interactive=True,
        )
        
        dropdown_reports = gr.Dropdown(
            POSSIBLE_REPORTS,
            label="Or select specific reports",
            multiselect=True,
            value=None,
            interactive=True,
        )

        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, visible= False)
        output_language = gr.Textbox(label="Language",show_label = True,elem_id = "language",lines = 1,interactive = False, visible= False)                            






#---------------------------------------------------------------------------------------
# OTHER TABS
#---------------------------------------------------------------------------------------

    with gr.Tab("Settings",elem_id = "tab-config",id = 2):

        gr.Markdown("Reminder: You can talk in any language, ClimateQ&A is multi-lingual!")


        dropdown_sources = gr.CheckboxGroup(
            ["IPCC", "IPBES","IPOS", "OpenAlex"],
            label="Select source",
            value=["IPCC"],
            interactive=True,
        )

        dropdown_reports = gr.Dropdown(
            POSSIBLE_REPORTS,
            label="Or select specific reports",
            multiselect=True,
            value=None,
            interactive=True,
        )

        dropdown_audience = gr.Dropdown(
            ["Children","General public","Experts"],
            label="Select audience",
            value="Experts",
            interactive=True,
        )

        after = gr.Slider(minimum=1950,maximum=2023,step=1,value=1960,label="Publication date",show_label=True,interactive=True,elem_id="date-papers")

        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("About",elem_classes = "max-height other-tabs"):
        with gr.Row():
            with gr.Column(scale=1):




                gr.Markdown(
                    """
                    ### More info
                    - See more info at [https://climateqa.com](https://climateqa.com/docs/intro/)
                    - Feedbacks on this [form](https://forms.office.com/e/1Yzgxm6jbp)
                                                
                    ### Citation
                    """
                )
                with gr.Accordion(CITATION_LABEL,elem_id="citation", open = False,):
                    # # Display citation label and text)
                    gr.Textbox(
                        value=CITATION_TEXT,
                        label="",
                        interactive=False,
                        show_copy_button=True,
                        lines=len(CITATION_TEXT.split('\n')),
                    )



    def start_chat(query,history):
        history = history + [ChatMessage(role="user", content=query)]
        return (gr.update(interactive = False),gr.update(selected=1),history)
    
    def finish_chat():
        return gr.update(interactive = True,value = "")
    
    # Initialize visibility states
    summary_visible = False
    relevant_visible = False

    # Functions to toggle visibility
    def toggle_summary_visibility():
        global summary_visible
        summary_visible = not summary_visible
        return gr.update(visible=summary_visible)

    def toggle_relevant_visibility():
        global relevant_visible
        relevant_visible = not relevant_visible
        return gr.update(visible=relevant_visible)


    def change_completion_status(current_state):
        current_state = 1 - current_state
        return current_state
    
    def update_sources_number_display(sources_textbox, figures_cards, current_graphs, papers_html):
        sources_number = sources_textbox.count("<h2>")
        figures_number = figures_cards.count("<h2>")
        graphs_number = current_graphs.count("<iframe")
        papers_number = papers_html.count("<h2>")
        sources_notif_label = f"Sources ({sources_number})"
        figures_notif_label = f"Figures ({figures_number})"
        graphs_notif_label = f"Recommended content ({graphs_number})"
        papers_notif_label = f"Papers ({papers_number})"

        return gr.update(label = sources_notif_label), gr.update(label = figures_notif_label), gr.update(label = graphs_notif_label), gr.update(label = papers_notif_label)
    
    (textbox
        .submit(start_chat, [textbox,chatbot], [textbox,tabs,chatbot],queue = False,api_name = "start_chat_textbox")
        .then(chat, [textbox,chatbot,dropdown_audience, dropdown_sources,dropdown_reports, dropdown_external_sources] ,[chatbot,sources_textbox,output_query,output_language, sources_raw, current_graphs],concurrency_limit = 8,api_name = "chat_textbox")
        .then(finish_chat, None, [textbox],api_name = "finish_chat_textbox")
        # .then(update_sources_number_display, [sources_textbox, figures_cards, current_graphs,papers_html],[tab_sources, tab_figures, tab_recommended_content, tab_papers] )
    )

    (examples_hidden
        .change(start_chat, [examples_hidden,chatbot], [textbox,tabs,chatbot],queue = False,api_name = "start_chat_examples")
        .then(chat, [examples_hidden,chatbot,dropdown_audience, dropdown_sources,dropdown_reports, dropdown_external_sources] ,[chatbot,sources_textbox,output_query,output_language, sources_raw, current_graphs],concurrency_limit = 8,api_name = "chat_textbox")
        .then(finish_chat, None, [textbox],api_name = "finish_chat_examples")
        # .then(update_sources_number_display, [sources_textbox, figures_cards, current_graphs,papers_html],[tab_sources, tab_figures, tab_recommended_content, tab_papers] )
    )


    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))]


    sources_raw.change(process_figures, inputs=[sources_raw], outputs=[figures_cards, gallery_component])
    
    
    sources_textbox.change(update_sources_number_display, [sources_textbox, figures_cards, current_graphs,papers_html],[tab_sources, tab_figures, tab_recommended_content, tab_papers])
    figures_cards.change(update_sources_number_display, [sources_textbox, figures_cards, current_graphs,papers_html],[tab_sources, tab_figures, tab_recommended_content, tab_papers])
    current_graphs.change(update_sources_number_display, [sources_textbox, figures_cards, current_graphs,papers_html],[tab_sources, tab_figures, tab_recommended_content, tab_papers])
    papers_html.change(update_sources_number_display, [sources_textbox, figures_cards, current_graphs,papers_html],[tab_sources, tab_figures, tab_recommended_content, tab_papers])

    dropdown_samples.change(change_sample_questions,dropdown_samples,samples)

    textbox.submit(find_papers,[textbox,after, dropdown_external_sources], [papers_html,citations_network,papers_summary])
    examples_hidden.change(find_papers,[examples_hidden,after,dropdown_external_sources], [papers_html,citations_network,papers_summary])

    btn_summary.click(toggle_summary_visibility, outputs=summary_popup)
    btn_relevant_papers.click(toggle_relevant_visibility, outputs=relevant_popup)

    demo.queue()

demo.launch(ssr_mode=False)