app.py

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


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

from io import BytesIO
import base64

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

from utils import create_user_id



# ClimateQ&A imports
from climateqa.engine.llm import get_llm
from climateqa.engine.rag import make_rag_chain
from climateqa.engine.vectorstore import get_pinecone_vectorstore
from climateqa.engine.retriever import ClimateQARetriever
from climateqa.engine.embeddings import get_embeddings_function
from climateqa.engine.prompts import audience_prompts
from climateqa.sample_questions import QUESTIONS
from climateqa.constants import POSSIBLE_REPORTS
from climateqa.utils import get_image_from_azure_blob_storage

# 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()



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"<span class='doc-ref'><sup>{subpart}</sup></span>" for subpart in subparts]
            parts.append("".join(subparts))
        else:
            parts.append(part)
    content_parts = "".join(parts)
    return content_parts


# Create vectorstore and retriever
vectorstore = get_pinecone_vectorstore(embeddings_function)
llm = get_llm(max_tokens = 1024,temperature = 0.0)


def make_pairs(lst):
    """from a list of even lenght, make tupple pairs"""
    return [(lst[i], lst[i + 1]) for i in range(0, len(lst), 2)]


def serialize_docs(docs):
    new_docs = []
    for doc in docs:
        new_doc = {}
        new_doc["page_content"] = doc.page_content
        new_doc["metadata"] = doc.metadata
        new_docs.append(new_doc)
    return new_docs


# import asyncio
# from typing import Any, Dict, List
# from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackHandler

# class MyCustomAsyncHandler(AsyncCallbackHandler):
#     """Async callback handler that can be used to handle callbacks from langchain."""

#     async def on_chain_start(
#         self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
#     ) -> Any:
#         """Run when chain starts running."""
#         print("zzzz....")
#         await asyncio.sleep(3)
#         print(f"on_chain_start {serialized['name']}")
#         # raise gr.Error("ClimateQ&A Error: Timeout, try another question and if the error remains, you can contact us :)")



async def chat(query,history,audience,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)"""

    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 = ["IPCC"]

    if len(reports) == 0:
        reports = []


    retriever = ClimateQARetriever(vectorstore=vectorstore,sources = sources,min_size = 200,reports = reports,k_summary = 3,k_total = 15,threshold=0.5)
    rag_chain = make_rag_chain(retriever,llm)

    # gradio_format = make_pairs([a.content for a in history]) + [(query, "")]
    # history = history + [(query,"")]
    # print(history)
    # print(gradio_format)

    # # reset memory
    # memory.clear()
    # for message in history:
    #     memory.chat_memory.add_message(message)
    
    inputs = {"query": query,"audience": audience_prompt}
    result = rag_chain.astream_log(inputs) #{"callbacks":[MyCustomAsyncHandler()]})
    # result = rag_chain.stream(inputs)

    reformulated_question_path_id = "/logs/flatten_dict/final_output"
    retriever_path_id = "/logs/Retriever/final_output"
    streaming_output_path_id = "/logs/AzureChatOpenAI:2/streamed_output_str/-"
    final_output_path_id = "/streamed_output/-"

    docs_html = ""
    output_query = ""
    output_language = ""
    gallery = []

    # for output in result:

    #     if "language" in output:
    #         output_language = output["language"]
    #     if "question" in output:
    #         output_query = output["question"]
    #     if "docs" in output:

    #         try:
    #             docs = output['docs'] # List[Document]
    #             docs_html = []
    #             for i, d in enumerate(docs, 1):
    #                 docs_html.append(make_html_source(d, i))
    #             docs_html = "".join(docs_html)
    #         except TypeError:
    #             print("No documents found")
    #             continue

    #     if "answer" in output:
    #         new_token = output["answer"] # str
    #         time.sleep(0.03)
    #         answer_yet = history[-1][1] + new_token
    #         answer_yet = parse_output_llm_with_sources(answer_yet)
    #         history[-1] = (query,answer_yet)

    #     yield history,docs_html,output_query,output_language,gallery



    # async def fallback_iterator(iterable):
    #     async for item in iterable:
    #         try:
    #             yield item
    #         except Exception as e:
    #             print(f"Error in fallback iterator: {e}")
    #             raise gr.Error(f"ClimateQ&A Error: {e}\nThe error has been noted, try another question and if the error remains, you can contact us :)")

    try:
        async for op in result:


            op = op.ops[0]
            # print("ITERATION",op)

            if op['path'] == reformulated_question_path_id: # reforulated question
                try:
                    output_language = op['value']["language"] # str
                    output_query = op["value"]["question"]
                except Exception as e:
                    raise gr.Error(f"ClimateQ&A Error: {e} - The error has been noted, try another question and if the error remains, you can contact us :)")
            
            elif op['path'] == retriever_path_id: # documents
                try:
                    docs = op['value']['documents'] # List[Document]
                    docs_html = []
                    for i, d in enumerate(docs, 1):
                        docs_html.append(make_html_source(d, i))
                    docs_html = "".join(docs_html)
                except TypeError:
                    print("No documents found")
                    print("op: ",op)
                    continue

            elif op['path'] == streaming_output_path_id: # final answer
                new_token = op['value'] # str
                time.sleep(0.01)
                answer_yet = history[-1][1] + new_token
                answer_yet = parse_output_llm_with_sources(answer_yet)
                history[-1] = (query,answer_yet)

        
            # elif op['path'] == final_output_path_id:
            #     final_output = op['value']

            #     if "answer" in final_output:
                
            #         final_output = final_output["answer"]
            #         print(final_output)
            #         answer = history[-1][1] + final_output
            #         answer = parse_output_llm_with_sources(answer)
            #         history[-1] = (query,answer)

            else:
                continue

            history = [tuple(x) for x in history]
            yield history,docs_html,output_query,output_language,gallery

    except Exception as e:
        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][0]
            logs = {
                "user_id": str(user_id),
                "prompt": prompt,
                "query": prompt,
                "question":output_query,
                "sources":sources,
                "docs":serialize_docs(docs),
                "answer": history[-1][1],
                "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 :)")

    image_dict = {}
    for i,doc in enumerate(docs):
        
        if doc.metadata["chunk_type"] == "image":
            try:
                key = f"Image {i}"
                image_path = doc.metadata["image_path"].split("documents/")[1]
                img = get_image_from_azure_blob_storage(image_path)

                # Convert the image to a byte buffer
                buffered = BytesIO()
                img.save(buffered, format="PNG")
                img_str = base64.b64encode(buffered.getvalue()).decode()

                # Embedding the base64 string in Markdown
                markdown_image = f"![Alt text](data:image/png;base64,{img_str})"
                image_dict[key] = {"img":img,"md":markdown_image,"caption":doc.page_content,"key":key,"figure_code":doc.metadata["figure_code"]}
            except Exception as e:
                print(f"Skipped adding image {i} because of {e}")

    if len(image_dict) > 0:

        gallery = [x["img"] for x in list(image_dict.values())]
        img = list(image_dict.values())[0]
        img_md = img["md"]
        img_caption = img["caption"]
        img_code = img["figure_code"]
        if img_code != "N/A":
            img_name = f"{img['key']} - {img['figure_code']}"
        else:
            img_name = f"{img['key']}"

        answer_yet = history[-1][1] + f"\n\n{img_md}\n<p class='chatbot-caption'><b>{img_name}</b> - {img_caption}</p>"
        history[-1] = (history[-1][0],answer_yet)
        history = [tuple(x) for x in history]

    # gallery = [x.metadata["image_path"] for x in docs if (len(x.metadata["image_path"]) > 0 and "IAS" in x.metadata["image_path"])]
    # if len(gallery) > 0:
    #     gallery = list(set("|".join(gallery).split("|")))
    #     gallery = [get_image_from_azure_blob_storage(x) for x in gallery]

    yield history,docs_html,output_query,output_language,gallery


    # memory.save_context(inputs, {"answer": gradio_format[-1][1]})
    # yield gradio_format, memory.load_memory_variables({})["history"], source_string
    
# async def chat_with_timeout(query, history, audience, sources, reports, timeout_seconds=2):
#     async def timeout_gen(async_gen, timeout):
#         try:
#             while True:
#                 try:
#                     yield await asyncio.wait_for(async_gen.__anext__(), timeout)
#                 except StopAsyncIteration:
#                     break
#         except asyncio.TimeoutError:
#             raise gr.Error("Operation timed out. Please try again.")

#     return timeout_gen(chat(query, history, audience, sources, reports), timeout_seconds)



# # A wrapper function that includes a timeout
# async def chat_with_timeout(query, history, audience, sources, reports, timeout_seconds=2):
#     try:
#         # Use asyncio.wait_for to apply a timeout to the chat function
#         return await asyncio.wait_for(chat(query, history, audience, sources, reports), timeout_seconds)
#     except asyncio.TimeoutError:
#         # Handle the timeout error as desired
#         raise gr.Error("Operation timed out. Please try again.")




def make_html_source(source,i):
    meta = source.metadata
    # content = source.page_content.split(":",1)[1].strip()
    content = source.page_content.strip()

    toc_levels = []
    for j in range(2):
        level = meta[f"toc_level{j}"]
        if level != "N/A":
            toc_levels.append(level)
        else:
            break
    toc_levels = " > ".join(toc_levels)
    print(toc_levels)

    if len(toc_levels) > 0:
        name = f"<b>{toc_levels}</b><br/>{meta['name']}"
    else:
        name = meta['name']

    print(name)


    if meta["chunk_type"] == "text":

        card = f"""
    <div class="card">
        <div class="card-content">
            <h2>Doc {i} - {meta['short_name']} - Page {int(meta['page_number'])}</h2>
            <p>{content}</p>
        </div>
        <div class="card-footer">
            <span>{name}</span>
            <a href="{meta['url']}#page={int(meta['page_number'])}" target="_blank" class="pdf-link">
                <span role="img" aria-label="Open PDF">🔗</span>
            </a>
        </div>
    </div>
    """
    
    else:

        if meta["figure_code"] != "N/A":
            title = f"{meta['figure_code']} - {meta['short_name']}"
        else:
            title = f"{meta['short_name']}"

        card = f"""
    <div class="card card-image">
        <div class="card-content">
            <h2>Image {i} - {title} - Page {int(meta['page_number'])}</h2>
            <p>{content}</p>
            <p class='ai-generated'>AI-generated description</p>
        </div>
        <div class="card-footer">
            <span>{name}</span>
            <a href="{meta['url']}#page={int(meta['page_number'])}" target="_blank" class="pdf-link">
                <span role="img" aria-label="Open PDF">🔗</span>
            </a>
        </div>
    </div>
    """
        
    return card



#     else:
#         docs_string = "No relevant passages found in the climate science reports (IPCC and IPBES)"
#         complete_response = "**No relevant passages found in the climate science reports (IPCC and IPBES), you may want to ask a more specific question (specifying your question on climate issues).**"
#         messages.append({"role": "assistant", "content": complete_response})
#         gradio_format = make_pairs([a["content"] for a in messages[1:]])
#         yield gradio_format, messages, docs_string


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)
    print("Uploading logs to Azure Blob Storage")
    print("----------------------------------")
    print("")
    print(logs)
    file_client.upload_file(logs)
    print("Logs uploaded to Azure Blob Storage")


# --------------------------------------------------------------------
# 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.*

What do you want to learn ?
"""


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



with gr.Blocks(title="Climate Q&A", css="style.css", theme=theme,elem_id = "main-component") as demo:
    # user_id_state = gr.State([user_id])

    with gr.Tab("ClimateQ&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,"https://i.ibb.co/YNyd5W2/logo4.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=1,lines = 1,interactive = True)


            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("Citations",elem_id = "tab-citations",id = 1):
                        sources_textbox = gr.HTML(show_label=False, elem_id="sources-textbox")
                        docs_textbox = gr.State("")

                    with gr.Tab("Configuration",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"],
                            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,
                        )

                        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)





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


    with gr.Tab("Figures",elem_id = "tab-images",elem_classes = "max-height other-tabs"):
        gallery_component = gr.Gallery()

    with gr.Tab("About ClimateQ&A",elem_classes = "max-height other-tabs"):
        with gr.Row():
            with gr.Column(scale=1):
                gr.Markdown(
                    """
    <p><b>Climate change and environmental disruptions have become some of the most pressing challenges facing our planet today</b>. As global temperatures rise and ecosystems suffer, it is essential for individuals to understand the gravity of the situation in order to make informed decisions and advocate for appropriate policy changes.</p>
    <p>However, comprehending the vast and complex scientific information can be daunting, as the scientific consensus references, such as <b>the Intergovernmental Panel on Climate Change (IPCC) reports, span thousands of pages</b>. To bridge this gap and make climate science more accessible, we introduce <b>ClimateQ&A as a tool to distill expert-level knowledge into easily digestible insights about climate science.</b></p>
    <div class="tip-box">
    <div class="tip-box-title">
        <span class="light-bulb" role="img" aria-label="Light Bulb">💡</span>
        How does ClimateQ&A work?
    </div>
    ClimateQ&A harnesses modern OCR techniques to parse and preprocess IPCC reports. By leveraging state-of-the-art question-answering algorithms, <i>ClimateQ&A is able to sift through the extensive collection of climate scientific reports and identify relevant passages in response to user inquiries</i>. Furthermore, the integration of the ChatGPT API allows ClimateQ&A to present complex data in a user-friendly manner, summarizing key points and facilitating communication of climate science to a wider audience.
    </div>
    """
                )

            with gr.Column(scale=1):
                gr.Markdown("![](https://i.postimg.cc/fLvsvMzM/Untitled-design-5.png)")
                gr.Markdown("*Source : IPCC AR6 - Synthesis Report of the IPCC 6th assessment report (AR6)*")

        gr.Markdown("## How to use ClimateQ&A")
        with gr.Row():
            with gr.Column(scale=1):
                gr.Markdown(
                    """
        ### Getting started
        - In the chatbot section, simply type your climate-related question, and ClimateQ&A will provide an answer with references to relevant IPCC reports.
            - ClimateQ&A retrieves specific passages from the IPCC reports to help answer your question accurately.
            - Source information, including page numbers and passages, is displayed on the right side of the screen for easy verification.
            - Feel free to ask follow-up questions within the chatbot for a more in-depth understanding.
            - You can ask question in any language, ClimateQ&A is multi-lingual !
        - ClimateQ&A integrates multiple sources (IPCC and IPBES, … ) to cover various aspects of environmental science, such as climate change and biodiversity. See all sources used below.
        """
                )
            with gr.Column(scale=1):
                gr.Markdown(
                    """
        ### Limitations
        <div class="warning-box">
        <ul>
            <li>Please note that, like any AI, the model may occasionally generate an inaccurate or imprecise answer. Always refer to the provided sources to verify the validity of the information given. If you find any issues with the response, kindly provide feedback to help improve the system.</li>
            <li>ClimateQ&A is specifically designed for climate-related inquiries. If you ask a non-environmental question, the chatbot will politely remind you that its focus is on climate and environmental issues.</li>
        </div>
        """
                )


    with gr.Tab("Contact, feedback and feature requests",elem_classes = "max-height other-tabs"):
        gr.Markdown(
            """

        For any question or press request, contact Théo Alves Da Costa at <b>theo.alvesdacosta@ekimetrics.com</b>

        - ClimateQ&A welcomes community contributions. To participate, head over to the Community Tab and create a "New Discussion" to ask questions and share your insights.
        - Provide feedback through email, letting us know which insights you found accurate, useful, or not. Your input will help us improve the platform.
        - Only a few sources (see below) are integrated (all IPCC, IPBES), if you are a climate science researcher and net to sift through another report, please let us know.
        
        *This tool has been developed by the R&D lab at **Ekimetrics** (Jean Lelong, Nina Achache, Gabriel Olympie, Nicolas Chesneau, Natalia De la Calzada, Théo Alves Da Costa)*
        """
        )
    # with gr.Row():
    #     with gr.Column(scale=1):
    #         gr.Markdown("### Feedbacks")
    #         feedback = gr.Textbox(label="Write your feedback here")
    #         feedback_output = gr.Textbox(label="Submit status")
    #         feedback_save = gr.Button(value="submit feedback")
    #         feedback_save.click(
    #             save_feedback,
    #             inputs=[feedback, user_id_state],
    #             outputs=feedback_output,
    #         )
    #         gr.Markdown(
    #             "If you need us to ask another climate science report or ask any question, contact us at <b>theo.alvesdacosta@ekimetrics.com</b>"
    #         )

    #     with gr.Column(scale=1):
    #         gr.Markdown("### OpenAI API")
    #         gr.Markdown(
    #             "To make climate science accessible to a wider audience, we have opened our own OpenAI API key with a monthly cap of $1000. If you already have an API key, please use it to help conserve bandwidth for others."
    #         )
    #         openai_api_key_textbox = gr.Textbox(
    #             placeholder="Paste your OpenAI API key (sk-...) and hit Enter",
    #             show_label=False,
    #             lines=1,
    #             type="password",
    #         )
    # openai_api_key_textbox.change(set_openai_api_key, inputs=[openai_api_key_textbox])
    # openai_api_key_textbox.submit(set_openai_api_key, inputs=[openai_api_key_textbox])

    with gr.Tab("Sources",elem_classes = "max-height other-tabs"):
        gr.Markdown("""
    | Source | Report | URL | Number of pages | Release date |
    | --- | --- | --- | --- | --- |
    IPCC | Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC. | https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf | 32 | 2021
    IPCC | Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC. | https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf | 2409 | 2021
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    IPCC | Summary for Policymakers. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of the WGII to the AR6 of the IPCC. | https://www.ipcc.ch/report/ar6/wg2/downloads/report/IPCC_AR6_WGII_SummaryForPolicymakers.pdf | 34 | 2022
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    IPCC | Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of the WGIII to the AR6 of the IPCC. | https://www.ipcc.ch/report/ar6/wg3/downloads/report/IPCC_AR6_WGIII_SummaryForPolicymakers.pdf | 50 | 2022
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""")

    with gr.Tab("Carbon Footprint",elem_classes = "max-height other-tabs"):
        gr.Markdown("""

Carbon emissions were measured during the development and inference process using CodeCarbon [https://github.com/mlco2/codecarbon](https://github.com/mlco2/codecarbon)

| Phase | Description | Emissions | Source |
| --- | --- | --- | --- |
| Development  | OCR and parsing all pdf documents with AI | 28gCO2e | CodeCarbon |
| Development | Question Answering development | 114gCO2e | CodeCarbon |
| Inference | Question Answering | ~0.102gCO2e / call | CodeCarbon |
| Inference | API call to turbo-GPT | ~0.38gCO2e / call | https://medium.com/@chrispointon/the-carbon-footprint-of-chatgpt-e1bc14e4cc2a |

Carbon Emissions are **relatively low but not negligible** compared to other usages: one question asked to ClimateQ&A is around 0.482gCO2e - equivalent to 2.2m by car (https://datagir.ademe.fr/apps/impact-co2/)  
Or around 2 to 4 times more than a typical Google search. 
"""
    )
        
    with gr.Tab("Changelog",elem_classes = "max-height other-tabs"):
        gr.Markdown("""
                    
##### Upcoming features
- Figures retrieval and multimodal system
- Conversational chat
- Intent routing
- Local environment setup
                    
##### v1.3.0-alpha - *2024-01-31*
- Completely redesign chunking system mechanism at the parsing phase (hierarchical aggregations, better control to get 256 tokens)
- Switched to Pinecone serverless for performance improvements
- Add TOC section for each source in the citations panel as breadcrumbs
- *EXPERIMENTAL* - Figure retrieval system within the answer (caption pictures with GPT4 Vision and indexing the caption in the retrieval system)
                    
##### v1.2.1 - *2024-01-16*
- *BUG* - corrected asynchronous bug failing the chatbot frequently
                    
##### v1.2.0 - *2023-11-27*
- Added new IPBES assessment on Invasive Species (SPM and chapters)
- Switched all the codebase to LCEL (Langchain Expression Language)
- Added sample questions by category
- Switched embeddings from old ``sentence-transformers/multi-qa-mpnet-base-dot-v1`` to ``BAAI/bge-base-en-v1.5``
- Report filtering to select directly the report you want to source your answers from
- First naive version of a figures retrieval system by looking up the figures in the retrieved pages

##### v1.1.0 - *2023-10-16*
- ClimateQ&A on Hugging Face is finally working again with all the new features !
- Switched all python code to langchain codebase for cleaner code, easier maintenance and future features
- Updated GPT model to August version
- Added streaming response to improve UX
- Created a custom Retriever chain to avoid calling the LLM if there is no documents retrieved
- Use of HuggingFace embed on https://climateqa.com to avoid demultiplying deployments
                    
##### v1.0.0 - *2023-05-11*
- First version of clean interface on https://climateqa.com
- Add children mode on https://climateqa.com
- Add follow-up questions https://climateqa.com
"""
    )




    def start_chat(query,history):
        history = history + [(query,"")]
        history = [tuple(x) for x in history]
        print(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_audience, dropdown_sources,dropdown_reports], [chatbot,sources_textbox,output_query,output_language,gallery_component],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_audience, dropdown_sources,dropdown_reports], [chatbot,sources_textbox,output_query,output_language,gallery_component],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)

    # # textbox.submit(predict_climateqa,[textbox,bot],[None,bot,sources_textbox])
    # (textbox
    #     .submit(answer_user, [textbox,examples_hidden, bot], [textbox, bot],queue = False)
    #     .success(change_tab,None,tabs)
    #     .success(fetch_sources,[textbox,dropdown_sources], [textbox,sources_textbox,docs_textbox,output_query,output_language])
    #     .success(answer_bot, [textbox,bot,docs_textbox,output_query,output_language,dropdown_audience], [textbox,bot],queue = True)
    #     .success(lambda x : textbox,[textbox],[textbox])
    # )

    # (examples_hidden
    #     .change(answer_user_example, [textbox,examples_hidden, bot], [textbox, bot],queue = False)
    #     .success(change_tab,None,tabs)
    #     .success(fetch_sources,[textbox,dropdown_sources], [textbox,sources_textbox,docs_textbox,output_query,output_language])
    #     .success(answer_bot, [textbox,bot,docs_textbox,output_query,output_language,dropdown_audience], [textbox,bot],queue=True)
    #     .success(lambda x : textbox,[textbox],[textbox])
    # )
    # submit_button.click(answer_user, [textbox, bot], [textbox, bot], queue=True).then(
    #         answer_bot, [textbox,bot,dropdown_audience,dropdown_sources], [textbox,bot,sources_textbox]
    #     )


    demo.queue()

demo.launch()