import gradio as gr
from utils import create_user_id
# Langchain
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
# ClimateQ&A imports
from anyqa.embeddings import EMBEDDING_MODEL_NAME
from anyqa.llm import get_llm
from anyqa.qa_logging import log
from anyqa.chains import load_qa_chain_with_text
from anyqa.chains import load_reformulation_chain
from anyqa.vectorstore import get_vectorstore
from anyqa.retriever import QARetriever
from anyqa.prompts import audience_prompts
# 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,
}
user_id = create_user_id()
# ---------------------------------------------------------------------------
# ClimateQ&A core functions
# ---------------------------------------------------------------------------
from langchain.callbacks.base import BaseCallbackHandler
from queue import Empty
from threading import Thread
from langchain.schema import LLMResult
from typing import Any, Union, Dict, List
from queue import SimpleQueue
# # Create a Queue
# Q = Queue()
import re
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
job_done = object() # signals the processing is done
class StreamingGradioCallbackHandler(BaseCallbackHandler):
def __init__(self, q: SimpleQueue):
self.q = q
def on_llm_start(
self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
) -> None:
"""Run when LLM starts running. Clean the queue."""
while not self.q.empty():
try:
self.q.get(block=False)
except Empty:
continue
def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
"""Run on new LLM token. Only available when streaming is enabled."""
self.q.put(token)
def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
"""Run when LLM ends running."""
self.q.put(job_done)
def on_llm_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> None:
"""Run when LLM errors."""
self.q.put(job_done)
# Create embeddings function and LLM
embeddings_function = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL_NAME)
# Create vectorstore and retriever
vectorstore = get_vectorstore(embeddings_function)
# ---------------------------------------------------------------------------
# ClimateQ&A Streaming
# From https://github.com/gradio-app/gradio/issues/5345
# And https://stackoverflow.com/questions/76057076/how-to-stream-agents-response-in-langchain
# ---------------------------------------------------------------------------
from threading import Thread
def answer_user(query, query_example, history):
if len(query) <= 2:
raise Exception("Please ask a longer question")
return query, history + [[query, ". . ."]]
def answer_user_example(query, query_example, history):
return query_example, history + [[query_example, ". . ."]]
def fetch_sources(query, sources):
# Prepare default values
if len(sources) == 0:
sources = ["IPCC"]
llm_reformulation = get_llm(
max_tokens=512, temperature=0.0, verbose=True, streaming=False
)
retriever = QARetriever(
vectorstore=vectorstore, sources=[], k_summary=0, k_total=10
)
reformulation_chain = load_reformulation_chain(llm_reformulation)
# Calculate language
output_reformulation = reformulation_chain({"query": query})
question = output_reformulation["question"]
language = output_reformulation["language"]
# Retrieve docs
docs = retriever.get_relevant_documents(question)
if len(docs) > 0:
# Already display the sources
sources_text = []
for i, d in enumerate(docs, 1):
sources_text.append(make_html_source(d, i))
citations_text = "".join(sources_text)
docs_text = "\n\n".join([d.page_content for d in docs])
return "", citations_text, docs_text, question, language
else:
sources_text = (
"⚠️ No relevant passages found in the scientific reports (IPCC and IPBES)"
)
citations_text = "**⚠️ No relevant passages found in the sources, you may want to ask a more specific question.**"
docs_text = ""
return "", citations_text, docs_text, question, language
def answer_bot(query, history, docs, question, language, audience):
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 Queue for streaming LLMs
Q = SimpleQueue()
llm_streaming = get_llm(
max_tokens=1024,
temperature=0.0,
verbose=True,
streaming=True,
callbacks=[StreamingGradioCallbackHandler(Q), StreamingStdOutCallbackHandler()],
)
qa_chain = load_qa_chain_with_text(llm_streaming)
def threaded_chain(question, audience, language, docs):
try:
response = qa_chain(
{
"question": question,
"audience": audience,
"language": language,
"summaries": docs,
}
)
Q.put(response)
Q.put(job_done)
except Exception as e:
print(e)
history[-1][1] = ""
textbox = gr.Textbox(
placeholder=". . .", show_label=False, scale=1, lines=1, interactive=False
)
if len(docs) > 0:
# Start thread for streaming
thread = Thread(
target=threaded_chain,
kwargs={
"question": question,
"audience": audience_prompt,
"language": language,
"docs": docs,
},
)
thread.start()
while True:
next_item = Q.get(block=True) # Blocks until an input is available
if next_item is job_done:
break
elif isinstance(next_item, str):
new_paragraph = history[-1][1] + next_item
new_paragraph = parse_output_llm_with_sources(new_paragraph)
history[-1][1] = new_paragraph
yield textbox, history
else:
pass
thread.join()
log(question=question, history=history, docs=docs, user_id=user_id)
else:
complete_response = "**⚠️ No relevant passages found in the sources, you may want to ask a more specific question.**"
history[-1][1] += complete_response
yield "", history
# ---------------------------------------------------------------------------
# ClimateQ&A core functions
# ---------------------------------------------------------------------------
def make_html_source(source, i):
meta = source.metadata
content = source.page_content.split(":", 1)[1].strip()
link = (
f'🔗'
if "url" in meta
else ""
)
return f"""
Doc {i} - {meta['short_name']} - Page {int(meta['page_number'])}
{content}
"""
def reset_textbox():
return gr.update(value="")
# --------------------------------------------------------------------
# Gradio
# --------------------------------------------------------------------
init_prompt = """
Hello, I'm a conversational assistant. I will answer your questions by **sifting through trusted data sources**.
💡 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 which sources you want me to look for answers. By default, I will search in all sources.
⚠️ 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)
def change_tab():
return gr.Tabs.update(selected=1)
with gr.Blocks(title="❓ Q&A", css="style.css", theme=theme) as demo:
# user_id_state = gr.State([user_id])
with gr.Tab("❓ Q&A"):
with gr.Row(elem_id="chatbot-row"):
with gr.Column(scale=2):
# state = gr.State([system_template])
bot = gr.Chatbot(
value=[[None, init_prompt]],
show_copy_button=True,
show_label=False,
elem_id="chatbot",
layout="panel",
avatar_images=("assets/bot_avatar.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,
)
# submit_button = gr.Button(">",scale = 1,elem_id = "submit-button")
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(elem_id="hidden-message")
questions = [
"How does doaism view our dependence on modern technology?",
"From a doaism perspective, should we embrace or challenge the rise of AI?",
"How might doaism influence sustainable economic practices?",
"Does doaism support the idea of a minimalistic economy over consumerism?",
"How does doaism interpret the dynamics of modern relationships?",
"From a doaism viewpoint, how should society handle conflicts and disagreements?",
"How might doaism guide our approach to mental and physical health?",
"Does doaism offer insights into balancing work-life pressures in the modern age?",
"How does doaism view the purpose and methods of modern education?",
"From a doaism perspective, should learning be more experiential than theoretical?",
]
examples_questions = gr.Examples(
questions,
[examples_hidden],
examples_per_page=10,
run_on_click=False,
# cache_examples=True,
)
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, this tool is multi-lingual!"
)
dropdown_sources = gr.CheckboxGroup(
["IPCC", "IPBES"],
label="Select reports",
value=["IPCC"],
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,
)
(
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])
)
# ---------------------------------------------------------------------------------------
# OTHER TABS
# ---------------------------------------------------------------------------------------
with gr.Tab("ℹ️ About ClimateQ&A", elem_classes="max-height"):
with gr.Row():
with gr.Column(scale=1):
gr.Markdown(
"""
Climate change and environmental disruptions have become some of the most pressing challenges facing our planet today. 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.
However, comprehending the vast and complex scientific information can be daunting, as the scientific consensus references, such as the Intergovernmental Panel on Climate Change (IPCC) reports, span thousands of pages. To bridge this gap and make climate science more accessible, we introduce ClimateQ&A as a tool to distill expert-level knowledge into easily digestible insights about climate science.
💡
How does ClimateQ&A work?
ClimateQ&A harnesses modern OCR techniques to parse and preprocess IPCC reports. By leveraging state-of-the-art question-answering algorithms,
ClimateQ&A is able to sift through the extensive collection of climate scientific reports and identify relevant passages in response to user inquiries. 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.
"""
)
with gr.Column(scale=1):
gr.Markdown("")
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
- 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.
- 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.
"""
)
with gr.Tab("📧 Contact, feedback and feature requests"):
gr.Markdown(
"""
🤞 For any question or press request, contact Théo Alves Da Costa at theo.alvesdacosta@ekimetrics.com
- 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.Tab("📚 Sources", elem_classes="max-height"):
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
IPCC | Technical Summary. 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_TS.pdf | 112 | 2021
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
IPCC | Technical Summary. 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_TechnicalSummary.pdf | 84 | 2022
IPCC | Full Report. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of the WGII to the AR6 of the IPCC. | https://report.ipcc.ch/ar6/wg2/IPCC_AR6_WGII_FullReport.pdf | 3068 | 2022
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
IPCC | Technical Summary. 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_TechnicalSummary.pdf | 102 | 2022
IPCC | Full Report. 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_FullReport.pdf | 2258 | 2022
IPCC | Summary for Policymakers. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty. | https://www.ipcc.ch/site/assets/uploads/sites/2/2022/06/SPM_version_report_LR.pdf | 24 | 2018
IPCC | Summary for Policymakers. In: Climate Change and Land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems. | https://www.ipcc.ch/site/assets/uploads/sites/4/2022/11/SRCCL_SPM.pdf | 36 | 2019
IPCC | Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. | https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/01_SROCC_SPM_FINAL.pdf | 36 | 2019
IPCC | Technical Summary. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. | https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/02_SROCC_TS_FINAL.pdf | 34 | 2019
IPCC | Chapter 1 - Framing and Context of the Report. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. | https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/03_SROCC_Ch01_FINAL.pdf | 60 | 2019
IPCC | Chapter 2 - High Mountain Areas. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. | https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/04_SROCC_Ch02_FINAL.pdf | 72 | 2019
IPCC | Chapter 3 - Polar Regions. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. | https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/05_SROCC_Ch03_FINAL.pdf | 118 | 2019
IPCC | Chapter 4 - Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. | https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/06_SROCC_Ch04_FINAL.pdf | 126 | 2019
IPCC | Chapter 5 - Changing Ocean, Marine Ecosystems, and Dependent Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. | https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/07_SROCC_Ch05_FINAL.pdf | 142 | 2019
IPCC | Chapter 6 - Extremes, Abrupt Changes and Managing Risk. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. | https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/08_SROCC_Ch06_FINAL.pdf | 68 | 2019
IPCC | Cross-Chapter Box 9: Integrative Cross-Chapter Box on Low-Lying Islands and Coasts. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. | https://www.ipcc.ch/site/assets/uploads/sites/3/2019/11/11_SROCC_CCB9-LLIC_FINAL.pdf | 18 | 2019
IPCC | Annex I: Glossary [Weyer, N.M. (ed.)]. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. | https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/10_SROCC_AnnexI-Glossary_FINAL.pdf | 28 | 2019
IPBES | Full Report. Global assessment report on biodiversity and ecosystem services of the IPBES. | https://zenodo.org/record/6417333/files/202206_IPBES%20GLOBAL%20REPORT_FULL_DIGITAL_MARCH%202022.pdf | 1148 | 2019
IPBES | Summary for Policymakers. Global assessment report on biodiversity and ecosystem services of the IPBES (Version 1). | https://zenodo.org/record/3553579/files/ipbes_global_assessment_report_summary_for_policymakers.pdf | 60 | 2019
IPBES | Full Report. Thematic assessment of the sustainable use of wild species of the IPBES. | https://zenodo.org/record/7755805/files/IPBES_ASSESSMENT_SUWS_FULL_REPORT.pdf | 1008 | 2022
IPBES | Summary for Policymakers. Summary for policymakers of the thematic assessment of the sustainable use of wild species of the IPBES. | https://zenodo.org/record/7411847/files/EN_SPM_SUSTAINABLE%20USE%20OF%20WILD%20SPECIES.pdf | 44 | 2022
IPBES | Full Report. Regional Assessment Report on Biodiversity and Ecosystem Services for Africa. | https://zenodo.org/record/3236178/files/ipbes_assessment_report_africa_EN.pdf | 494 | 2018
IPBES | Summary for Policymakers. Regional Assessment Report on Biodiversity and Ecosystem Services for Africa. | https://zenodo.org/record/3236189/files/ipbes_assessment_spm_africa_EN.pdf | 52 | 2018
IPBES | Full Report. Regional Assessment Report on Biodiversity and Ecosystem Services for the Americas. | https://zenodo.org/record/3236253/files/ipbes_assessment_report_americas_EN.pdf | 660 | 2018
IPBES | Summary for Policymakers. Regional Assessment Report on Biodiversity and Ecosystem Services for the Americas. | https://zenodo.org/record/3236292/files/ipbes_assessment_spm_americas_EN.pdf | 44 | 2018
IPBES | Full Report. Regional Assessment Report on Biodiversity and Ecosystem Services for Asia and the Pacific. | https://zenodo.org/record/3237374/files/ipbes_assessment_report_ap_EN.pdf | 616 | 2018
IPBES | Summary for Policymakers. Regional Assessment Report on Biodiversity and Ecosystem Services for Asia and the Pacific. | https://zenodo.org/record/3237383/files/ipbes_assessment_spm_ap_EN.pdf | 44 | 2018
IPBES | Full Report. Regional Assessment Report on Biodiversity and Ecosystem Services for Europe and Central Asia. | https://zenodo.org/record/3237429/files/ipbes_assessment_report_eca_EN.pdf | 894 | 2018
IPBES | Summary for Policymakers. Regional Assessment Report on Biodiversity and Ecosystem Services for Europe and Central Asia. | https://zenodo.org/record/3237468/files/ipbes_assessment_spm_eca_EN.pdf | 52 | 2018
IPBES | Full Report. Assessment Report on Land Degradation and Restoration. | https://zenodo.org/record/3237393/files/ipbes_assessment_report_ldra_EN.pdf | 748 | 2018
IPBES | Summary for Policymakers. Assessment Report on Land Degradation and Restoration. | https://zenodo.org/record/3237393/files/ipbes_assessment_report_ldra_EN.pdf | 48 | 2018
"""
)
with gr.Tab("🛢️ Carbon Footprint"):
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 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"):
gr.Markdown(
"""
##### v1.0.0 - 2023-10-25
- Cloned from ClimateQ&A
- Added support for other topics
"""
)
demo.queue(concurrency_count=16)
demo.launch()