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
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
# 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
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,display_graph
from front.utils import make_html_source,parse_output_llm_with_sources,serialize_docs,make_toolbox
# 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()
# Create vectorstore and retriever
vectorstore = get_pinecone_vectorstore(embeddings_function)
llm = get_llm(provider="openai",max_tokens = 1024,temperature = 0.0)
reranker = get_reranker("large")
agent = make_graph_agent(llm,vectorstore,reranker)
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)"""
date_now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
print(f">> NEW QUESTION ({date_now}) : {query}")
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: # TODO
reports = []
inputs = {"user_input": query,"audience": audience_prompt,"sources":sources}
result = agent.astream_events(inputs,version = "v1")
# path_reformulation = "/logs/reformulation/final_output"
# path_keywords = "/logs/keywords/final_output"
# path_retriever = "/logs/find_documents/final_output"
# path_answer = "/logs/answer/streamed_output_str/-"
docs = []
docs_html = ""
output_query = ""
output_language = ""
output_keywords = ""
gallery = []
start_streaming = False
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
try:
docs = event["data"]["output"]["documents"]
docs_html = []
for i, d in enumerate(docs, 1):
docs_html.append(make_html_source(d, i))
used_documents = used_documents + [d.metadata["name"] for d in docs]
history[-1].content = "Adding sources :\n\n - " + "\n - ".join(np.unique(used_documents))
docs_html = "".join(docs_html)
except Exception as e:
print(f"Error getting documents: {e}")
print(event)
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"]:# if streaming answer
if start_streaming == False:
start_streaming = True
history.append(ChatMessage(role="assistant", content = ""))
answer_message_content += event["data"]["chunk"].content
answer_message_content = parse_output_llm_with_sources(answer_message_content)
history[-1] = ChatMessage(role="assistant", content = answer_message_content)
# history.append(ChatMessage(role="assistant", content = new_message_content))
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,gallery #,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 :)")
image_dict = {}
for i,doc in enumerate(docs):
if doc.metadata["chunk_type"] == "image":
try:
key = f"Image {i+1}"
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""
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']}"
history.append(ChatMessage(role="assistant", content = f"\n\n{img_md}\n{img_name} - {img_caption}
"))
# answer_yet = history[-1][1] + f"\n\n{img_md}\n{img_name} - {img_caption}
"
# history[-1] = (history[-1][0],answer_yet)
# history = [tuple(x) for x in history]
yield history,docs_html,output_query,output_language,gallery#,output_query,output_keywords
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 = {
"doc":50,
"id":100,
"title":300,
"doi":100,
"publication_year":100,
"abstract":500,
"rerank_score":100,
"is_oa":50,
}
papers_cols = list(papers_cols_widths.keys())
papers_cols_widths = list(papers_cols_widths.values())
# --------------------------------------------------------------------
# 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_paths=os.getcwd()+ "/style.css", theme=theme,elem_id = "main-component") as demo:
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"),
)
# 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=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("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("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","IPOS"],
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("Papers (beta)",elem_id = "tab-papers",elem_classes = "max-height other-tabs"):
# with gr.Row():
# with gr.Column(scale=1):
# query_papers = gr.Textbox(placeholder="Question",show_label=False,lines = 1,interactive = True,elem_id="query-papers")
# keywords_papers = gr.Textbox(placeholder="Keywords",show_label=False,lines = 1,interactive = True,elem_id="keywords-papers")
# after = gr.Slider(minimum=1950,maximum=2023,step=1,value=1960,label="Publication date",show_label=True,interactive=True,elem_id="date-papers")
# search_papers = gr.Button("Search",elem_id="search-papers",interactive=True)
# with gr.Column(scale=7):
# with gr.Tab("Summary",elem_id="papers-summary-tab"):
# papers_summary = gr.Markdown(visible=True,elem_id="papers-summary")
# with gr.Tab("Relevant papers",elem_id="papers-results-tab"):
# papers_dataframe = gr.Dataframe(visible=True,elem_id="papers-table",headers = papers_cols)
# with gr.Tab("Citations network",elem_id="papers-network-tab"):
# citations_network = gr.HTML(visible=True,elem_id="papers-citations-network")
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://climateqa.com](https://climateqa.com/docs/intro/)")
def start_chat(query,history):
# history = history + [(query,None)]
# history = [tuple(x) for x in 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 = ""))
(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)
demo.queue()
demo.launch(ssr_mode=False)