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from climateqa.engine.embeddings import get_embeddings_function |
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embeddings_function = get_embeddings_function() |
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from sentence_transformers import CrossEncoder |
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import gradio as gr |
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from gradio_modal import Modal |
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import pandas as pd |
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import numpy as np |
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import os |
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import time |
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import re |
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import json |
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from gradio import ChatMessage |
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from io import BytesIO |
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import base64 |
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from datetime import datetime |
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from azure.storage.fileshare import ShareServiceClient |
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from utils import create_user_id |
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from gradio_modal import Modal |
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from PIL import Image |
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from langchain_core.runnables.schema import StreamEvent |
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from climateqa.engine.llm import get_llm |
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from climateqa.engine.vectorstore import get_pinecone_vectorstore |
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from climateqa.engine.reranker import get_reranker |
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from climateqa.engine.embeddings import get_embeddings_function |
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from climateqa.engine.chains.prompts import audience_prompts |
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from climateqa.sample_questions import QUESTIONS |
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from climateqa.constants import POSSIBLE_REPORTS, OWID_CATEGORIES |
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from climateqa.utils import get_image_from_azure_blob_storage |
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from climateqa.engine.graph import make_graph_agent |
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from climateqa.engine.embeddings import get_embeddings_function |
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from climateqa.engine.chains.retrieve_papers import find_papers |
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from front.utils import serialize_docs,process_figures |
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from climateqa.event_handler import init_audience, handle_retrieved_documents, stream_answer,handle_retrieved_owid_graphs |
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try: |
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from dotenv import load_dotenv |
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load_dotenv() |
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except Exception as e: |
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pass |
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import requests |
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theme = gr.themes.Base( |
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primary_hue="blue", |
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secondary_hue="red", |
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font=[gr.themes.GoogleFont("Poppins"), "ui-sans-serif", "system-ui", "sans-serif"], |
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) |
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init_prompt = "" |
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system_template = { |
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"role": "system", |
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"content": init_prompt, |
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} |
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account_key = os.environ["BLOB_ACCOUNT_KEY"] |
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if len(account_key) == 86: |
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account_key += "==" |
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credential = { |
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"account_key": account_key, |
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"account_name": os.environ["BLOB_ACCOUNT_NAME"], |
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} |
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account_url = os.environ["BLOB_ACCOUNT_URL"] |
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file_share_name = "climateqa" |
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service = ShareServiceClient(account_url=account_url, credential=credential) |
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share_client = service.get_share_client(file_share_name) |
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user_id = create_user_id() |
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CITATION_LABEL = "BibTeX citation for ClimateQ&A" |
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CITATION_TEXT = r"""@misc{climateqa, |
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author={Théo Alves Da Costa, Timothée Bohe}, |
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title={ClimateQ&A, AI-powered conversational assistant for climate change and biodiversity loss}, |
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year={2024}, |
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howpublished= {\url{https://climateqa.com}}, |
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} |
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@software{climateqa, |
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author = {Théo Alves Da Costa, Timothée Bohe}, |
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publisher = {ClimateQ&A}, |
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title = {ClimateQ&A, AI-powered conversational assistant for climate change and biodiversity loss}, |
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} |
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""" |
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vectorstore = get_pinecone_vectorstore(embeddings_function, index_name = os.getenv("PINECONE_API_INDEX")) |
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vectorstore_graphs = get_pinecone_vectorstore(embeddings_function, index_name = os.getenv("PINECONE_API_INDEX_OWID"), text_key="description") |
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llm = get_llm(provider="openai",max_tokens = 1024,temperature = 0.0) |
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reranker = get_reranker("nano") |
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agent = make_graph_agent(llm=llm, vectorstore_ipcc=vectorstore, vectorstore_graphs=vectorstore_graphs, reranker=reranker) |
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def update_config_modal_visibility(config_open): |
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new_config_visibility_status = not config_open |
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return gr.update(visible=new_config_visibility_status), new_config_visibility_status |
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async def chat(query, history, audience, sources, reports, relevant_content_sources, search_only): |
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"""taking a query and a message history, use a pipeline (reformulation, retriever, answering) to yield a tuple of: |
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(messages in gradio format, messages in langchain format, source documents)""" |
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date_now = datetime.now().strftime("%Y-%m-%d %H:%M:%S") |
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print(f">> NEW QUESTION ({date_now}) : {query}") |
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audience_prompt = init_audience(audience) |
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if sources is None or len(sources) == 0: |
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sources = ["IPCC", "IPBES", "IPOS"] |
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if reports is None or len(reports) == 0: |
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reports = [] |
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inputs = {"user_input": query,"audience": audience_prompt,"sources_input":sources, "relevant_content_sources" : relevant_content_sources, "search_only": search_only} |
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result = agent.astream_events(inputs,version = "v1") |
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docs = [] |
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used_figures=[] |
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related_contents = [] |
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docs_html = "" |
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output_query = "" |
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output_language = "" |
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output_keywords = "" |
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start_streaming = False |
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graphs_html = "" |
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figures = '<div class="figures-container"><p></p> </div>' |
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steps_display = { |
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"categorize_intent":("🔄️ Analyzing user message",True), |
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"transform_query":("🔄️ Thinking step by step to answer the question",True), |
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"retrieve_documents":("🔄️ Searching in the knowledge base",False), |
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} |
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used_documents = [] |
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answer_message_content = "" |
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try: |
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async for event in result: |
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if "langgraph_node" in event["metadata"]: |
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node = event["metadata"]["langgraph_node"] |
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if event["event"] == "on_chain_end" and event["name"] == "retrieve_documents" : |
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docs, docs_html, history, used_documents, related_contents = handle_retrieved_documents(event, history, used_documents) |
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elif event["event"] == "on_chain_end" and node == "categorize_intent" and event["name"] == "_write": |
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intent = event["data"]["output"]["intent"] |
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if "language" in event["data"]["output"]: |
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output_language = event["data"]["output"]["language"] |
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else : |
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output_language = "English" |
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history[-1].content = f"Language identified : {output_language} \n Intent identified : {intent}" |
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elif event["name"] in steps_display.keys() and event["event"] == "on_chain_start": |
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event_description, display_output = steps_display[node] |
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if not hasattr(history[-1], 'metadata') or history[-1].metadata["title"] != event_description: |
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history.append(ChatMessage(role="assistant", content = "", metadata={'title' :event_description})) |
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elif event["name"] != "transform_query" and event["event"] == "on_chat_model_stream" and node in ["answer_rag", "answer_search","answer_chitchat"]: |
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history, start_streaming, answer_message_content = stream_answer(history, event, start_streaming, answer_message_content) |
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elif event["name"] in ["retrieve_graphs", "retrieve_graphs_ai"] and event["event"] == "on_chain_end": |
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graphs_html = handle_retrieved_owid_graphs(event, graphs_html) |
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if event["name"] == "transform_query" and event["event"] =="on_chain_end": |
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if hasattr(history[-1],"content"): |
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history[-1].content += "Decompose question into sub-questions: \n\n - " + "\n - ".join([q["question"] for q in event["data"]["output"]["remaining_questions"]]) |
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if event["name"] == "categorize_intent" and event["event"] == "on_chain_start": |
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print("X") |
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yield history, docs_html, output_query, output_language, related_contents , graphs_html, |
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except Exception as e: |
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print(event, "has failed") |
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raise gr.Error(f"{e}") |
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try: |
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if os.getenv("GRADIO_ENV") != "local": |
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timestamp = str(datetime.now().timestamp()) |
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file = timestamp + ".json" |
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prompt = history[1]["content"] |
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logs = { |
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"user_id": str(user_id), |
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"prompt": prompt, |
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"query": prompt, |
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"question":output_query, |
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"sources":sources, |
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"docs":serialize_docs(docs), |
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"answer": history[-1].content, |
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"time": timestamp, |
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} |
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log_on_azure(file, logs, share_client) |
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except Exception as e: |
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print(f"Error logging on Azure Blob Storage: {e}") |
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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 :)") |
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yield history, docs_html, output_query, output_language, related_contents, graphs_html |
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def save_feedback(feed: str, user_id): |
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if len(feed) > 1: |
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timestamp = str(datetime.now().timestamp()) |
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file = user_id + timestamp + ".json" |
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logs = { |
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"user_id": user_id, |
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"feedback": feed, |
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"time": timestamp, |
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} |
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log_on_azure(file, logs, share_client) |
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return "Feedback submitted, thank you!" |
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def log_on_azure(file, logs, share_client): |
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logs = json.dumps(logs) |
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file_client = share_client.get_file_client(file) |
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file_client.upload_file(logs) |
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init_prompt = """ |
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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**. |
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❓ How to use |
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- **Language**: You can ask me your questions in any language. |
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- **Audience**: You can specify your audience (children, general public, experts) to get a more adapted answer. |
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- **Sources**: You can choose to search in the IPCC or IPBES reports, or both. |
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- **Relevant content sources**: You can choose to search for figures, papers, or graphs that can be relevant for your question. |
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⚠️ Limitations |
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*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.* |
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🛈 Information |
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Please note that we log your questions for meta-analysis purposes, so avoid sharing any sensitive or personal information. |
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What do you want to learn ? |
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""" |
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def vote(data: gr.LikeData): |
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if data.liked: |
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print(data.value) |
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else: |
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print(data) |
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def save_graph(saved_graphs_state, embedding, category): |
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print(f"\nCategory:\n{saved_graphs_state}\n") |
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if category not in saved_graphs_state: |
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saved_graphs_state[category] = [] |
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if embedding not in saved_graphs_state[category]: |
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saved_graphs_state[category].append(embedding) |
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return saved_graphs_state, gr.Button("Graph Saved") |
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with gr.Blocks(title="Climate Q&A", css_paths=os.getcwd()+ "/style.css", theme=theme,elem_id = "main-component") as demo: |
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chat_completed_state = gr.State(0) |
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current_graphs = gr.State([]) |
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saved_graphs = gr.State({}) |
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config_open = gr.State(False) |
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with gr.Tab("ClimateQ&A"): |
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with gr.Row(elem_id="chatbot-row"): |
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with gr.Column(scale=2): |
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chatbot = gr.Chatbot( |
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value = [ChatMessage(role="assistant", content=init_prompt)], |
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type = "messages", |
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show_copy_button=True, |
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show_label = False, |
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elem_id="chatbot", |
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layout = "panel", |
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avatar_images = (None,"https://i.ibb.co/YNyd5W2/logo4.png"), |
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max_height="80vh", |
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height="100vh" |
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) |
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with gr.Row(elem_id = "input-message"): |
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textbox=gr.Textbox(placeholder="Ask me anything here!",show_label=False,scale=7,lines = 1,interactive = True,elem_id="input-textbox") |
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config_button = gr.Button("",elem_id="config-button") |
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with gr.Column(scale=2, variant="panel",elem_id = "right-panel"): |
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with gr.Tabs(elem_id = "right_panel_tab") as tabs: |
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with gr.TabItem("Examples",elem_id = "tab-examples",id = 0): |
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examples_hidden = gr.Textbox(visible = False) |
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first_key = list(QUESTIONS.keys())[0] |
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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") |
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samples = [] |
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for i,key in enumerate(QUESTIONS.keys()): |
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examples_visible = True if i == 0 else False |
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with gr.Row(visible = examples_visible) as group_examples: |
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examples_questions = gr.Examples( |
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QUESTIONS[key], |
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[examples_hidden], |
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examples_per_page=8, |
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run_on_click=False, |
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elem_id=f"examples{i}", |
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api_name=f"examples{i}", |
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) |
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samples.append(group_examples) |
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with gr.Tab("Sources",elem_id = "tab-sources",id = 1) as tab_sources: |
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sources_textbox = gr.HTML(show_label=False, elem_id="sources-textbox") |
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with gr.Tab("Recommended content", elem_id="tab-recommended_content",id=2) as tab_recommended_content: |
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with gr.Tabs(elem_id = "group-subtabs") as tabs_recommended_content: |
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with gr.Tab("Figures",elem_id = "tab-figures",id = 3) as tab_figures: |
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sources_raw = gr.State() |
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with Modal(visible=False, elem_id="modal_figure_galery") as figure_modal: |
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gallery_component = gr.Gallery(object_fit='scale-down',elem_id="gallery-component", height="80vh") |
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show_full_size_figures = gr.Button("Show figures in full size",elem_id="show-figures",interactive=True) |
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show_full_size_figures.click(lambda : Modal(visible=True),None,figure_modal) |
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figures_cards = gr.HTML(show_label=False, elem_id="sources-figures") |
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with gr.Tab("Papers",elem_id = "tab-citations",id = 4) as tab_papers: |
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with gr.Accordion(visible=True, elem_id="papers-summary-popup", label= "See summary of relevant papers", open= False) as summary_popup: |
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papers_summary = gr.Markdown("", visible=True, elem_id="papers-summary") |
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with gr.Accordion(visible=True, elem_id="papers-relevant-popup",label= "See relevant papers", open= False) as relevant_popup: |
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papers_html = gr.HTML(show_label=False, elem_id="papers-textbox") |
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btn_citations_network = gr.Button("Explore papers citations network") |
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with Modal(visible=False) as papers_modal: |
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citations_network = gr.HTML("<h3>Citations Network Graph</h3>", visible=True, elem_id="papers-citations-network") |
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btn_citations_network.click(lambda: Modal(visible=True), None, papers_modal) |
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with gr.Tab("Graphs", elem_id="tab-graphs", id=5) as tab_graphs: |
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graphs_container = gr.HTML("<h2>There are no graphs to be displayed at the moment. Try asking another question.</h2>",elem_id="graphs-container") |
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current_graphs.change(lambda x : x, inputs=[current_graphs], outputs=[graphs_container]) |
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with Modal(visible=False,elem_id="modal-config") as config_modal: |
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gr.Markdown("Reminders: You can talk in any language, ClimateQ&A is multi-lingual!") |
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dropdown_sources = gr.CheckboxGroup( |
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["IPCC", "IPBES","IPOS"], |
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label="Select source (by default search in all sources)", |
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value=["IPCC"], |
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interactive=True, |
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) |
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dropdown_reports = gr.Dropdown( |
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POSSIBLE_REPORTS, |
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label="Or select specific reports", |
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multiselect=True, |
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value=None, |
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interactive=True, |
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) |
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dropdown_external_sources = gr.CheckboxGroup( |
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["IPCC figures","OpenAlex", "OurWorldInData"], |
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label="Select database to search for relevant content", |
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value=["IPCC figures"], |
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interactive=True, |
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) |
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search_only = gr.Checkbox(label="Search only for recommended content without chating", value=False, interactive=True, elem_id="checkbox-chat") |
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dropdown_audience = gr.Dropdown( |
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["Children","General public","Experts"], |
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label="Select audience", |
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value="Experts", |
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interactive=True, |
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) |
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after = gr.Slider(minimum=1950,maximum=2023,step=1,value=1960,label="Publication date",show_label=True,interactive=True,elem_id="date-papers", visible=False) |
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output_query = gr.Textbox(label="Query used for retrieval",show_label = True,elem_id = "reformulated-query",lines = 2,interactive = False, visible= False) |
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output_language = gr.Textbox(label="Language",show_label = True,elem_id = "language",lines = 1,interactive = False, visible= False) |
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dropdown_external_sources.change(lambda x: gr.update(visible = True ) if "OpenAlex" in x else gr.update(visible=False) , inputs=[dropdown_external_sources], outputs=[after]) |
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close_config_modal = gr.Button("Validate and Close",elem_id="close-config-modal") |
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close_config_modal.click(fn=update_config_modal_visibility, inputs=[config_open], outputs=[config_modal, config_open]) |
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config_button.click(fn=update_config_modal_visibility, inputs=[config_open], outputs=[config_modal, config_open]) |
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with gr.Tab("About",elem_classes = "max-height other-tabs"): |
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with gr.Row(): |
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with gr.Column(scale=1): |
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gr.Markdown( |
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""" |
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### More info |
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- See more info at [https://climateqa.com](https://climateqa.com/docs/intro/) |
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- Feedbacks on this [form](https://forms.office.com/e/1Yzgxm6jbp) |
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### Citation |
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""" |
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) |
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with gr.Accordion(CITATION_LABEL,elem_id="citation", open = False,): |
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|
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gr.Textbox( |
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value=CITATION_TEXT, |
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label="", |
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interactive=False, |
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show_copy_button=True, |
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lines=len(CITATION_TEXT.split('\n')), |
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) |
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def start_chat(query,history,search_only): |
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history = history + [ChatMessage(role="user", content=query)] |
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if not search_only: |
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return (gr.update(interactive = False),gr.update(selected=1),history) |
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else: |
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return (gr.update(interactive = False),gr.update(selected=2),history) |
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def finish_chat(): |
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return gr.update(interactive = True,value = "") |
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summary_visible = False |
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relevant_visible = False |
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def toggle_summary_visibility(): |
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global summary_visible |
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summary_visible = not summary_visible |
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return gr.update(visible=summary_visible) |
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def toggle_relevant_visibility(): |
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global relevant_visible |
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relevant_visible = not relevant_visible |
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return gr.update(visible=relevant_visible) |
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def change_completion_status(current_state): |
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current_state = 1 - current_state |
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return current_state |
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def update_sources_number_display(sources_textbox, figures_cards, current_graphs, papers_html): |
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sources_number = sources_textbox.count("<h2>") |
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figures_number = figures_cards.count("<h2>") |
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graphs_number = current_graphs.count("<iframe") |
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papers_number = papers_html.count("<h2>") |
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sources_notif_label = f"Sources ({sources_number})" |
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figures_notif_label = f"Figures ({figures_number})" |
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graphs_notif_label = f"Graphs ({graphs_number})" |
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papers_notif_label = f"Papers ({papers_number})" |
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recommended_content_notif_label = f"Recommended content ({figures_number + graphs_number + papers_number})" |
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return gr.update(label = recommended_content_notif_label), gr.update(label = sources_notif_label), gr.update(label = figures_notif_label), gr.update(label = graphs_notif_label), gr.update(label = papers_notif_label) |
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|
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(textbox |
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.submit(start_chat, [textbox,chatbot, search_only], [textbox,tabs,chatbot],queue = False,api_name = "start_chat_textbox") |
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.then(chat, [textbox,chatbot,dropdown_audience, dropdown_sources,dropdown_reports, dropdown_external_sources, search_only] ,[chatbot,sources_textbox,output_query,output_language, sources_raw, current_graphs],concurrency_limit = 8,api_name = "chat_textbox") |
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.then(finish_chat, None, [textbox],api_name = "finish_chat_textbox") |
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) |
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|
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(examples_hidden |
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.change(start_chat, [examples_hidden,chatbot, search_only], [textbox,tabs,chatbot],queue = False,api_name = "start_chat_examples") |
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.then(chat, [examples_hidden,chatbot,dropdown_audience, dropdown_sources,dropdown_reports, dropdown_external_sources, search_only] ,[chatbot,sources_textbox,output_query,output_language, sources_raw, current_graphs],concurrency_limit = 8,api_name = "chat_textbox") |
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.then(finish_chat, None, [textbox],api_name = "finish_chat_examples") |
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) |
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|
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def change_sample_questions(key): |
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index = list(QUESTIONS.keys()).index(key) |
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visible_bools = [False] * len(samples) |
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visible_bools[index] = True |
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return [gr.update(visible=visible_bools[i]) for i in range(len(samples))] |
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sources_raw.change(process_figures, inputs=[sources_raw], outputs=[figures_cards, gallery_component]) |
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sources_textbox.change(update_sources_number_display, [sources_textbox, figures_cards, current_graphs,papers_html],[tab_recommended_content, tab_sources, tab_figures, tab_graphs, tab_papers]) |
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figures_cards.change(update_sources_number_display, [sources_textbox, figures_cards, current_graphs,papers_html],[tab_recommended_content, tab_sources, tab_figures, tab_graphs, tab_papers]) |
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current_graphs.change(update_sources_number_display, [sources_textbox, figures_cards, current_graphs,papers_html],[tab_recommended_content, tab_sources, tab_figures, tab_graphs, tab_papers]) |
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papers_html.change(update_sources_number_display, [sources_textbox, figures_cards, current_graphs,papers_html],[tab_recommended_content, tab_sources, tab_figures, tab_graphs, tab_papers]) |
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dropdown_samples.change(change_sample_questions,dropdown_samples,samples) |
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textbox.submit(find_papers,[textbox,after, dropdown_external_sources], [papers_html,citations_network,papers_summary]) |
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examples_hidden.change(find_papers,[examples_hidden,after,dropdown_external_sources], [papers_html,citations_network,papers_summary]) |
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demo.queue() |
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demo.launch(ssr_mode=False) |
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|
