refacto into different tabs

app.py

@@ -1,18 +1,7 @@
 # Import necessary libraries
 import os
-import json
-import time
-import re
-import base64
-from datetime import datetime
-from io import BytesIO
-
-import numpy as np
-import pandas as pd
 import gradio as gr
-from gradio import ChatMessage
-from gradio_modal import Modal
-from sentence_transformers import CrossEncoder
+
 from azure.storage.fileshare import ShareServiceClient
 
 # Import custom modules
@@ -20,21 +9,15 @@ from climateqa.engine.embeddings import get_embeddings_function
 from climateqa.engine.llm import get_llm
 from climateqa.engine.vectorstore import get_pinecone_vectorstore
 from climateqa.engine.reranker import get_reranker
-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.graph import make_graph_agent
 from climateqa.engine.chains.retrieve_papers import find_papers
-from front.utils import serialize_docs, process_figures
-from climateqa.event_handler import (
-    init_audience,
-    handle_retrieved_documents,
-    stream_answer,
-    handle_retrieved_owid_graphs,
-    convert_to_docs_to_html
-)
+from climateqa.chat import start_chat, chat_stream, finish_chat
+
+from front.tabs import (create_config_modal, create_examples_tab, create_papers_tab, create_figures_tab, create_chat_interface, create_about_tab)
+from front.utils import process_figures
+
+
 from utils import create_user_id
-from front.utils import make_html_source
 import logging
 
 logging.basicConfig(level=logging.WARNING)
@@ -42,6 +25,7 @@ os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'  # Suppresses INFO and WARNING logs
 logging.getLogger().setLevel(logging.WARNING)
 
 
+
 # Load environment variables in local mode
 try:
     from dotenv import load_dotenv
@@ -57,25 +41,6 @@ theme = gr.themes.Base(
     font=[gr.themes.GoogleFont("Poppins"), "ui-sans-serif", "system-ui", "sans-serif"],
 )
 
-# Initialize prompt and system template
-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.
-- **Relevant content sources**: You can choose to search for figures, papers, or graphs that can be relevant for your question.
-
-⚠️ Limitations
-*Please note that the AI is not perfect and may sometimes give irrelevant answers. If you are not satisfied with the answer, please ask a more specific question or report your feedback to help us improve the system.*
-
-🛈 Information
-Please note that we log your questions for meta-analysis purposes, so avoid sharing any sensitive or personal information.
-
-What do you want to learn ?
-"""
-
 # Azure Blob Storage credentials
 account_key = os.environ["BLOB_ACCOUNT_KEY"]
 if len(account_key) == 86:
@@ -93,20 +58,7 @@ share_client = service.get_share_client(file_share_name)
 
 user_id = create_user_id()
 
-# Citation information
-CITATION_LABEL = "BibTeX citation for ClimateQ&A"
-CITATION_TEXT = r"""@misc{climateqa,
-    author={Théo Alves Da Costa, Timothée Bohe},
-    title={ClimateQ&A, AI-powered conversational assistant for climate change and biodiversity loss},
-    year={2024},
-    howpublished= {\url{https://climateqa.com}},
-}
-@software{climateqa,
-    author = {Théo Alves Da Costa, Timothée Bohe},
-    publisher = {ClimateQ&A},
-    title = {ClimateQ&A, AI-powered conversational assistant for climate change and biodiversity loss},
-}
-"""
+
 
 # Create vectorstore and retriever
 embeddings_function = get_embeddings_function()
@@ -119,224 +71,21 @@ reranker = get_reranker("nano")
 
 agent = make_graph_agent(llm=llm, vectorstore_ipcc=vectorstore, vectorstore_graphs=vectorstore_graphs, vectorstore_region = vectorstore_region, reranker=reranker, threshold_docs=0)#TODO put back default 0.2
 
-# Function to update modal visibility
-def update_config_modal_visibility(config_open):
-    new_config_visibility_status = not config_open
-    return gr.update(visible=new_config_visibility_status), new_config_visibility_status
-
-# Main chat function
-async def chat(
-    query: str, 
-    history: list[ChatMessage], 
-    audience: str, 
-    sources: list[str], 
-    reports: list[str], 
-    relevant_content_sources_selection: list[str], 
-    search_only: bool
-) -> tuple[list, str, str, str, list, str]:
-    """Process a chat query and return response with relevant sources and visualizations.
-    
-    Args:
-        query (str): The user's question
-        history (list): Chat message history
-        audience (str): Target audience type
-        sources (list): Knowledge base sources to search
-        reports (list): Specific reports to search within sources
-        relevant_content_sources_selection (list): Types of content to retrieve (figures, papers, etc)
-        search_only (bool): Whether to only search without generating answer
-        
-    Yields:
-        tuple: Contains:
-            - history: Updated chat history
-            - docs_html: HTML of retrieved documents
-            - output_query: Processed query
-            - output_language: Detected language
-            - related_contents: Related content
-            - graphs_html: HTML of relevant graphs
-    """
-    # Log incoming question
-    date_now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-    print(f">> NEW QUESTION ({date_now}) : {query}")
-
-    audience_prompt = init_audience(audience)
-    sources = sources or ["IPCC", "IPBES"]
-    reports = reports or []
-
-    # Prepare inputs for agent
-    inputs = {
-        "user_input": query,
-        "audience": audience_prompt,
-        "sources_input": sources,
-        "relevant_content_sources_selection": relevant_content_sources_selection,
-        "search_only": search_only,
-        "reports": reports
-    }
-
-    # Get streaming events from agent
-    result = agent.astream_events(inputs, version="v1")
-
-    # Initialize state variables
-    docs = []
-    related_contents = []
-    docs_html = ""
-    new_docs_html = ""
-    output_query = ""
-    output_language = ""
-    output_keywords = ""
-    start_streaming = False
-    graphs_html = ""    
-    used_documents = []
-    answer_message_content = ""
-
-    # Define processing steps
-    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),
-        "retrieve_local_data": ("🔄️ Searching in the knowledge base", False),
-    }
-
-    try:
-        # Process streaming events
-        async for event in result:
-
-            if "langgraph_node" in event["metadata"]:
-                node = event["metadata"]["langgraph_node"]
-
-                # Handle document retrieval
-                if event["event"] == "on_chain_end" and event["name"] in ["retrieve_documents","retrieve_local_data"] and event["data"]["output"] != None:
-                    history, used_documents = handle_retrieved_documents(
-                        event, history, used_documents
-                    )
-                if event["event"] == "on_chain_end" and event["name"] == "answer_search" :
-                    docs = event["data"]["input"]["documents"]
-                    docs_html = convert_to_docs_to_html(docs)                    
-                    related_contents = event["data"]["input"]["related_contents"]
-        
-                # Handle intent categorization
-                elif (event["event"] == "on_chain_end" and 
-                      node == "categorize_intent" and 
-                      event["name"] == "_write"):
-                    intent = event["data"]["output"]["intent"]
-                    output_language = event["data"]["output"].get("language", "English")
-                    history[-1].content = f"Language identified: {output_language}\nIntent identified: {intent}"
-
-                # Handle processing steps display
-                elif event["name"] in steps_display and event["event"] == "on_chain_start":
-                    event_description, display_output = steps_display[node]
-                    if (not hasattr(history[-1], 'metadata') or 
-                        history[-1].metadata["title"] != event_description):
-                        history.append(ChatMessage(
-                            role="assistant",
-                            content="",
-                            metadata={'title': event_description}
-                        ))
-
-                # Handle answer streaming
-                elif (event["name"] != "transform_query" and 
-                      event["event"] == "on_chat_model_stream" and
-                      node in ["answer_rag","answer_rag_no_docs", "answer_search", "answer_chitchat"]):
-                    history, start_streaming, answer_message_content = stream_answer(
-                        history, event, start_streaming, answer_message_content
-                    )
-
-                # Handle graph retrieval
-                elif event["name"] in ["retrieve_graphs", "retrieve_graphs_ai"] and event["event"] == "on_chain_end":
-                    graphs_html = handle_retrieved_owid_graphs(event, graphs_html)
-
-                # Handle query transformation
-                if event["name"] == "transform_query" and event["event"] == "on_chain_end":
-                    if hasattr(history[-1], "content"):
-                        sub_questions = [q["question"] for q in event["data"]["output"]["questions_list"]]
-                        history[-1].content += "Decompose question into sub-questions:\n\n - " + "\n - ".join(sub_questions)
-
-            yield history, docs_html, output_query, output_language, related_contents, graphs_html 
-
-    except Exception as e:
-        print(f"Event {event} has failed")
-        raise gr.Error(str(e))
-
-    try:
-        # Log interaction to Azure if not in local environment
-        if os.getenv("GRADIO_ENV") != "local":
-            timestamp = str(datetime.now().timestamp())
-            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(f"{timestamp}.json", logs, share_client)
-    except Exception as e:
-        print(f"Error logging on Azure Blob Storage: {e}")
-        error_msg = f"ClimateQ&A Error: {str(e)[:100]} - The error has been noted, try another question and if the error remains, you can contact us :)"
-        raise gr.Error(error_msg)
-
-    yield history, docs_html, output_query, output_language, related_contents, graphs_html 
-
-# Function to save feedback
-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!"
-
-# Function to log data on Azure
-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)
-
-
 
+async def chat(query, history, audience, sources, reports, relevant_content_sources_selection, search_only):
+    async for event in chat_stream(agent, query, history, audience, sources, reports, relevant_content_sources_selection, search_only, share_client, user_id):
+        yield event
 
 
 # --------------------------------------------------------------------
 # Gradio
 # --------------------------------------------------------------------
 
-
-
-def vote(data: gr.LikeData):
-    if data.liked:
-        print(data.value)
-    else:
-        print(data)
-
-def save_graph(saved_graphs_state, embedding, category):
-    print(f"\nCategory:\n{saved_graphs_state}\n")
-    if category not in saved_graphs_state:
-        saved_graphs_state[category] = []
-    if embedding not in saved_graphs_state[category]:
-        saved_graphs_state[category].append(embedding)
-    return saved_graphs_state, gr.Button("Graph Saved")
-
-
-# Functions to toggle visibility
-def toggle_summary_visibility():
-    global summary_visible
-    summary_visible = not summary_visible
-    return gr.update(visible=summary_visible)
-
-def toggle_relevant_visibility():
-    global relevant_visible
-    relevant_visible = not relevant_visible
-    return gr.update(visible=relevant_visible)
-
-def change_completion_status(current_state):
-    current_state = 1 - current_state
-    return current_state
+# Function to update modal visibility
+def update_config_modal_visibility(config_open):
+    new_config_visibility_status = not config_open
+    return gr.update(visible=new_config_visibility_status), new_config_visibility_status
+    
 
 def update_sources_number_display(sources_textbox, figures_cards, current_graphs, papers_html):
     sources_number = sources_textbox.count("<h2>")
@@ -351,240 +100,8 @@ def update_sources_number_display(sources_textbox, figures_cards, current_graphs
 
     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)
 
-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))]
-
-
-
-# Chat functions
-def start_chat(query, history, search_only):
-    history = history + [ChatMessage(role="user", content=query)]
-    if not search_only:
-        return (gr.update(interactive=False), gr.update(selected=1), history, [])
-    else:
-        return (gr.update(interactive=False), gr.update(selected=2), history, [])
-
-def finish_chat():
-    return gr.update(interactive=True, value="")
-
-# Initialize visibility states
-summary_visible = False
-relevant_visible = False
-
-# UI Layout Components
-def create_chat_interface():
-    chatbot = gr.Chatbot(
-        value=[ChatMessage(role="assistant", content=init_prompt)],
-        type="messages", 
-        show_copy_button=True,
-        show_label=False,
-        elem_id="chatbot",
-        layout="panel",
-        avatar_images=(None, "https://i.ibb.co/YNyd5W2/logo4.png"),
-        max_height="80vh",
-        height="100vh"
-    )
-    
-    with gr.Row(elem_id="input-message"):
-        
-        textbox = gr.Textbox(
-            placeholder="Ask me anything here!",
-            show_label=False,
-            scale=12,
-            lines=1,
-            interactive=True,
-            elem_id="input-textbox"
-        )
-        
-        config_button = gr.Button("", elem_id="config-button")
-    
-    return chatbot, textbox, config_button
-
-def create_examples_tab():
-    examples_hidden = gr.Textbox(visible=False)
-    first_key = list(QUESTIONS.keys())[0]
-    dropdown_samples = gr.Dropdown(
-        choices=QUESTIONS.keys(),
-        value=first_key,
-        interactive=True,
-        label="Select a category of sample questions",
-        elem_id="dropdown-samples"
-    )
-
-    samples = []
-    for i, key in enumerate(QUESTIONS.keys()):
-        examples_visible = (i == 0)
-        with gr.Row(visible=examples_visible) as group_examples:
-            examples_questions = gr.Examples(
-                examples=QUESTIONS[key],
-                inputs=[examples_hidden],
-                examples_per_page=8,
-                run_on_click=False,
-                elem_id=f"examples{i}",
-                api_name=f"examples{i}"
-            )
-        samples.append(group_examples)
-        
-    return examples_hidden, dropdown_samples, samples
-
-def create_figures_tab():
-    sources_raw = gr.State()
-    new_figures = gr.State([])
-    used_figures = gr.State([])
-    
-    with Modal(visible=False, elem_id="modal_figure_galery") as figure_modal:
-        gallery_component = gr.Gallery(
-            object_fit='scale-down',
-            elem_id="gallery-component",
-            height="80vh"
-        )
-        
-    show_full_size_figures = gr.Button(
-        "Show figures in full size",
-        elem_id="show-figures",
-        interactive=True
-    )
-    show_full_size_figures.click(
-        lambda: Modal(visible=True),
-        None,
-        figure_modal
-    )
-
-    figures_cards = gr.HTML(show_label=False, elem_id="sources-figures")
-    
-    return sources_raw, new_figures, used_figures, gallery_component, figures_cards, figure_modal
-
-def create_papers_tab():
-    with gr.Accordion(
-        visible=True,
-        elem_id="papers-summary-popup",
-        label="See summary of relevant papers",
-        open=False
-    ) as summary_popup:
-        papers_summary = gr.Markdown("", visible=True, elem_id="papers-summary")
-
-    with gr.Accordion(
-        visible=True,
-        elem_id="papers-relevant-popup",
-        label="See relevant papers",
-        open=False
-    ) as relevant_popup:
-        papers_html = gr.HTML(show_label=False, elem_id="papers-textbox")
-
-    btn_citations_network = gr.Button("Explore papers citations network")
-    with Modal(visible=False) as papers_modal:
-        citations_network = gr.HTML(
-            "<h3>Citations Network Graph</h3>",
-            visible=True,
-            elem_id="papers-citations-network"
-        )
-    btn_citations_network.click(
-        lambda: Modal(visible=True),
-        None,
-        papers_modal
-    )
-    
-    return papers_summary, papers_html, citations_network, papers_modal
-
-def create_config_modal(config_open):
-    with Modal(visible=False, elem_id="modal-config") as config_modal:
-        gr.Markdown("Reminders: You can talk in any language, ClimateQ&A is multi-lingual!")
-
-        dropdown_sources = gr.CheckboxGroup(
-            choices=["IPCC", "IPBES", "IPOS"],
-            label="Select source (by default search in all sources)",
-            value=["IPCC"],
-            interactive=True
-        )
-
-        dropdown_reports = gr.Dropdown(
-            choices=POSSIBLE_REPORTS,
-            label="Or select specific reports",
-            multiselect=True,
-            value=None,
-            interactive=True
-        )
-
-        dropdown_external_sources = gr.CheckboxGroup(
-            choices=["Figures (IPCC/IPBES)", "Papers (OpenAlex)", "Graphs (OurWorldInData)","POC region"],
-            label="Select database to search for relevant content",
-            value=["Figures (IPCC/IPBES)","POC region"],
-            interactive=True
-        )
-
-        search_only = gr.Checkbox(
-            label="Search only for recommended content without chating",
-            value=False,
-            interactive=True,
-            elem_id="checkbox-chat"
-        )
-
-        dropdown_audience = gr.Dropdown(
-            choices=["Children", "General public", "Experts"],
-            label="Select audience", 
-            value="Experts",
-            interactive=True
-        )
-
-        after = gr.Slider(
-            minimum=1950,
-            maximum=2023,
-            step=1,
-            value=1960,
-            label="Publication date",
-            show_label=True,
-            interactive=True,
-            elem_id="date-papers",
-            visible=False
-        )
-
-        output_query = gr.Textbox(
-            label="Query used for retrieval",
-            show_label=True,
-            elem_id="reformulated-query",
-            lines=2,
-            interactive=False,
-            visible=False
-        )
-
-        output_language = gr.Textbox(
-            label="Language",
-            show_label=True,
-            elem_id="language",
-            lines=1,
-            interactive=False,
-            visible=False
-        )
-
-        dropdown_external_sources.change(
-            lambda x: gr.update(visible="Papers (OpenAlex)" in x),
-            inputs=[dropdown_external_sources],
-            outputs=[after]
-        )
-
-        close_config_modal = gr.Button("Validate and Close", elem_id="close-config-modal")
-        close_config_modal.click(
-            fn=update_config_modal_visibility,
-            inputs=[config_open],
-            outputs=[config_modal, config_open]
-        )
-        
-        return {
-            "config_open" : config_open,
-            "config_modal": config_modal,
-            "dropdown_sources": dropdown_sources,
-            "dropdown_reports": dropdown_reports,
-            "dropdown_external_sources": dropdown_external_sources,
-            "search_only": search_only,
-            "dropdown_audience": dropdown_audience,
-            "after": after,
-            "output_query": output_query,
-            "output_language": output_language,
-        }
 
+# # UI Layout Components
 def cqa_tab(tab_name):
     # State variables
     current_graphs = gr.State([])
@@ -599,7 +116,7 @@ def cqa_tab(tab_name):
                 with gr.Tabs(elem_id="right_panel_tab") as tabs:
                     # Examples tab
                     with gr.TabItem("Examples", elem_id="tab-examples", id=0):
-                        examples_hidden, dropdown_samples, samples = create_examples_tab()
+                        examples_hidden = create_examples_tab()
 
                     # Sources tab
                     with gr.Tab("Sources", elem_id="tab-sources", id=1) as tab_sources:
@@ -631,8 +148,6 @@ def cqa_tab(tab_name):
         "new_figures": new_figures,
         "current_graphs": current_graphs,
         "examples_hidden": examples_hidden,
-        "dropdown_samples": dropdown_samples,
-        "samples": samples,
         "sources_textbox": sources_textbox,
         "figures_cards": figures_cards,
         "gallery_component": gallery_component,
@@ -648,27 +163,7 @@ def cqa_tab(tab_name):
         "graph_container": graphs_container
     }
                                 
-def about_tab():
-    with gr.Tab("About", elem_classes="max-height other-tabs"):
-        with gr.Row():
-            with gr.Column(scale=1):
-                gr.Markdown(
-                    """
-                    ### More info
-                    - See more info at [https://climateqa.com](https://climateqa.com/docs/intro/)
-                    - Feedbacks on this [form](https://forms.office.com/e/1Yzgxm6jbp)
-                                                
-                    ### Citation
-                    """
-                )
-                with gr.Accordion(CITATION_LABEL, elem_id="citation", open=False):
-                    gr.Textbox(
-                        value=CITATION_TEXT,
-                        label="",
-                        interactive=False,
-                        show_copy_button=True,
-                        lines=len(CITATION_TEXT.split('\n')),
-                    )
+ 
     
 def event_handling(
     main_tab_components,
@@ -682,8 +177,6 @@ def event_handling(
     new_figures = main_tab_components["new_figures"]
     current_graphs = main_tab_components["current_graphs"]
     examples_hidden = main_tab_components["examples_hidden"]
-    dropdown_samples = main_tab_components["dropdown_samples"]
-    samples = main_tab_components["samples"]
     sources_textbox = main_tab_components["sources_textbox"]
     figures_cards = main_tab_components["figures_cards"]
     gallery_component = main_tab_components["gallery_component"]
@@ -708,183 +201,62 @@ def event_handling(
     after = config_components["after"]
     output_query = config_components["output_query"]
     output_language = config_components["output_language"]
+    close_config_modal = config_components["close_config_modal_button"]
     
     new_sources_hmtl = gr.State([])
     
 
      
-    config_button.click(
-        fn=update_config_modal_visibility,
-        inputs=[config_open],
-        outputs=[config_modal, config_open]
-    )
-    
-    
+    for button in [config_button, close_config_modal]:
+        button.click(
+            fn=update_config_modal_visibility,
+            inputs=[config_open],
+            outputs=[config_modal, config_open]
+        )
+    # Event for textbox
     (textbox
-        .submit(start_chat, [textbox, chatbot, search_only], [textbox, tabs, chatbot, sources_raw], queue=False, api_name=f"start_chat_textbox_{tab_name}")
-        .then(chat, [textbox, chatbot, dropdown_audience, dropdown_sources, dropdown_reports, dropdown_external_sources, search_only], [chatbot, new_sources_hmtl, output_query, output_language, new_figures, current_graphs], concurrency_limit=8, api_name="chat_textbox")
-        .then(finish_chat, None, [textbox], api_name=f"finish_chat_textbox_{tab_name}")
+        .submit(start_chat, [textbox, chatbot, search_only], [textbox, tabs, chatbot, sources_raw], queue=False, api_name=f"start_chat_{textbox.elem_id}")
+        .then(chat, [textbox, chatbot, dropdown_audience, dropdown_sources, dropdown_reports, dropdown_external_sources, search_only], [chatbot, new_sources_hmtl, output_query, output_language, new_figures, current_graphs], concurrency_limit=8, api_name=f"chat_{textbox.elem_id}")
+        .then(finish_chat, None, [textbox], api_name=f"finish_chat_{textbox.elem_id}")
     )
-
-
-
+    # Event for examples_hidden
     (examples_hidden
-        .change(start_chat, [examples_hidden, chatbot, search_only], [textbox, tabs, chatbot, sources_raw], queue=False, api_name=f"start_chat_examples_{tab_name}")
-        .then(chat, [examples_hidden, chatbot, dropdown_audience, dropdown_sources, dropdown_reports, dropdown_external_sources, search_only], [chatbot, new_sources_hmtl, output_query, output_language, new_figures, current_graphs], concurrency_limit=8, api_name="chat_textbox")
-        .then(finish_chat, None, [textbox], api_name=f"finish_chat_examples_{tab_name}")
+        .change(start_chat, [examples_hidden, chatbot, search_only], [examples_hidden, tabs, chatbot, sources_raw], queue=False, api_name=f"start_chat_{examples_hidden.elem_id}")
+        .then(chat, [examples_hidden, chatbot, dropdown_audience, dropdown_sources, dropdown_reports, dropdown_external_sources, search_only], [chatbot, new_sources_hmtl, output_query, output_language, new_figures, current_graphs], concurrency_limit=8, api_name=f"chat_{examples_hidden.elem_id}")
+        .then(finish_chat, None, [examples_hidden], api_name=f"finish_chat_{examples_hidden.elem_id}")
     )
-
+    
     new_sources_hmtl.change(lambda x : x, inputs = [new_sources_hmtl], outputs = [sources_textbox])
     current_graphs.change(lambda x: x, inputs=[current_graphs], outputs=[graphs_container])
     new_figures.change(process_figures, inputs=[sources_raw, new_figures], outputs=[sources_raw, figures_cards, gallery_component])
 
     # Update sources numbers
-    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])
-    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])
-    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])
-    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])
-
-    # Other questions examples
-    dropdown_samples.change(change_sample_questions, dropdown_samples, samples)
-
-    # Search for papers
-    textbox.submit(find_papers, [textbox, after, dropdown_external_sources], [papers_html, citations_network, papers_summary])
-    examples_hidden.change(find_papers, [examples_hidden, after, dropdown_external_sources], [papers_html, citations_network, papers_summary])
+    for component in [sources_textbox, figures_cards, current_graphs, papers_html]:
+        component.change(update_sources_number_display, [sources_textbox, figures_cards, current_graphs, papers_html], [tab_recommended_content, tab_sources, tab_figures, tab_graphs, tab_papers])
     
+    
+    # Search for papers
+    for component in [textbox, examples_hidden]:
+        component.submit(find_papers, [component, after, dropdown_external_sources], [papers_html, citations_network, papers_summary])
 
 def main_ui():
-    config_open = gr.State(False)
+    # config_open = gr.State(True)
     with gr.Blocks(title="Climate Q&A", css_paths=os.getcwd()+ "/style.css", theme=theme, elem_id="main-component") as demo: 
-        config_components = create_config_modal(config_open)       
+        config_components = create_config_modal()  
+             
         with gr.Tabs():
             cqa_components = cqa_tab(tab_name = "ClimateQ&A")
-            local_cqa_components = cqa_tab(tab_name = "Beta - POC Adapt'Action")
+            # local_cqa_components = cqa_tab(tab_name = "Beta - POC Adapt'Action")
         
-            about_tab()
+            create_about_tab()
         
         event_handling(cqa_components, config_components, tab_name = 'ClimateQ&A')
-        event_handling(local_cqa_components, config_components, tab_name = 'Beta - POC Adapt\'Action')
+        # event_handling(local_cqa_components, config_components, tab_name = 'Beta - POC Adapt\'Action')
+        
         demo.queue()
         
     return demo
 
-
-    
-
-    
-
-# # Main UI Assembly
-# with gr.Blocks(title="Climate Q&A", css_paths=os.getcwd()+ "/style.css", theme=theme, elem_id="main-component") as demo:
-    
-#     # State variables
-#     # chat_completed_state = gr.State(0)
-#     current_graphs = gr.State([])
-#     saved_graphs = gr.State({})
-#     new_sources_hmtl = gr.State([])
-
-#     config_open = gr.State(False)
-
-#     with gr.Tab("ClimateQ&A"):
-#         with gr.Row(elem_id="chatbot-row"):
-#             # Left column - Chat interface
-#             with gr.Column(scale=2):
-#                 chatbot, textbox, config_button = create_chat_interface()
-
-#             # Right column - Content panels
-#             with gr.Column(scale=2, variant="panel", elem_id="right-panel"):
-#                 with gr.Tabs(elem_id="right_panel_tab") as tabs:
-#                     # Examples tab
-#                     with gr.TabItem("Examples", elem_id="tab-examples", id=0):
-#                         examples_hidden, dropdown_samples, samples = create_examples_tab()
-
-#                     # Sources tab
-#                     with gr.Tab("Sources", elem_id="tab-sources", id=1) as tab_sources:
-#                         sources_textbox = gr.HTML(show_label=False, elem_id="sources-textbox")
-
-
-#                     # Recommended content tab
-#                     with gr.Tab("Recommended content", elem_id="tab-recommended_content", id=2) as tab_recommended_content:
-#                         with gr.Tabs(elem_id="group-subtabs") as tabs_recommended_content:
-#                             # Figures subtab
-#                             with gr.Tab("Figures", elem_id="tab-figures", id=3) as tab_figures:
-#                                 sources_raw, new_figures, used_figures, gallery_component, figures_cards, figure_modal = create_figures_tab()
-
-#                             # Papers subtab
-#                             with gr.Tab("Papers", elem_id="tab-citations", id=4) as tab_papers:
-#                                 papers_summary, papers_html, citations_network, papers_modal = create_papers_tab()
-
-#                             # Graphs subtab
-#                             with gr.Tab("Graphs", elem_id="tab-graphs", id=5) as tab_graphs:
-#                                 graphs_container = gr.HTML(
-#                                     "<h2>There are no graphs to be displayed at the moment. Try asking another question.</h2>",
-#                                     elem_id="graphs-container"
-#                                 )
-
-
-                    
-#     with gr.Tab("About", elem_classes="max-height other-tabs"):
-#         with gr.Row():
-#             with gr.Column(scale=1):
-#                 gr.Markdown(
-#                     """
-#                     ### More info
-#                     - See more info at [https://climateqa.com](https://climateqa.com/docs/intro/)
-#                     - Feedbacks on this [form](https://forms.office.com/e/1Yzgxm6jbp)
-                                                
-#                     ### Citation
-#                     """
-#                 )
-#                 with gr.Accordion(CITATION_LABEL, elem_id="citation", open=False):
-#                     gr.Textbox(
-#                         value=CITATION_TEXT,
-#                         label="",
-#                         interactive=False,
-#                         show_copy_button=True,
-#                         lines=len(CITATION_TEXT.split('\n')),
-#                     )
-#     # Configuration pannel
-#     config_modal, dropdown_sources, dropdown_reports, dropdown_external_sources, search_only, dropdown_audience, after, output_query, output_language = create_config_modal(config_open)
-    
-#     # Event handlers    
-#     config_button.click(
-#         fn=update_config_modal_visibility,
-#         inputs=[config_open],
-#         outputs=[config_modal, config_open]
-#     )
-    
-    
-#     (textbox
-#         .submit(start_chat, [textbox, chatbot, search_only], [textbox, tabs, chatbot, sources_raw], queue=False, api_name="start_chat_textbox")
-#         .then(chat, [textbox, chatbot, dropdown_audience, dropdown_sources, dropdown_reports, dropdown_external_sources, search_only], [chatbot, new_sources_hmtl, output_query, output_language, new_figures, current_graphs], 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, search_only], [textbox, tabs, chatbot, sources_raw], queue=False, api_name="start_chat_examples")
-#         .then(chat, [examples_hidden, chatbot, dropdown_audience, dropdown_sources, dropdown_reports, dropdown_external_sources, search_only], [chatbot, new_sources_hmtl, output_query, output_language, new_figures, current_graphs], concurrency_limit=8, api_name="chat_textbox")
-#         .then(finish_chat, None, [textbox], api_name="finish_chat_examples")
-#     )
-
-#     new_sources_hmtl.change(lambda x : x, inputs = [new_sources_hmtl], outputs = [sources_textbox])
-#     new_figures.change(process_figures, inputs=[sources_raw, new_figures], outputs=[sources_raw, figures_cards, gallery_component])
-#     current_graphs.change(lambda x: x, inputs=[current_graphs], outputs=[graphs_container])
-    
-#     # Update sources numbers
-#     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])
-#     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])
-#     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])
-#     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])
-
-#     # Other questions examples
-#     dropdown_samples.change(change_sample_questions, dropdown_samples, samples)
-
-#     # Search for papers
-#     textbox.submit(find_papers, [textbox, after, dropdown_external_sources], [papers_html, citations_network, papers_summary])
-#     examples_hidden.change(find_papers, [examples_hidden, after, dropdown_external_sources], [papers_html, citations_network, papers_summary])
-
-#     demo.queue()
     
 demo = main_ui()
 demo.launch(ssr_mode=False)

climateqa/chat.py

@@ -0,0 +1,197 @@
+import os
+from datetime import datetime
+import gradio as gr
+# from .agent import agent
+from gradio import ChatMessage
+from langgraph.graph.state import CompiledStateGraph
+import json
+
+from .handle_stream_events import (
+    init_audience,
+    handle_retrieved_documents,
+    convert_to_docs_to_html,
+    stream_answer,
+    handle_retrieved_owid_graphs,
+    serialize_docs,
+)
+
+# Function to log data on Azure
+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)
+
+# Chat functions
+def start_chat(query, history, search_only):
+    history = history + [ChatMessage(role="user", content=query)]
+    if not search_only:
+        return (gr.update(interactive=False), gr.update(selected=1), history, [])
+    else:
+        return (gr.update(interactive=False), gr.update(selected=2), history, [])
+
+def finish_chat():
+    return gr.update(interactive=True, value="")
+
+def log_interaction_to_azure(history, output_query, sources, docs, share_client, user_id):
+    try:
+        # Log interaction to Azure if not in local environment
+        if os.getenv("GRADIO_ENV") != "local":
+            timestamp = str(datetime.now().timestamp())
+            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(f"{timestamp}.json", logs, share_client)
+    except Exception as e:
+        print(f"Error logging on Azure Blob Storage: {e}")
+        error_msg = f"ClimateQ&A Error: {str(e)[:100]} - The error has been noted, try another question and if the error remains, you can contact us :)"
+        raise gr.Error(error_msg)
+    
+# Main chat function
+async def chat_stream(
+    agent : CompiledStateGraph,
+    query: str, 
+    history: list[ChatMessage], 
+    audience: str, 
+    sources: list[str], 
+    reports: list[str], 
+    relevant_content_sources_selection: list[str], 
+    search_only: bool,
+    share_client,
+    user_id: str
+) -> tuple[list, str, str, str, list, str]:
+    """Process a chat query and return response with relevant sources and visualizations.
+    
+    Args:
+        query (str): The user's question
+        history (list): Chat message history
+        audience (str): Target audience type
+        sources (list): Knowledge base sources to search
+        reports (list): Specific reports to search within sources
+        relevant_content_sources_selection (list): Types of content to retrieve (figures, papers, etc)
+        search_only (bool): Whether to only search without generating answer
+        
+    Yields:
+        tuple: Contains:
+            - history: Updated chat history
+            - docs_html: HTML of retrieved documents
+            - output_query: Processed query
+            - output_language: Detected language
+            - related_contents: Related content
+            - graphs_html: HTML of relevant graphs
+    """
+    # Log incoming question
+    date_now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    print(f">> NEW QUESTION ({date_now}) : {query}")
+
+    audience_prompt = init_audience(audience)
+    sources = sources or ["IPCC", "IPBES"]
+    reports = reports or []
+
+    # Prepare inputs for agent
+    inputs = {
+        "user_input": query,
+        "audience": audience_prompt,
+        "sources_input": sources,
+        "relevant_content_sources_selection": relevant_content_sources_selection,
+        "search_only": search_only,
+        "reports": reports
+    }
+
+    # Get streaming events from agent
+    result = agent.astream_events(inputs, version="v1")
+
+    # Initialize state variables
+    docs = []
+    related_contents = []
+    docs_html = ""
+    new_docs_html = ""
+    output_query = ""
+    output_language = ""
+    output_keywords = ""
+    start_streaming = False
+    graphs_html = ""    
+    used_documents = []
+    answer_message_content = ""
+
+    # Define processing steps
+    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),
+        "retrieve_local_data": ("🔄️ Searching in the knowledge base", False),
+    }
+
+    try:
+        # Process streaming events
+        async for event in result:
+
+            if "langgraph_node" in event["metadata"]:
+                node = event["metadata"]["langgraph_node"]
+
+                # Handle document retrieval
+                if event["event"] == "on_chain_end" and event["name"] in ["retrieve_documents","retrieve_local_data"] and event["data"]["output"] != None:
+                    history, used_documents = handle_retrieved_documents(
+                        event, history, used_documents
+                    )
+                if event["event"] == "on_chain_end" and event["name"] == "answer_search" :
+                    docs = event["data"]["input"]["documents"]
+                    docs_html = convert_to_docs_to_html(docs)                    
+                    related_contents = event["data"]["input"]["related_contents"]
+        
+                # Handle intent categorization
+                elif (event["event"] == "on_chain_end" and 
+                      node == "categorize_intent" and 
+                      event["name"] == "_write"):
+                    intent = event["data"]["output"]["intent"]
+                    output_language = event["data"]["output"].get("language", "English")
+                    history[-1].content = f"Language identified: {output_language}\nIntent identified: {intent}"
+
+                # Handle processing steps display
+                elif event["name"] in steps_display and event["event"] == "on_chain_start":
+                    event_description, display_output = steps_display[node]
+                    if (not hasattr(history[-1], 'metadata') or 
+                        history[-1].metadata["title"] != event_description):
+                        history.append(ChatMessage(
+                            role="assistant",
+                            content="",
+                            metadata={'title': event_description}
+                        ))
+
+                # Handle answer streaming
+                elif (event["name"] != "transform_query" and 
+                      event["event"] == "on_chat_model_stream" and
+                      node in ["answer_rag","answer_rag_no_docs", "answer_search", "answer_chitchat"]):
+                    history, start_streaming, answer_message_content = stream_answer(
+                        history, event, start_streaming, answer_message_content
+                    )
+
+                # Handle graph retrieval
+                elif event["name"] in ["retrieve_graphs", "retrieve_graphs_ai"] and event["event"] == "on_chain_end":
+                    graphs_html = handle_retrieved_owid_graphs(event, graphs_html)
+
+                # Handle query transformation
+                if event["name"] == "transform_query" and event["event"] == "on_chain_end":
+                    if hasattr(history[-1], "content"):
+                        sub_questions = [q["question"] for q in event["data"]["output"]["questions_list"]]
+                        history[-1].content += "Decompose question into sub-questions:\n\n - " + "\n - ".join(sub_questions)
+
+            yield history, docs_html, output_query, output_language, related_contents, graphs_html 
+
+    except Exception as e:
+        print(f"Event {event} has failed")
+        raise gr.Error(str(e))
+
+ 
+
+    # Call the function to log interaction
+    log_interaction_to_azure(history, output_query, sources, docs, share_client, user_id)
+
+    yield history, docs_html, output_query, output_language, related_contents, graphs_html 

front/deprecated.py

@@ -0,0 +1,46 @@
+
+# Functions to toggle visibility
+def toggle_summary_visibility():
+    global summary_visible
+    summary_visible = not summary_visible
+    return gr.update(visible=summary_visible)
+
+def toggle_relevant_visibility():
+    global relevant_visible
+    relevant_visible = not relevant_visible
+    return gr.update(visible=relevant_visible)
+
+def change_completion_status(current_state):
+    current_state = 1 - current_state
+    return current_state    
+
+
+
+def vote(data: gr.LikeData):
+    if data.liked:
+        print(data.value)
+    else:
+        print(data)
+
+def save_graph(saved_graphs_state, embedding, category):
+    print(f"\nCategory:\n{saved_graphs_state}\n")
+    if category not in saved_graphs_state:
+        saved_graphs_state[category] = []
+    if embedding not in saved_graphs_state[category]:
+        saved_graphs_state[category].append(embedding)
+    return saved_graphs_state, gr.Button("Graph Saved")
+
+
+# Function to save feedback
+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!"
+

front/tabs/__init__.py

@@ -0,0 +1,6 @@
+from .tab_config import create_config_modal
+from .tab_examples import create_examples_tab
+from .tab_papers import create_papers_tab
+from .tab_figures import create_figures_tab
+from .chat_interface import create_chat_interface
+from .tab_about import create_about_tab

front/tabs/chat_interface.py

@@ -0,0 +1,55 @@
+import gradio as gr
+from gradio.components import ChatMessage
+
+# Initialize prompt and system template
+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.
+- **Relevant content sources**: You can choose to search for figures, papers, or graphs that can be relevant for your question.
+
+⚠️ Limitations
+*Please note that the AI is not perfect and may sometimes give irrelevant answers. If you are not satisfied with the answer, please ask a more specific question or report your feedback to help us improve the system.*
+
+🛈 Information
+Please note that we log your questions for meta-analysis purposes, so avoid sharing any sensitive or personal information.
+
+What do you want to learn ?
+"""
+
+
+
+# UI Layout Components
+def create_chat_interface():
+    chatbot = gr.Chatbot(
+        value=[ChatMessage(role="assistant", content=init_prompt)],
+        type="messages", 
+        show_copy_button=True,
+        show_label=False,
+        elem_id="chatbot",
+        layout="panel",
+        avatar_images=(None, "https://i.ibb.co/YNyd5W2/logo4.png"),
+        max_height="80vh",
+        height="100vh"
+    )
+    
+    with gr.Row(elem_id="input-message"):
+        
+        textbox = gr.Textbox(
+            placeholder="Ask me anything here!",
+            show_label=False,
+            scale=12,
+            lines=1,
+            interactive=True,
+            elem_id="input-textbox"
+        )
+        
+        config_button = gr.Button("", elem_id="config-button")
+    
+    return chatbot, textbox, config_button
+
+
+        

front/tabs/main_tab.py

@@ -0,0 +1,69 @@
+import gradio as gr
+from .chat_interface import create_chat_interface
+from .tab_examples import create_examples_tab
+from .tab_papers import create_papers_tab
+from .tab_figures import create_figures_tab
+from .chat_interface import create_chat_interface
+
+def cqa_tab(tab_name):
+    # State variables
+    current_graphs = gr.State([])
+    with gr.Tab(tab_name):
+        with gr.Row(elem_id="chatbot-row"):
+            # Left column - Chat interface
+            with gr.Column(scale=2):
+                chatbot, textbox, config_button = create_chat_interface()
+
+            # Right column - Content panels
+            with gr.Column(scale=2, variant="panel", elem_id="right-panel"):
+                with gr.Tabs(elem_id="right_panel_tab") as tabs:
+                    # Examples tab
+                    with gr.TabItem("Examples", elem_id="tab-examples", id=0):
+                        examples_hidden, dropdown_samples, samples = create_examples_tab()
+
+                    # Sources tab
+                    with gr.Tab("Sources", elem_id="tab-sources", id=1) as tab_sources:
+                        sources_textbox = gr.HTML(show_label=False, elem_id="sources-textbox")
+
+
+                    # Recommended content tab
+                    with gr.Tab("Recommended content", elem_id="tab-recommended_content", id=2) as tab_recommended_content:
+                        with gr.Tabs(elem_id="group-subtabs") as tabs_recommended_content:
+                            # Figures subtab
+                            with gr.Tab("Figures", elem_id="tab-figures", id=3) as tab_figures:
+                                sources_raw, new_figures, used_figures, gallery_component, figures_cards, figure_modal = create_figures_tab()
+
+                            # Papers subtab
+                            with gr.Tab("Papers", elem_id="tab-citations", id=4) as tab_papers:
+                                papers_summary, papers_html, citations_network, papers_modal = create_papers_tab()
+
+                            # Graphs subtab
+                            with gr.Tab("Graphs", elem_id="tab-graphs", id=5) as tab_graphs:
+                                graphs_container = gr.HTML(
+                                    "<h2>There are no graphs to be displayed at the moment. Try asking another question.</h2>",
+                                    elem_id="graphs-container"
+                                )
+    return {
+        "chatbot": chatbot,
+        "textbox": textbox,
+        "tabs": tabs,
+        "sources_raw": sources_raw,
+        "new_figures": new_figures,
+        "current_graphs": current_graphs,
+        "examples_hidden": examples_hidden,
+        "dropdown_samples": dropdown_samples,
+        "samples": samples,
+        "sources_textbox": sources_textbox,
+        "figures_cards": figures_cards,
+        "gallery_component": gallery_component,
+        "config_button": config_button,
+        "papers_html": papers_html,
+        "citations_network": citations_network,
+        "papers_summary": papers_summary,
+        "tab_recommended_content": tab_recommended_content,
+        "tab_sources": tab_sources,
+        "tab_figures": tab_figures,
+        "tab_graphs": tab_graphs,
+        "tab_papers": tab_papers,
+        "graph_container": graphs_container
+    }

front/tabs/tab_about.py

@@ -0,0 +1,38 @@
+import gradio as gr
+
+# Citation information
+CITATION_LABEL = "BibTeX citation for ClimateQ&A"
+CITATION_TEXT = r"""@misc{climateqa,
+    author={Théo Alves Da Costa, Timothée Bohe},
+    title={ClimateQ&A, AI-powered conversational assistant for climate change and biodiversity loss},
+    year={2024},
+    howpublished= {\url{https://climateqa.com}},
+}
+@software{climateqa,
+    author = {Théo Alves Da Costa, Timothée Bohe},
+    publisher = {ClimateQ&A},
+    title = {ClimateQ&A, AI-powered conversational assistant for climate change and biodiversity loss},
+}
+"""
+
+def create_about_tab():
+    with gr.Tab("About", elem_classes="max-height other-tabs"):
+        with gr.Row():
+            with gr.Column(scale=1):
+                gr.Markdown(
+                    """
+                    ### More info
+                    - See more info at [https://climateqa.com](https://climateqa.com/docs/intro/)
+                    - Feedbacks on this [form](https://forms.office.com/e/1Yzgxm6jbp)
+                                                
+                    ### Citation
+                    """
+                )
+                with gr.Accordion(CITATION_LABEL, elem_id="citation", open=False):
+                    gr.Textbox(
+                        value=CITATION_TEXT,
+                        label="",
+                        interactive=False,
+                        show_copy_button=True,
+                        lines=len(CITATION_TEXT.split('\n')),
+                    )   

front/tabs/tab_config.py

@@ -0,0 +1,123 @@
+import gradio as gr
+from gradio_modal import Modal
+from climateqa.constants import POSSIBLE_REPORTS
+from typing import TypedDict
+
+class ConfigPanel(TypedDict):
+    config_open: gr.State
+    config_modal: Modal
+    dropdown_sources: gr.CheckboxGroup
+    dropdown_reports: gr.Dropdown
+    dropdown_external_sources: gr.CheckboxGroup
+    search_only: gr.Checkbox
+    dropdown_audience: gr.Dropdown
+    after: gr.Slider
+    output_query: gr.Textbox
+    output_language: gr.Textbox
+    
+
+def create_config_modal():
+    config_open = gr.State(value=True)
+    with Modal(visible=False, elem_id="modal-config") as config_modal:
+        gr.Markdown("Reminders: You can talk in any language, ClimateQ&A is multi-lingual!")
+
+        dropdown_sources = gr.CheckboxGroup(
+            choices=["IPCC", "IPBES", "IPOS"],
+            label="Select source (by default search in all sources)",
+            value=["IPCC"],
+            interactive=True
+        )
+
+        dropdown_reports = gr.Dropdown(
+            choices=POSSIBLE_REPORTS,
+            label="Or select specific reports",
+            multiselect=True,
+            value=None,
+            interactive=True
+        )
+
+        dropdown_external_sources = gr.CheckboxGroup(
+            choices=["Figures (IPCC/IPBES)", "Papers (OpenAlex)", "Graphs (OurWorldInData)","POC region"],
+            label="Select database to search for relevant content",
+            value=["Figures (IPCC/IPBES)","POC region"],
+            interactive=True
+        )
+
+        search_only = gr.Checkbox(
+            label="Search only for recommended content without chating",
+            value=False,
+            interactive=True,
+            elem_id="checkbox-chat"
+        )
+
+        dropdown_audience = gr.Dropdown(
+            choices=["Children", "General public", "Experts"],
+            label="Select audience", 
+            value="Experts",
+            interactive=True
+        )
+
+        after = gr.Slider(
+            minimum=1950,
+            maximum=2023,
+            step=1,
+            value=1960,
+            label="Publication date",
+            show_label=True,
+            interactive=True,
+            elem_id="date-papers",
+            visible=False
+        )
+
+        output_query = gr.Textbox(
+            label="Query used for retrieval",
+            show_label=True,
+            elem_id="reformulated-query",
+            lines=2,
+            interactive=False,
+            visible=False
+        )
+
+        output_language = gr.Textbox(
+            label="Language",
+            show_label=True,
+            elem_id="language",
+            lines=1,
+            interactive=False,
+            visible=False
+        )
+
+        dropdown_external_sources.change(
+            lambda x: gr.update(visible="Papers (OpenAlex)" in x),
+            inputs=[dropdown_external_sources],
+            outputs=[after]
+        )
+
+        close_config_modal_button = gr.Button("Validate and Close", elem_id="close-config-modal")
+
+        
+        # return ConfigPanel(
+        #     config_open=config_open,
+        #     config_modal=config_modal,
+        #     dropdown_sources=dropdown_sources,
+        #     dropdown_reports=dropdown_reports,
+        #     dropdown_external_sources=dropdown_external_sources,
+        #     search_only=search_only,
+        #     dropdown_audience=dropdown_audience,
+        #     after=after,
+        #     output_query=output_query,
+        #     output_language=output_language
+        # )
+        return {
+            "config_open" : config_open,
+            "config_modal": config_modal,
+            "dropdown_sources": dropdown_sources,
+            "dropdown_reports": dropdown_reports,
+            "dropdown_external_sources": dropdown_external_sources,
+            "search_only": search_only,
+            "dropdown_audience": dropdown_audience,
+            "after": after,
+            "output_query": output_query,
+            "output_language": output_language,
+            "close_config_modal_button": close_config_modal_button
+        }

front/tabs/tab_examples.py

@@ -0,0 +1,40 @@
+import gradio as gr
+from climateqa.sample_questions import QUESTIONS
+
+
+def create_examples_tab():
+    examples_hidden = gr.Textbox(visible=False)
+    first_key = list(QUESTIONS.keys())[0]
+    dropdown_samples = gr.Dropdown(
+        choices=QUESTIONS.keys(),
+        value=first_key,
+        interactive=True,
+        label="Select a category of sample questions",
+        elem_id="dropdown-samples"
+    )
+
+    samples = []
+    for i, key in enumerate(QUESTIONS.keys()):
+        examples_visible = (i == 0)
+        with gr.Row(visible=examples_visible) as group_examples:
+            examples_questions = gr.Examples(
+                examples=QUESTIONS[key],
+                inputs=[examples_hidden],
+                examples_per_page=8,
+                run_on_click=False,
+                elem_id=f"examples{i}",
+                api_name=f"examples{i}"
+            )
+        samples.append(group_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))]
+    
+    # event listener
+    dropdown_samples.change(change_sample_questions, dropdown_samples, samples)
+
+    return examples_hidden

front/tabs/tab_figures.py

@@ -0,0 +1,31 @@
+import gradio as gr
+from gradio_modal import Modal
+
+
+def create_figures_tab():
+    sources_raw = gr.State()
+    new_figures = gr.State([])
+    used_figures = gr.State([])
+    
+    with Modal(visible=False, elem_id="modal_figure_galery") as figure_modal:
+        gallery_component = gr.Gallery(
+            object_fit='scale-down',
+            elem_id="gallery-component",
+            height="80vh"
+        )
+        
+    show_full_size_figures = gr.Button(
+        "Show figures in full size",
+        elem_id="show-figures",
+        interactive=True
+    )
+    show_full_size_figures.click(
+        lambda: Modal(visible=True),
+        None,
+        figure_modal
+    )
+
+    figures_cards = gr.HTML(show_label=False, elem_id="sources-figures")
+    
+    return sources_raw, new_figures, used_figures, gallery_component, figures_cards, figure_modal
+

front/tabs/tab_papers.py

@@ -0,0 +1,36 @@
+import gradio as gr
+from gradio_modal import Modal
+
+
+def create_papers_tab():
+    with gr.Accordion(
+        visible=True,
+        elem_id="papers-summary-popup",
+        label="See summary of relevant papers",
+        open=False
+    ) as summary_popup:
+        papers_summary = gr.Markdown("", visible=True, elem_id="papers-summary")
+
+    with gr.Accordion(
+        visible=True,
+        elem_id="papers-relevant-popup",
+        label="See relevant papers",
+        open=False
+    ) as relevant_popup:
+        papers_html = gr.HTML(show_label=False, elem_id="papers-textbox")
+
+    btn_citations_network = gr.Button("Explore papers citations network")
+    with Modal(visible=False) as papers_modal:
+        citations_network = gr.HTML(
+            "<h3>Citations Network Graph</h3>",
+            visible=True,
+            elem_id="papers-citations-network"
+        )
+    btn_citations_network.click(
+        lambda: Modal(visible=True),
+        None,
+        papers_modal
+    )
+    
+    return papers_summary, papers_html, citations_network, papers_modal
+