Merge branch 'dev' into feat/talk_to_data_graph

app.py

@@ -15,7 +15,8 @@ from climateqa.chat import start_chat, chat_stream, finish_chat
 from climateqa.engine.talk_to_data.main import ask_drias, DRIAS_MODELS
 from climateqa.engine.talk_to_data.myVanna import MyVanna
 
-from front.tabs import (create_config_modal, create_examples_tab, create_papers_tab, create_figures_tab, create_chat_interface, create_about_tab)
+from front.tabs import (create_config_modal, cqa_tab, create_about_tab)
+from front.tabs import (MainTabPanel, ConfigPanel)
 from front.utils import process_figures
 from gradio_modal import Modal
 
@@ -239,39 +240,12 @@ def cqa_tab(tab_name):
                                 )
 
                                 
-    return {
-        "chatbot": chatbot,
-        "textbox": textbox,
-        "tabs": tabs,
-        "sources_raw": sources_raw,
-        "new_figures": new_figures,
-        "current_graphs": current_graphs,
-        "examples_hidden": examples_hidden,
-        "sources_textbox": sources_textbox,
-        "figures_cards": figures_cards,
-        "gallery_component": gallery_component,
-        "config_button": config_button,
-        "papers_direct_search" : papers_direct_search,
-        "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,
-        # "vanna_sql_query": vanna_sql_query,
-        # "vanna_table" : vanna_table,
-        # "vanna_display": vanna_display
-    }
-                                
-def config_event_handling(main_tabs_components : list[dict], config_componenets : dict):
-    config_open = config_componenets["config_open"]
-    config_modal = config_componenets["config_modal"]
-    close_config_modal = config_componenets["close_config_modal_button"]
+def config_event_handling(main_tabs_components : list[MainTabPanel], config_componenets : ConfigPanel):
+    config_open = config_componenets.config_open
+    config_modal = config_componenets.config_modal
+    close_config_modal = config_componenets.close_config_modal_button
     
-    for button in [close_config_modal] + [main_tab_component["config_button"] for main_tab_component in main_tabs_components]:
+    for button in [close_config_modal] + [main_tab_component.config_button for main_tab_component in main_tabs_components]:
         button.click(
             fn=update_config_modal_visibility,
             inputs=[config_open],
@@ -279,58 +253,45 @@ def config_event_handling(main_tabs_components : list[dict], config_componenets
         ) 
     
 def event_handling(
-    main_tab_components,
-    config_components,
+    main_tab_components : MainTabPanel,
+    config_components : ConfigPanel,
     tab_name="ClimateQ&A"
 ):
-    chatbot = main_tab_components["chatbot"]
-    textbox = main_tab_components["textbox"]
-    tabs = main_tab_components["tabs"]
-    sources_raw = main_tab_components["sources_raw"]
-    new_figures = main_tab_components["new_figures"]
-    current_graphs = main_tab_components["current_graphs"]
-    examples_hidden = main_tab_components["examples_hidden"]
-    sources_textbox = main_tab_components["sources_textbox"]
-    figures_cards = main_tab_components["figures_cards"]
-    gallery_component = main_tab_components["gallery_component"]
-    # config_button = main_tab_components["config_button"]
-    papers_direct_search = main_tab_components["papers_direct_search"]
-    papers_html = main_tab_components["papers_html"]
-    citations_network = main_tab_components["citations_network"]
-    papers_summary = main_tab_components["papers_summary"]
-    tab_recommended_content = main_tab_components["tab_recommended_content"]
-    tab_sources = main_tab_components["tab_sources"]
-    tab_figures = main_tab_components["tab_figures"]
-    tab_graphs = main_tab_components["tab_graphs"]
-    tab_papers = main_tab_components["tab_papers"]
-    graphs_container = main_tab_components["graph_container"]
-    # vanna_sql_query = main_tab_components["vanna_sql_query"]
-    # vanna_table = main_tab_components["vanna_table"]
-    # vanna_display = main_tab_components["vanna_display"]
-    
+    chatbot = main_tab_components.chatbot
+    textbox = main_tab_components.textbox
+    tabs = main_tab_components.tabs
+    sources_raw = main_tab_components.sources_raw
+    new_figures = main_tab_components.new_figures
+    current_graphs = main_tab_components.current_graphs
+    examples_hidden = main_tab_components.examples_hidden
+    sources_textbox = main_tab_components.sources_textbox
+    figures_cards = main_tab_components.figures_cards
+    gallery_component = main_tab_components.gallery_component
+    papers_direct_search = main_tab_components.papers_direct_search
+    papers_html = main_tab_components.papers_html
+    citations_network = main_tab_components.citations_network
+    papers_summary = main_tab_components.papers_summary
+    tab_recommended_content = main_tab_components.tab_recommended_content
+    tab_sources = main_tab_components.tab_sources
+    tab_figures = main_tab_components.tab_figures
+    tab_graphs = main_tab_components.tab_graphs
+    tab_papers = main_tab_components.tab_papers
+    graphs_container = main_tab_components.graph_container
+    follow_up_examples = main_tab_components.follow_up_examples
+    follow_up_examples_hidden = main_tab_components.follow_up_examples_hidden
     
-    # config_open = config_components["config_open"]
-    # config_modal = config_components["config_modal"]
-    dropdown_sources = config_components["dropdown_sources"]
-    dropdown_reports = config_components["dropdown_reports"]
-    dropdown_external_sources = config_components["dropdown_external_sources"]
-    search_only = config_components["search_only"]
-    dropdown_audience = config_components["dropdown_audience"]
-    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"]
+    dropdown_sources = config_components.dropdown_sources
+    dropdown_reports = config_components.dropdown_reports
+    dropdown_external_sources = config_components.dropdown_external_sources
+    search_only = config_components.search_only
+    dropdown_audience = config_components.dropdown_audience
+    after = config_components.after
+    output_query = config_components.output_query
+    output_language = config_components.output_language
     
     new_sources_hmtl = gr.State([])
     ttd_data = gr.State([])
 
-     
-    # for button in [config_button, close_config_modal]:
-    #     button.click(
-    #         fn=update_config_modal_visibility,
-    #         inputs=[config_open],
-    #         outputs=[config_modal, config_open]
-    #     )
     
     if tab_name == "ClimateQ&A":
         print("chat cqa - message sent")
@@ -338,15 +299,20 @@ def event_handling(
         # Event for textbox
         (textbox
             .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(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, follow_up_examples.dataset], 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], [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(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,follow_up_examples.dataset], concurrency_limit=8, api_name=f"chat_{examples_hidden.elem_id}")
             .then(finish_chat, None, [textbox], api_name=f"finish_chat_{examples_hidden.elem_id}")
         )
+        (follow_up_examples_hidden
+            .change(start_chat, [follow_up_examples_hidden, chatbot, search_only], [follow_up_examples_hidden, tabs, chatbot, sources_raw], queue=False, api_name=f"start_chat_{examples_hidden.elem_id}")
+            .then(chat, [follow_up_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,follow_up_examples.dataset], concurrency_limit=8, api_name=f"chat_{examples_hidden.elem_id}")
+            .then(finish_chat, None, [textbox], api_name=f"finish_chat_{follow_up_examples_hidden.elem_id}")
+        )
     
     elif tab_name == "Beta - POC Adapt'Action":
         print("chat poc - message sent")
@@ -362,6 +328,11 @@ def event_handling(
             .then(chat_poc, [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, [textbox], api_name=f"finish_chat_{examples_hidden.elem_id}")
         )
+        (follow_up_examples_hidden
+            .change(start_chat, [follow_up_examples_hidden, chatbot, search_only], [follow_up_examples_hidden, tabs, chatbot, sources_raw], queue=False, api_name=f"start_chat_{examples_hidden.elem_id}")
+            .then(chat, [follow_up_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,follow_up_examples.dataset], concurrency_limit=8, api_name=f"chat_{examples_hidden.elem_id}")
+            .then(finish_chat, None, [textbox], api_name=f"finish_chat_{follow_up_examples_hidden.elem_id}")
+        )
     
     
     new_sources_hmtl.change(lambda x : x, inputs = [new_sources_hmtl], outputs = [sources_textbox])

climateqa/chat.py

@@ -101,6 +101,7 @@ async def chat_stream(
     audience_prompt = init_audience(audience)
     sources = sources or ["IPCC", "IPBES"]
     reports = reports or []
+    relevant_history_discussion = history[-2:] if len(history) > 1 else []
 
     # Prepare inputs for agent
     inputs = {
@@ -109,7 +110,8 @@ async def chat_stream(
         "sources_input": sources,
         "relevant_content_sources_selection": relevant_content_sources_selection,
         "search_only": search_only,
-        "reports": reports
+        "reports": reports,
+        "chat_history": relevant_history_discussion,
     }
 
     # Get streaming events from agent
@@ -129,6 +131,7 @@ async def chat_stream(
     retrieved_contents = []
     answer_message_content = ""
     vanna_data = {}
+    follow_up_examples = gr.Dataset(samples=[])
 
     # Define processing steps
     steps_display = {
@@ -200,7 +203,12 @@ async def chat_stream(
                         sub_questions = [q["question"] + "-> relevant sources : " + str(q["sources"]) 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#, vanna_data
+                # Handle follow up questions
+                if event["name"] == "generate_follow_up" and event["event"] == "on_chain_end": 
+                    follow_up_examples = event["data"]["output"].get("follow_up_questions", [])
+                    follow_up_examples = gr.Dataset(samples= [ [question] for question in follow_up_examples ])
+
+            yield history, docs_html, output_query, output_language, related_contents, graphs_html, follow_up_examples#, vanna_data
 
     except Exception as e:
         print(f"Event {event} has failed")
@@ -211,4 +219,4 @@ async def chat_stream(
     # 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#, vanna_data
+    yield history, docs_html, output_query, output_language, related_contents, graphs_html, follow_up_examples#, vanna_data

climateqa/engine/chains/answer_rag.py

@@ -65,6 +65,7 @@ def make_rag_node(llm,with_docs = True):
     async def answer_rag(state,config):
         print("---- Answer RAG ----")
         start_time = time.time()
+        chat_history = state.get("chat_history",[])
         print("Sources used : " +  "\n".join([x.metadata["short_name"] + " - page " + str(x.metadata["page_number"])  for x in state["documents"]]))
 
         answer = await rag_chain.ainvoke(state,config)
@@ -73,9 +74,10 @@ def make_rag_node(llm,with_docs = True):
         elapsed_time = end_time - start_time
         print("RAG elapsed time: ", elapsed_time)
         print("Answer size : ", len(answer))
-        # print(f"\n\nAnswer:\n{answer}")
         
-        return {"answer":answer}
+        chat_history.append({"question":state["query"],"answer":answer})
+        
+        return {"answer":answer,"chat_history": chat_history}
 
     return answer_rag
 

climateqa/engine/chains/follow_up.py

@@ -0,0 +1,32 @@
+from typing import List
+from langchain.prompts import ChatPromptTemplate
+
+
+FOLLOW_UP_TEMPLATE = """Based on the previous question and answer, generate 2-3 relevant follow-up questions that would help explore the topic further.
+
+Previous Question: {user_input}
+Previous Answer: {answer}
+
+Generate short, concise, focused follow-up questions
+You don't need a full question as it will be reformulated later as a standalone question with the context. Eg. "Details the first point"
+"""
+
+def make_follow_up_node(llm):
+    prompt = ChatPromptTemplate.from_template(FOLLOW_UP_TEMPLATE)
+    
+    def generate_follow_up(state):
+        if not state.get("answer"):
+            return state
+            
+        response = llm.invoke(prompt.format(
+            user_input=state["user_input"],
+            answer=state["answer"]
+        ))
+        
+        # Extract questions from response
+        follow_ups = [q.strip() for q in response.content.split("\n") if q.strip()]
+        state["follow_up_questions"] = follow_ups
+        
+        return state
+        
+    return generate_follow_up

climateqa/engine/chains/retrieve_documents.py

@@ -621,10 +621,7 @@ def make_IPx_retriever_node(vectorstore,reranker,llm,rerank_by_question=True, k_
 
 def make_POC_retriever_node(vectorstore,reranker,llm,rerank_by_question=True, k_final=15, k_before_reranking=100, k_summary=5):
     
-    async def retrieve_POC_docs_node(state, config):
-        if "POC region" not in state["relevant_content_sources_selection"]  :  
-            return {}
-        
+    async def retrieve_POC_docs_node(state, config):        
         source_type = "POC"
         POC_questions_index = [i for i, x in enumerate(state["questions_list"]) if x["source_type"] == "POC"]
         
@@ -665,10 +662,7 @@ def make_POC_by_ToC_retriever_node(
         k_summary=5,
     ):
     
-    async def retrieve_POC_docs_node(state, config):
-        if "POC region" not in state["relevant_content_sources_selection"]  :  
-            return {}
-        
+    async def retrieve_POC_docs_node(state, config):     
         search_figures = "Figures (IPCC/IPBES)" in state["relevant_content_sources_selection"]
         search_only = state["search_only"]
         search_only = state["search_only"]

climateqa/engine/chains/standalone_question.py

@@ -0,0 +1,39 @@
+from langchain.prompts import ChatPromptTemplate
+
+def make_standalone_question_chain(llm):
+    prompt = ChatPromptTemplate.from_messages([
+        ("system", """You are a helpful assistant that transforms user questions into standalone questions 
+        by incorporating context from the chat history if needed. The output should be a self-contained 
+        question that can be understood without any additional context.
+        
+        Examples:
+        Chat History: "Let's talk about renewable energy"
+        User Input: "What about solar?"
+        Output: "What are the key aspects of solar energy as a renewable energy source?"
+        
+        Chat History: "What causes global warming?"
+        User Input: "And what are its effects?"
+        Output: "What are the effects of global warming on the environment and society?"
+        """),
+        ("user", """Chat History: {chat_history}
+        User Question: {question}
+        
+        Transform this into a standalone question:""")
+    ])
+    
+    chain = prompt | llm
+    return chain
+
+def make_standalone_question_node(llm):
+    standalone_chain = make_standalone_question_chain(llm)
+    
+    def transform_to_standalone(state):
+        chat_history = state.get("chat_history", "")
+        output = standalone_chain.invoke({
+            "chat_history": chat_history,
+            "question": state["user_input"]
+        })
+        state["user_input"] = output.content
+        return state
+        
+    return transform_to_standalone

climateqa/engine/graph.py

@@ -23,13 +23,15 @@ from .chains.retrieve_documents import make_IPx_retriever_node, make_POC_retriev
 from .chains.answer_rag import make_rag_node
 from .chains.graph_retriever import make_graph_retriever_node
 from .chains.chitchat_categorization import make_chitchat_intent_categorization_node
-# from .chains.set_defaults import set_defaults
+from .chains.standalone_question import make_standalone_question_node
+from .chains.follow_up import make_follow_up_node  # Add this import
 
 class GraphState(TypedDict):
     """
     Represents the state of our graph.
     """
     user_input : str
+    chat_history : str
     language : str
     intent : str
     search_graphs_chitchat : bool
@@ -49,6 +51,7 @@ class GraphState(TypedDict):
     recommended_content : List[Document] # OWID Graphs  # TODO merge with related_contents
     search_only : bool = False
     reports : List[str] = []
+    follow_up_questions: List[str] = []
 
 def dummy(state):
     return 
@@ -100,15 +103,6 @@ def route_continue_retrieve_documents(state):
     else:
         return "retrieve_documents"
     
-def route_continue_retrieve_local_documents(state):
-    index_question_poc = [i for i, x in enumerate(state["questions_list"]) if x["source_type"] == "POC"]
-    questions_poc_finished = all(elem in state["handled_questions_index"] for elem in index_question_poc)
-    # if questions_poc_finished and state["search_only"]:
-    #     return END
-    if questions_poc_finished or ("POC region" not in state["relevant_content_sources_selection"]):
-        return "end_retrieve_local_documents"
-    else:
-        return "retrieve_local_data"
     
 def route_retrieve_documents(state):
     sources_to_retrieve = []
@@ -120,6 +114,11 @@ def route_retrieve_documents(state):
         return END
     return sources_to_retrieve
 
+def route_follow_up(state):
+    if state["follow_up_questions"]:
+        return "process_follow_up"
+    return END
+
 def make_id_dict(values):
     return {k:k for k in values}
 
@@ -128,6 +127,7 @@ def make_graph_agent(llm, vectorstore_ipcc, vectorstore_graphs, vectorstore_regi
     workflow = StateGraph(GraphState)
 
     # Define the node functions
+    standalone_question_node = make_standalone_question_node(llm)
     categorize_intent = make_intent_categorization_node(llm)
     transform_query = make_query_transform_node(llm)
     translate_query = make_translation_node(llm)
@@ -139,9 +139,11 @@ def make_graph_agent(llm, vectorstore_ipcc, vectorstore_graphs, vectorstore_regi
     answer_rag = make_rag_node(llm, with_docs=True)
     answer_rag_no_docs = make_rag_node(llm, with_docs=False)
     chitchat_categorize_intent = make_chitchat_intent_categorization_node(llm)
+    generate_follow_up = make_follow_up_node(llm)
 
     # Define the nodes
     # workflow.add_node("set_defaults", set_defaults)
+    workflow.add_node("standalone_question", standalone_question_node)
     workflow.add_node("categorize_intent", categorize_intent)
     workflow.add_node("answer_climate", dummy)
     workflow.add_node("answer_search", answer_search)
@@ -155,9 +157,11 @@ def make_graph_agent(llm, vectorstore_ipcc, vectorstore_graphs, vectorstore_regi
     workflow.add_node("retrieve_documents", retrieve_documents)
     workflow.add_node("answer_rag", answer_rag)
     workflow.add_node("answer_rag_no_docs", answer_rag_no_docs)
+    workflow.add_node("generate_follow_up", generate_follow_up)
+    # workflow.add_node("process_follow_up", standalone_question_node)
 
     # Entry point
-    workflow.set_entry_point("categorize_intent")
+    workflow.set_entry_point("standalone_question")
 
     # CONDITIONAL EDGES
     workflow.add_conditional_edges(
@@ -189,20 +193,29 @@ def make_graph_agent(llm, vectorstore_ipcc, vectorstore_graphs, vectorstore_regi
         make_id_dict(["retrieve_graphs", END])
     )
 
+    # workflow.add_conditional_edges(
+    #     "generate_follow_up",
+    #     route_follow_up,
+    #     make_id_dict(["process_follow_up", END])
+    # )
+
     # Define the edges
+    workflow.add_edge("standalone_question", "categorize_intent")
     workflow.add_edge("translate_query", "transform_query")
     workflow.add_edge("transform_query", "retrieve_documents") #TODO put back
     # workflow.add_edge("transform_query", "retrieve_local_data")
     # workflow.add_edge("transform_query", END) # TODO remove
 
     workflow.add_edge("retrieve_graphs", END)
-    workflow.add_edge("answer_rag", END)
-    workflow.add_edge("answer_rag_no_docs", END)
+    workflow.add_edge("answer_rag", "generate_follow_up")
+    workflow.add_edge("answer_rag_no_docs", "generate_follow_up")
     workflow.add_edge("answer_chitchat", "chitchat_categorize_intent")
     workflow.add_edge("retrieve_graphs_chitchat", END)
 
     # workflow.add_edge("retrieve_local_data", "answer_search")
     workflow.add_edge("retrieve_documents", "answer_search")
+    workflow.add_edge("generate_follow_up",END)
+    # workflow.add_edge("process_follow_up", "categorize_intent")
 
     # Compile
     app = workflow.compile()
@@ -228,6 +241,8 @@ def make_graph_agent_poc(llm, vectorstore_ipcc, vectorstore_graphs, vectorstore_
     workflow = StateGraph(GraphState)
 
     # Define the node functions
+    standalone_question_node = make_standalone_question_node(llm)
+
     categorize_intent = make_intent_categorization_node(llm)
     transform_query = make_query_transform_node(llm)
     translate_query = make_translation_node(llm)
@@ -240,9 +255,11 @@ def make_graph_agent_poc(llm, vectorstore_ipcc, vectorstore_graphs, vectorstore_
     answer_rag = make_rag_node(llm, with_docs=True)
     answer_rag_no_docs = make_rag_node(llm, with_docs=False)
     chitchat_categorize_intent = make_chitchat_intent_categorization_node(llm)
+    generate_follow_up = make_follow_up_node(llm)
 
     # Define the nodes
     # workflow.add_node("set_defaults", set_defaults)
+    workflow.add_node("standalone_question", standalone_question_node)
     workflow.add_node("categorize_intent", categorize_intent)
     workflow.add_node("answer_climate", dummy)
     workflow.add_node("answer_search", answer_search)
@@ -258,9 +275,10 @@ def make_graph_agent_poc(llm, vectorstore_ipcc, vectorstore_graphs, vectorstore_
     workflow.add_node("retrieve_documents", retrieve_documents)
     workflow.add_node("answer_rag", answer_rag)
     workflow.add_node("answer_rag_no_docs", answer_rag_no_docs)
+    workflow.add_node("generate_follow_up", generate_follow_up)
 
     # Entry point
-    workflow.set_entry_point("categorize_intent")
+    workflow.set_entry_point("standalone_question")
 
     # CONDITIONAL EDGES
     workflow.add_conditional_edges(
@@ -293,22 +311,21 @@ def make_graph_agent_poc(llm, vectorstore_ipcc, vectorstore_graphs, vectorstore_
     )
 
     # Define the edges
+    workflow.add_edge("standalone_question", "categorize_intent")
     workflow.add_edge("translate_query", "transform_query")
     workflow.add_edge("transform_query", "retrieve_documents") #TODO put back
     workflow.add_edge("transform_query", "retrieve_local_data")
     # workflow.add_edge("transform_query", END) # TODO remove
 
     workflow.add_edge("retrieve_graphs", END)
-    workflow.add_edge("answer_rag", END)
-    workflow.add_edge("answer_rag_no_docs", END)
+    workflow.add_edge("answer_rag", "generate_follow_up")
+    workflow.add_edge("answer_rag_no_docs", "generate_follow_up")
     workflow.add_edge("answer_chitchat", "chitchat_categorize_intent")
     workflow.add_edge("retrieve_graphs_chitchat", END)
 
     workflow.add_edge("retrieve_local_data", "answer_search")
     workflow.add_edge("retrieve_documents", "answer_search")
-
-    # workflow.add_edge("transform_query", "retrieve_drias_data")
-    # workflow.add_edge("retrieve_drias_data", END)
+    workflow.add_edge("generate_follow_up",END)
 
 
     # Compile

front/tabs/__init__.py

@@ -3,4 +3,7 @@ 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
+from .tab_about import create_about_tab
+from .main_tab import MainTabPanel
+from .tab_config import ConfigPanel
+from .main_tab import cqa_tab

front/tabs/chat_interface.py

@@ -21,21 +21,21 @@ What do you want to learn ?
 """
 
 init_prompt_poc = """
-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, PCAET of Paris, the Plan Biodiversité 2018-2024, and Acclimaterra reports from la Région Nouvelle-Aquitaine **.
+Bonjour, je suis ClimateQ&A, un assistant conversationnel conçu pour vous aider à comprendre le changement climatique et la perte de biodiversité. Je réponds à vos questions en **parcourant les rapports scientifiques du GIEC et de l'IPBES, le PCAET de Paris, le Plan Biodiversité 2018-2024, et les rapports Acclimaterra de la Région Nouvelle-Aquitaine**.
 
-❓ 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, and POC sources for local documents (PCAET, Plan Biodiversité, Acclimaterra).
-- **Relevant content sources**: You can choose to search for figures, papers, or graphs that can be relevant for your question.
+❓ Mode d'emploi
+- **Language** : Vous pouvez me poser vos questions dans n'importe quelle langue. 
+- **Audience** : Vous pouvez préciser votre public (enfants, grand public, experts) pour obtenir une réponse plus adaptée.
+- **Sources** : Vous pouvez choisir de chercher dans les rapports du GIEC ou de l'IPBES, et dans les sources POC pour les documents locaux (PCAET, Plan Biodiversité, Acclimaterra).
+- **Relevant content sources** : Vous pouvez choisir de rechercher des images, des papiers scientifiques ou des graphiques qui peuvent être pertinents pour votre 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.*
+*Veuillez noter que l'IA n'est pas parfaite et peut parfois donner des réponses non pertinentes. Si vous n'êtes pas satisfait de la réponse, veuillez poser une question plus précise ou nous faire part de vos commentaires pour nous aider à améliorer le système.*
 
-🛈 Information
-Please note that we log your questions for meta-analysis purposes, so avoid sharing any sensitive or personal information.
+🛈 Informations
+Veuillez noter que nous enregistrons vos questions à des fins de méta-analyse, évitez donc de partager toute information sensible ou personnelle.
 
-What do you want to learn ?
+Que voulez-vous apprendre ?
 """
 
 
@@ -54,7 +54,10 @@ def create_chat_interface(tab):
         max_height="80vh",
         height="100vh"
     )
-    
+    with gr.Row(elem_id="follow-up-examples"):
+        follow_up_examples_hidden = gr.Textbox(visible=False, elem_id="follow-up-hidden")
+        follow_up_examples = gr.Examples(examples=[ ], label="Follow up questions", inputs= [follow_up_examples_hidden], elem_id="follow-up-button", run_on_click=False)
+
     with gr.Row(elem_id="input-message"):
         
         textbox = gr.Textbox(
@@ -68,7 +71,7 @@ def create_chat_interface(tab):
         
         config_button = gr.Button("", elem_id="config-button")
     
-    return chatbot, textbox, config_button
+    return chatbot, textbox, config_button, follow_up_examples, follow_up_examples_hidden
 
 
         

front/tabs/main_tab.py

@@ -1,8 +1,37 @@
 import gradio as gr
+from gradio.helpers import Examples
+from typing import TypedDict
 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 dataclasses import dataclass
+
+@dataclass
+class MainTabPanel:
+    chatbot: gr.Chatbot
+    textbox: gr.Textbox
+    tabs: gr.Tabs
+    sources_raw: gr.State
+    new_figures: gr.State
+    current_graphs: gr.State
+    examples_hidden: gr.State
+    sources_textbox: gr.HTML
+    figures_cards: gr.HTML
+    gallery_component: gr.Gallery
+    config_button: gr.Button
+    papers_direct_search: gr.TextArea
+    papers_html: gr.HTML
+    citations_network: gr.Plot
+    papers_summary: gr.Textbox
+    tab_recommended_content: gr.Tab
+    tab_sources: gr.Tab
+    tab_figures: gr.Tab
+    tab_graphs: gr.Tab
+    tab_papers: gr.Tab
+    graph_container: gr.HTML
+    follow_up_examples : Examples
+    follow_up_examples_hidden : gr.Textbox
 
 def cqa_tab(tab_name):
     # State variables
@@ -11,14 +40,14 @@ def cqa_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(tab_name)
+                chatbot, textbox, config_button, follow_up_examples, follow_up_examples_hidden = create_chat_interface(tab_name)
 
             # 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()
+                        examples_hidden = create_examples_tab(tab_name)
 
                     # Sources tab
                     with gr.Tab("Sources", elem_id="tab-sources", id=1) as tab_sources:
@@ -34,7 +63,7 @@ def cqa_tab(tab_name):
 
                             # 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()
+                                papers_direct_search, 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:
@@ -42,27 +71,30 @@ def cqa_tab(tab_name):
                                     "<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
-    }
+
+                                
+    return MainTabPanel(
+        chatbot=chatbot,
+        textbox=textbox,
+        tabs=tabs,
+        sources_raw=sources_raw,
+        new_figures=new_figures,
+        current_graphs=current_graphs,
+        examples_hidden=examples_hidden,
+        sources_textbox=sources_textbox,
+        figures_cards=figures_cards,
+        gallery_component=gallery_component,
+        config_button=config_button,
+        papers_direct_search=papers_direct_search,
+        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,
+        follow_up_examples= follow_up_examples,
+        follow_up_examples_hidden = follow_up_examples_hidden
+    )

front/tabs/tab_config.py

@@ -2,8 +2,10 @@ import gradio as gr
 from gradio_modal import Modal
 from climateqa.constants import POSSIBLE_REPORTS
 from typing import TypedDict
+from dataclasses import dataclass
 
-class ConfigPanel(TypedDict):
+@dataclass
+class ConfigPanel:
     config_open: gr.State
     config_modal: Modal
     dropdown_sources: gr.CheckboxGroup
@@ -14,6 +16,7 @@ class ConfigPanel(TypedDict):
     after: gr.Slider
     output_query: gr.Textbox
     output_language: gr.Textbox
+    close_config_modal_button: gr.Button
     
 
 def create_config_modal():
@@ -37,9 +40,9 @@ def create_config_modal():
         )
 
         dropdown_external_sources = gr.CheckboxGroup(
-            choices=["Figures (IPCC/IPBES)", "Papers (OpenAlex)", "Graphs (OurWorldInData)","POC region"],
+            choices=["Figures (IPCC/IPBES)", "Papers (OpenAlex)", "Graphs (OurWorldInData)"],
             label="Select database to search for relevant content",
-            value=["Figures (IPCC/IPBES)","POC region"],
+            value=["Figures (IPCC/IPBES)"],
             interactive=True
         )
 
@@ -95,29 +98,16 @@ def create_config_modal():
 
         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
-        }
+        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,
+            close_config_modal_button=close_config_modal_button
+        )

style.css

@@ -115,6 +115,11 @@ main.flex.flex-1.flex-col {
     border-radius: 40px;
     padding-left: 30px;
     resize: none;
+    background-color: #f0f8ff; /* Light blue background */
+    border: 2px solid #4b8ec3; /* Blue border */
+    font-size: 16px; /* Increase font size */
+    color: #333; /* Text color */
+    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1); /* Add shadow */
 }
 
 #input-message > div {
@@ -474,6 +479,18 @@ a {
     text-decoration: none !important;
 }
 
+/* Follow-up Examples Styles */
+#follow-up-examples {
+    height: 15vh;
+    overflow-y: auto;
+    padding: 10px 0;
+}
+
+#follow-up-button {
+    height: 100%;
+    overflow-y: auto;
+}
+
 /* Media Queries */
 /* Desktop Media Query */
 @media screen and (min-width: 1024px) {
@@ -495,6 +512,15 @@ a {
         overflow-y: scroll !important;
     }
 
+    div#chatbot-row {
+        max-height: calc(100vh - 200px) !important;
+    }
+
+    div#chatbot {
+        height: 65vh !important;
+        max-height: 65vh !important;
+    }
+
     div#chatbot-row {
         max-height: calc(100vh - 90px) !important;
     }
@@ -513,7 +539,11 @@ a {
 /* Mobile Media Query */
 @media screen and (max-width: 767px) {
     div#chatbot {
-        height: 500px !important;
+        height: 400px !important;  /* Reduced from 500px */
+    }
+
+    #follow-up-examples {
+        height: 100px;
     }
 
     #submit-button {