feat: added plot informations for each plot (DRIAS & IPCC)

climateqa/engine/talk_to_data/drias/plot_informations.py

@@ -0,0 +1,88 @@
+from climateqa.engine.talk_to_data.drias.config import DRIAS_INDICATOR_TO_UNIT
+
+def indicator_evolution_informations(
+        indicator: str,
+        params: dict[str, str]
+) -> str:
+    unit = DRIAS_INDICATOR_TO_UNIT[indicator]
+    if "location" not in params:
+        raise ValueError('"location" must be provided in params')
+    location = params["location"]
+    return f"""
+This plot shows how the climate indicator **{indicator}** evolves over time in **{location}**.
+
+It combines both historical observations and future projections according to the climate scenario RCP8.5.
+
+The x-axis represents the years, and the y-axis shows the value of the indicator ({unit}).
+
+A 10-year rolling average curve is displayed to give a better idea of the overall trend.
+
+**Data source:**
+- The data come from the DRIAS TRACC data. The data were initially extracted from [the DRIAS website](https://www.drias-climat.fr/drias_prod/accueil/okapiWebDrias/index.jsp?iddrias=climat) and then preprocessed to a tabular format and uploaded as parquet in this [Hugging Face dataset](https://huggingface.co/datasets/timeki/drias_db).
+- For each year and climate model, the value of {indicator} in {location} is collected, to build the time series.
+- The coordinates used for {location} correspond to the closest available point in the DRIAS database, which uses a regular grid with a spatial resolution of 8 km.
+- The indicator values shown are those for the selected climate model.
+- If ALL climate model is selected, the average value of the indicator between all the climate models is used. 
+"""
+
+def indicator_number_of_days_per_year_informations(
+        indicator: str,
+        params: dict[str, str]
+) -> str:
+    unit = DRIAS_INDICATOR_TO_UNIT[indicator]
+    if "location" not in params:
+        raise ValueError('"location" must be provided in params')
+    location = params["location"]
+    return f"""
+This plot displays a bar chart showing the yearly frequency of the climate indicator **{indicator}** in **{location}**.
+
+The x-axis represents the years, and the y-axis shows the frequency of {indicator} ({unit}) per year.
+
+**Data source:**  
+- The data come from the DRIAS TRACC data. The data were initially extracted from [the DRIAS website](https://www.drias-climat.fr/drias_prod/accueil/okapiWebDrias/index.jsp?iddrias=climat) and then preprocessed to a tabular format and uploaded as parquet in this [Hugging Face dataset](https://huggingface.co/datasets/timeki/drias_db).
+- For each year and climate model, the value of {indicator} in {location} is collected, to build the time series.
+- The coordinates used for {location} correspond to the closest available point in the DRIAS database, which uses a regular grid with a spatial resolution of 8 km.
+- The indicator values shown are those for the selected climate model.
+- If ALL climate model is selected, the average value of the indicator between all the climate models is used. 
+"""
+
+def distribution_of_indicator_for_given_year_informations(
+        indicator: str,
+        params: dict[str, str]
+) -> str:
+    unit = DRIAS_INDICATOR_TO_UNIT[indicator]
+    year = params["year"]
+    if year is None:
+        year = 2030
+    return f"""
+This plot shows a histogram of the distribution of the climate indicator **{indicator}** across all locations for the year **{year}**.
+
+It allows you to visualize how the values of {indicator} ({unit}) are spread for a given year.
+
+**Data source:**  
+- The data come from the DRIAS TRACC data. The data were initially extracted from [the DRIAS website](https://www.drias-climat.fr/drias_prod/accueil/okapiWebDrias/index.jsp?iddrias=climat) and then preprocessed to a tabular format and uploaded as parquet in this [Hugging Face dataset](https://huggingface.co/datasets/timeki/drias_db).
+- For each grid point in the dataset and climate model, the value of {indicator} for the year {year} is extracted.
+- The indicator values shown are those for the selected climate model.
+- If ALL climate model is selected, the average value of the indicator between all the climate models is used. 
+"""
+
+def map_of_france_of_indicator_for_given_year_informations(
+        indicator: str,
+        params: dict[str, str]
+) -> str:
+    unit = DRIAS_INDICATOR_TO_UNIT[indicator]
+    year = params["year"]
+    if year is None:
+        year = 2030
+    return f"""
+This plot displays a choropleth map showing the spatial distribution of **{indicator}** across all regions of France for the year **{year}**.
+
+Each region is colored according to the value of the indicator ({unit}), allowing you to visually compare how {indicator} varies geographically within France for the selected year and climate model.
+
+**Data source:**  
+- The data come from the DRIAS TRACC data. The data were initially extracted from [the DRIAS website](https://www.drias-climat.fr/drias_prod/accueil/okapiWebDrias/index.jsp?iddrias=climat) and then preprocessed to a tabular format and uploaded as parquet in this [Hugging Face dataset](https://huggingface.co/datasets/timeki/drias_db).
+- For each region of France, the value of {indicator} in {year} and for the selected climate model is extracted and mapped to its geographic coordinates.
+- The regions correspond to 8 km squares centered on the grid points of the DRIAS dataset.
+- The indicator values shown are those for the selected climate model.
+- If ALL climate model is selected, the average value of the indicator between all the climate models is used.
+"""

climateqa/engine/talk_to_data/drias/plots.py

@@ -5,6 +5,7 @@ from typing import Callable
 import pandas as pd
 from plotly.graph_objects import Figure
 import plotly.graph_objects as go
+from climateqa.engine.talk_to_data.drias.plot_informations import distribution_of_indicator_for_given_year_informations, indicator_evolution_informations, indicator_number_of_days_per_year_informations, map_of_france_of_indicator_for_given_year_informations
 from climateqa.engine.talk_to_data.objects.plot import Plot
 from climateqa.engine.talk_to_data.drias.queries import (
     indicator_for_given_year_query,
@@ -162,6 +163,7 @@ indicator_evolution_at_location: Plot = {
     "params": ["indicator_column", "location", "model"],
     "plot_function": plot_indicator_evolution_at_location,
     "sql_query": indicator_per_year_at_location_query,
+    "plot_information": indicator_evolution_informations,
     'short_name': 'Indicator Evolution'
 }
 
@@ -246,6 +248,7 @@ indicator_number_of_days_per_year_at_location: Plot = {
     "params": ["indicator_column", "location", "model"],
     "plot_function": plot_indicator_number_of_days_per_year_at_location,
     "sql_query": indicator_per_year_at_location_query,
+    "plot_information": indicator_number_of_days_per_year_informations,
     "short_name": "Indicator Yearly Frequency",
 }
 
@@ -331,6 +334,7 @@ distribution_of_indicator_for_given_year: Plot = {
     "params": ["indicator_column", "model", "year"],
     "plot_function": plot_distribution_of_indicator_for_given_year,
     "sql_query": indicator_for_given_year_query,
+    "plot_information": distribution_of_indicator_for_given_year_informations,
     'short_name': 'Indicator Distribution'
 }
 
@@ -418,6 +422,7 @@ map_of_france_of_indicator_for_given_year: Plot = {
     "params": ["indicator_column", "year", "model"],
     "plot_function": plot_map_of_france_of_indicator_for_given_year,
     "sql_query": indicator_for_given_year_query,
+    "plot_information": map_of_france_of_indicator_for_given_year_informations,
     'short_name': 'Map of France'
 }
 

climateqa/engine/talk_to_data/ipcc/plot_informations.py

@@ -0,0 +1,50 @@
+from climateqa.engine.talk_to_data.ipcc.config import IPCC_INDICATOR_TO_UNIT
+
+def indicator_evolution_informations(
+        indicator: str,
+        params: dict[str,str],
+) -> str:
+    if "location" not in params:
+        raise ValueError('"location" must be provided in params')
+    location = params["location"]
+
+    unit = IPCC_INDICATOR_TO_UNIT[indicator]
+    return f"""
+This plot shows how the climate indicator **{indicator}** evolves over time in **{location}**.
+
+It combines both historical (from 1950 to 2015) observations and future (from 2016 to 2100) projections for the different SSP climate scenarios (SSP126, SSP245, SSP370 and SSP585).
+
+The x-axis represents the years (from 1950 to 2100), and the y-axis shows the value of the {indicator} ({unit}).
+
+Each line corresponds to a different scenario, allowing you to compare how {indicator} might change under various future conditions.
+
+**Data source:**  
+- The data comes from the CMIP6 IPCC ATLAS data. The data were initially extracted from [this referenced website](https://digital.csic.es/handle/10261/332744) and then preprocessed to a tabular format and uploaded as parquet in this [Hugging Face dataset](https://huggingface.co/datasets/Ekimetrics/ipcc-atlas).
+- The underlying data is retrieved by aggregating yearly values of {indicator} for the selected location, across all available scenarios. This means the system collects, for each year, the value of {indicator} in {location}, both for the historical period and for each scenario, to build the time series.
+- The coordinates used for {location} correspond to the closest available point in the IPCC database, which uses a regular grid with a spatial resolution of 1 degree.
+"""
+
+def choropleth_map_informations(
+        indicator: str,
+        params: dict[str, str],
+) -> str:
+    unit = IPCC_INDICATOR_TO_UNIT[indicator]
+    if "location" not in params:
+        raise ValueError('"location" must be provided in params')
+    location = params["location"]
+    country_name = params["country_name"]
+    year = params["year"]
+    if year is None:
+        year = 2050
+
+    return f"""
+This plot displays a choropleth map showing the spatial distribution of **{indicator}** across all regions of **{location}** country ({country_name}) for the year **{year}** and the chosen scenario.
+
+Each region is colored according to the value of the indicator ({unit}), allowing you to visually compare how {indicator} varies geographically within the country for the selected year and scenario.
+
+**Data source:**  
+- The data come from the CMIP6 IPCC ATLAS data. The data were initially extracted from [this referenced website](https://digital.csic.es/handle/10261/332744) and then preprocessed to a tabular format and uploaded as parquet in this [Hugging Face dataset](https://huggingface.co/datasets/Ekimetrics/ipcc-atlas).
+- For each region of {location} country ({country_name}), the value of {indicator} in {year} and for the selected scenario is extracted and mapped to its geographic coordinates.
+- The regions correspond to 1-degree squares centered on the grid points of the IPCC dataset.
+- The coordinates used for each region are those of the closest available grid point in the IPCC database, which uses a regular grid with a spatial resolution of 1 degree.
+"""

climateqa/engine/talk_to_data/ipcc/plots.py

@@ -5,6 +5,7 @@ import pandas as pd
 import geojson
 
 from climateqa.engine.talk_to_data.ipcc.config import IPCC_INDICATOR_TO_COLORSCALE, IPCC_INDICATOR_TO_UNIT, IPCC_SCENARIO
+from climateqa.engine.talk_to_data.ipcc.plot_informations import choropleth_map_informations, indicator_evolution_informations
 from climateqa.engine.talk_to_data.ipcc.queries import indicator_for_given_year_query, indicator_per_year_at_location_query
 from climateqa.engine.talk_to_data.objects.plot import Plot
 
@@ -97,6 +98,7 @@ indicator_evolution_at_location_historical_and_projections: Plot = {
     "params": ["indicator_column", "location"],
     "plot_function": plot_indicator_evolution_at_location_historical_and_projections,
     "sql_query": indicator_per_year_at_location_query,
+    "plot_information": indicator_evolution_informations,
     "short_name": "Indicator Evolution"
 }
 
@@ -177,7 +179,8 @@ choropleth_map_of_country_indicator_for_specific_year: Plot = {
     "params": ["indicator_column", "year", "location"],
     "plot_function": plot_choropleth_map_of_country_indicator_for_specific_year,
     "sql_query": indicator_for_given_year_query,
-    "short_name": "Choropleth Map"
+    "plot_information": choropleth_map_informations,
+    "short_name": "Choropleth Map",
 }
 
 IPCC_PLOTS = [

climateqa/engine/talk_to_data/main.py

@@ -5,7 +5,6 @@ from climateqa.engine.llm import get_llm
 from climateqa.engine.talk_to_data.workflow.ipcc import ipcc_workflow
 from climateqa.logging import log_drias_interaction_to_huggingface
 from climateqa.logging import log_drias_interaction_to_huggingface
-import ast
 
 async def ask_drias(query: str, index_state: int = 0, user_id: str | None = None) -> tuple:
     """Main function to process a DRIAS query and return results.
@@ -35,12 +34,16 @@ async def ask_drias(query: str, index_state: int = 0, user_id: str | None = None
     result_dataframes = []
     figures = []
     plot_title_list = []
-
+    plot_informations = []
 
     for output_title, output in final_state['outputs'].items():
         if output['status'] == 'OK':
             if output['table'] is not None: 
                 plot_title_list.append(output_title)
+            
+            if output['plot_information'] is not None:
+                plot_informations.append(output['plot_information'])
+
             if output['sql_query'] is not None:
                 sql_queries.append(output['sql_query'])
             
@@ -50,17 +53,19 @@ async def ask_drias(query: str, index_state: int = 0, user_id: str | None = None
                     figures.append(output['figure'])
 
     if "error" in final_state and final_state["error"] != "":
-        # No Sql query, no dataframe, no figure, empty sql queries list, empty result dataframes list, empty figures list, index state = 0, empty table list, error message
-        return None, None, None, [], [], [], 0, [], final_state["error"]
+        # No Sql query, no dataframe, no figure, no plot information, empty sql queries list, empty result dataframes list, empty figures list, empty plot information list, index state = 0, empty table list, error message
+        return None, None, None, None, [], [], [], 0, [], final_state["error"]
 
     sql_query = sql_queries[index_state]
     dataframe = result_dataframes[index_state]
-    figure = figures[index_state](dataframe)  
+    figure = figures[index_state](dataframe)
+    plot_information = plot_informations[index_state] 
     
     
     log_drias_interaction_to_huggingface(query, sql_query, user_id)
 
-    return sql_query, dataframe, figure, sql_queries, result_dataframes, figures, index_state, plot_title_list, ""
+    return sql_query, dataframe, figure, plot_information, sql_queries, result_dataframes, figures, plot_informations, index_state, plot_title_list, ""
+
 
 
 async def ask_ipcc(query: str, index_state: int = 0, user_id: str | None = None) -> tuple:
@@ -91,12 +96,16 @@ async def ask_ipcc(query: str, index_state: int = 0, user_id: str | None = None)
     result_dataframes = []
     figures = []
     plot_title_list = []
-
+    plot_informations = []
 
     for output_title, output in final_state['outputs'].items():
         if output['status'] == 'OK':
             if output['table'] is not None: 
                 plot_title_list.append(output_title)
+            
+            if output['plot_information'] is not None:
+                plot_informations.append(output['plot_information'])
+
             if output['sql_query'] is not None:
                 sql_queries.append(output['sql_query'])
             
@@ -106,13 +115,14 @@ async def ask_ipcc(query: str, index_state: int = 0, user_id: str | None = None)
                     figures.append(output['figure'])
 
     if "error" in final_state and final_state["error"] != "":
-        # No Sql query, no dataframe, no figure, empty sql queries list, empty result dataframes list, empty figures list, index state = 0, empty table list, error message
-        return None, None, None, [], [], [], 0, [], final_state["error"]
+        # No Sql query, no dataframe, no figure, no plot information, empty sql queries list, empty result dataframes list, empty figures list, empty plot information list, index state = 0, empty table list, error message
+        return None, None, None, None, [], [], [], 0, [], final_state["error"]
 
     sql_query = sql_queries[index_state]
     dataframe = result_dataframes[index_state]
-    figure = figures[index_state](dataframe)    
+    figure = figures[index_state](dataframe)
+    plot_information = plot_informations[index_state]    
     
     log_drias_interaction_to_huggingface(query, sql_query, user_id)
 
-    return sql_query, dataframe, figure, sql_queries, result_dataframes, figures, index_state, plot_title_list, ""
+    return sql_query, dataframe, figure, plot_information, sql_queries, result_dataframes, figures, plot_informations, index_state, plot_title_list, ""

climateqa/engine/talk_to_data/objects/plot.py

@@ -19,4 +19,5 @@ class Plot(TypedDict):
     params: list[str]
     plot_function: Callable[..., Callable[..., Figure]]
     sql_query: Callable[..., str]
+    plot_information: Callable[..., str]
     short_name: str

climateqa/engine/talk_to_data/objects/states.py

@@ -10,6 +10,7 @@ class TTDOutput(TypedDict):
     sql_query: Optional[str]
     dataframe: Optional[pd.DataFrame]
     figure: Optional[Callable[..., Figure]]
+    plot_information: Optional[str]
 class State(TypedDict):
     user_input: str
     plots: list[str]

climateqa/engine/talk_to_data/workflow/drias.py

@@ -37,7 +37,8 @@ async def process_output(
         'table': table,
         'sql_query': None,
         'dataframe': None,
-        'figure': None
+        'figure': None,
+        'plot_information': None
     }
     errors = {
         'have_sql_query': False,
@@ -55,6 +56,8 @@ async def process_output(
         results['status'] = 'ERROR'
         return output_title, results, errors
 
+    results['plot_information'] = plot['plot_information'](table, params)
+
     results['sql_query'] = sql_query
     errors['have_sql_query'] = True
 
@@ -142,6 +145,7 @@ async def drias_workflow(user_input: str) -> State:
         outputs[output_title]['sql_query'] = task_results['sql_query']
         outputs[output_title]['dataframe'] = task_results['dataframe']
         outputs[output_title]['figure'] = task_results['figure']
+        outputs[output_title]['plot_information'] = task_results['plot_information']
         outputs[output_title]['status'] = task_results['status']
         errors['have_sql_query'] |= task_errors['have_sql_query']
         errors['have_dataframe'] |= task_errors['have_dataframe']

climateqa/engine/talk_to_data/workflow/ipcc.py

@@ -37,7 +37,8 @@ async def process_output(
         'table': table,
         'sql_query': None,
         'dataframe': None,
-        'figure': None
+        'figure': None, 
+        'plot_information': None,
     }
     errors = {
         'have_sql_query': False,
@@ -55,6 +56,8 @@ async def process_output(
         results['status'] = 'ERROR'
         return output_title, results, errors
 
+    results['plot_information'] = plot['plot_information'](table, params)
+
     results['sql_query'] = sql_query
     errors['have_sql_query'] = True
 
@@ -140,6 +143,7 @@ async def ipcc_workflow(user_input: str) -> State:
         outputs[output_title]['sql_query'] = task_results['sql_query']
         outputs[output_title]['dataframe'] = task_results['dataframe']
         outputs[output_title]['figure'] = task_results['figure']
+        outputs[output_title]['plot_information'] = task_results['plot_information']
         outputs[output_title]['status'] = task_results['status']
         errors['have_sql_query'] |= task_errors['have_sql_query']
         errors['have_dataframe'] |= task_errors['have_dataframe']

front/tabs/tab_drias.py

@@ -18,6 +18,7 @@ class DriasUIElements(TypedDict):
     query_accordion: gr.Accordion
     drias_sql_query: gr.Textbox
     chart_accordion: gr.Accordion
+    plot_information: gr.Markdown
     model_selection: gr.Dropdown
     drias_display: gr.Plot
     table_accordion: gr.Accordion
@@ -64,13 +65,14 @@ def filter_by_model(dataframes, figures, index_state, model_selection):
     return df, figure
 
 
-def on_table_click(selected_label, table_names, sql_queries, dataframes, plots):
+def on_table_click(selected_label, table_names, sql_queries, dataframes, plot_informations, plots):
     index = table_names.index(selected_label)
     figure = plots[index](dataframes[index])
     return (
         sql_queries[index],
         dataframes[index],
         figure,
+        plot_informations[index],
         index,
     )
 
@@ -132,6 +134,8 @@ def create_drias_ui() -> DriasUIElements:
 
     
         with gr.Accordion(label="Chart", visible=False) as chart_accordion:
+            with gr.Accordion(label="Informations about the plot", open=False):
+                plot_information = gr.Markdown(value = "")
             model_selection = gr.Dropdown(
                 label="Model", choices=DRIAS_MODELS, value="ALL", interactive=True
             )
@@ -154,6 +158,7 @@ def create_drias_ui() -> DriasUIElements:
             query_accordion=query_accordion,
             drias_sql_query=drias_sql_query,
             chart_accordion=chart_accordion,
+            plot_information=plot_information,
             model_selection=model_selection,
             drias_display=drias_display,
             table_accordion=table_accordion,
@@ -168,6 +173,7 @@ def setup_drias_events(ui_elements: DriasUIElements, share_client=None, user_id=
     sql_queries_state = gr.State([])
     dataframes_state = gr.State([])
     plots_state = gr.State([])
+    plot_informations_state = gr.State([])
     index_state = gr.State(0)
     table_names_list = gr.State([])
     user_id = gr.State(user_id)
@@ -188,9 +194,11 @@ def setup_drias_events(ui_elements: DriasUIElements, share_client=None, user_id=
             ui_elements["drias_sql_query"],
             ui_elements["drias_table"],
             ui_elements["drias_display"],
+            ui_elements["plot_information"],
             sql_queries_state,
             dataframes_state,
             plots_state,
+            plot_informations_state,
             index_state,
             table_names_list,
             ui_elements["result_text"],
@@ -226,6 +234,7 @@ def setup_drias_events(ui_elements: DriasUIElements, share_client=None, user_id=
             sql_queries_state,
             dataframes_state,
             plots_state,
+            plot_informations_state,
             index_state,
             table_names_list,
             ui_elements["result_text"],
@@ -252,8 +261,8 @@ def setup_drias_events(ui_elements: DriasUIElements, share_client=None, user_id=
     # Handle table selection
     ui_elements["table_names_display"].change(
         fn=on_table_click,
-        inputs=[ui_elements["table_names_display"], table_names_list, sql_queries_state, dataframes_state, plots_state],
-        outputs=[ui_elements["drias_sql_query"], ui_elements["drias_table"], ui_elements["drias_display"], index_state],
+        inputs=[ui_elements["table_names_display"], table_names_list, sql_queries_state, dataframes_state, plot_informations_state, plots_state],
+        outputs=[ui_elements["drias_sql_query"], ui_elements["drias_table"], ui_elements["drias_display"], ui_elements["plot_information"], index_state],
     )
 
 def create_drias_tab(share_client=None, user_id=None):

front/tabs/tab_ipcc.py

@@ -19,6 +19,7 @@ class ipccUIElements(TypedDict):
     query_accordion: gr.Accordion
     ipcc_sql_query: gr.Textbox
     chart_accordion: gr.Accordion
+    plot_information: gr.Markdown
     scenario_selection: gr.Dropdown
     ipcc_display: gr.Plot
     table_accordion: gr.Accordion
@@ -52,7 +53,7 @@ def show_results(sql_queries_state, dataframes_state, plots_state, table_names):
 
 
 def show_filter_by_scenario(table_names, index_state, dataframes):
-    if table_names[index_state].startswith("Choropleth Map"):
+    if len(table_names) > 0 and table_names[index_state].startswith("Choropleth Map"):
         df = dataframes[index_state]
         return gr.update(visible=True, choices=sorted(df["scenario"].unique()), value=df["scenario"].unique()[0])
     else:
@@ -78,13 +79,14 @@ def display_table_names(table_names, index_state):
         for name in table_names
     ]
 
-def on_table_click(selected_label, table_names, sql_queries, dataframes, plots):
+def on_table_click(selected_label, table_names, sql_queries, dataframes, plot_informations, plots):
     index = table_names.index(selected_label)
     figure = plots[index](dataframes[index])
     return (
         sql_queries[index],
         dataframes[index],
         figure,
+        plot_informations[index],
         index,
     )
 
@@ -144,6 +146,9 @@ def create_ipcc_ui() -> ipccUIElements:
                 )
 
         with gr.Accordion(label="Chart", visible=False) as chart_accordion:
+            with gr.Accordion(label="Informations about the plot", open=False):
+                plot_information = gr.Markdown(value = "")
+
             scenario_selection = gr.Dropdown(
                 label="Scenario", choices=IPCC_MODELS, value="ALL", interactive=True, visible=False
             )
@@ -167,6 +172,7 @@ def create_ipcc_ui() -> ipccUIElements:
             query_accordion=query_accordion,
             ipcc_sql_query=ipcc_sql_query,
             chart_accordion=chart_accordion,
+            plot_information=plot_information,
             scenario_selection=scenario_selection,
             ipcc_display=ipcc_display,
             table_accordion=table_accordion,
@@ -181,6 +187,7 @@ def setup_ipcc_events(ui_elements: ipccUIElements, share_client=None, user_id=No
     sql_queries_state = gr.State([])
     dataframes_state = gr.State([])
     plots_state = gr.State([])
+    plot_informations_state = gr.State([])
     index_state = gr.State(0)
     table_names_list = gr.State([])
     user_id = gr.State(user_id)
@@ -201,9 +208,11 @@ def setup_ipcc_events(ui_elements: ipccUIElements, share_client=None, user_id=No
             ui_elements["ipcc_sql_query"],
             ui_elements["ipcc_table"],
             ui_elements["ipcc_display"],
+            ui_elements["plot_information"],
             sql_queries_state,
             dataframes_state,
             plots_state,
+            plot_informations_state,
             index_state,
             table_names_list,
             ui_elements["result_text"],
@@ -240,9 +249,11 @@ def setup_ipcc_events(ui_elements: ipccUIElements, share_client=None, user_id=No
             ui_elements["ipcc_sql_query"],
             ui_elements["ipcc_table"],
             ui_elements["ipcc_display"],
+            ui_elements["plot_information"],
             sql_queries_state,
             dataframes_state,
             plots_state,
+            plot_informations_state,
             index_state,
             table_names_list,
             ui_elements["result_text"],
@@ -273,8 +284,8 @@ def setup_ipcc_events(ui_elements: ipccUIElements, share_client=None, user_id=No
     # Handle table selection
     ui_elements["table_names_display"].change(
         fn=on_table_click,
-        inputs=[ui_elements["table_names_display"], table_names_list, sql_queries_state, dataframes_state, plots_state],
-        outputs=[ui_elements["ipcc_sql_query"], ui_elements["ipcc_table"], ui_elements["ipcc_display"], index_state],
+        inputs=[ui_elements["table_names_display"], table_names_list, sql_queries_state, dataframes_state, plot_informations_state, plots_state],
+        outputs=[ui_elements["ipcc_sql_query"], ui_elements["ipcc_table"], ui_elements["ipcc_display"], ui_elements["plot_information"], index_state],
     ).then(
         show_filter_by_scenario,
         inputs=[table_names_list, index_state, dataframes_state],