added separation between infos and results in two pages

.gitignore

@@ -1,2 +1,3 @@
 .env
 __pycache__/
+.venv/

app.py

@@ -46,78 +46,157 @@ description = "Example of simple chatbot with Gradio and Mistral AI via its API"
 
 # import gradio as gr
 from func_utils import *
+from summary_test import generate_irradiance_trend, get_mocked_summary
 
-with gr.Blocks() as demo:
-    gr.HTML(
-        """
-        <style>
-            /* Custom style for a specific row */
-            .box {
-                background-color: #90909b;
-                # padding: 20px;
-                border-radius: 10px;
-                # border: solid 2px #4CAF50;
-                display: flex;
-                align-content: center;
-                padding: 20px;
-            }
-            .culture_box {
-                background-color: #52525b;
-                border-radius: 10px;
-                display: flex;
-                align-content: center;
-            }
-        </style>
-        """
-    )
-    demo.title = "Démo GAIA - Les bénéfices de l'ombrage"
-    gr.HTML("<h1 style='text-align: center;'>Les bénéfices de l'ombrage</h1>")
-    gr.HTML(
-        "<p style='border: solid white 1px; border-radius: 10px; padding:20px'>L'outil vous permet de voir les avantages potentiels de l'ombrage sur votre exploitation. </p>"
-    )
-
-    with gr.Blocks() as infos:
+def go_to_page_1():
+    return gr.Column(visible=True), gr.Column(visible=False)
 
-        infos.title = "Informations sur votre exploitation"
-        gr.HTML("<h2>Renseignez les informations relatives à votre projet</h2>")
-        with gr.Row(equal_height=True):
-            with gr.Column(variant="panel", scale=1):
+with gr.Blocks() as demo:
+    with gr.Row():
+        page_1 = gr.Column(visible=True)
+        with page_1:
+            gr.HTML(
+                """
+                <style>
+                    /* Custom style for a specific row */
+                    .box {
+                        background-color: #90909b;
+                        border-radius: 10px;
+                        display: flex;
+                        align-content: center;
+                        padding: 20px;
+                    }
+                    .culture_box {
+                        background-color: #52525b;
+                        border-radius: 10px;
+                        display: flex;
+                        align-content: center;
+                    }
+                    .title-box{
+                        background-color: #90909b;
+                    }
+                </style>
+                """
+            )
+            demo.title = "Démo GAIA - Les bénéfices de l'ombrage"
+            gr.HTML("<h1 style='text-align: center;'>Les bénéfices de l'ombrage</h1>")
+            gr.HTML(
+                "<p style='border: solid white 1px; border-radius: 10px; padding:20px'>L'outil vous permet de voir les avantages potentiels de l'ombrage sur votre exploitation. </p>"
+            )
+
+            with gr.Blocks() as infos:
+
+                infos.title = "Informations sur votre exploitation"
+                gr.HTML("<h2>Renseignez les informations relatives à votre projet</h2>")
+                with gr.Row(equal_height=True):
+                    with gr.Column(variant="panel", scale=1):
+                        with gr.Row(equal_height=True, elem_classes="box"):
+                            with gr.Tab(label="Adresse", scale=1):
+                                address = gr.Textbox(
+                                    label="Addresse",
+                                    info="Adresse de votre projet",
+                                )
+                            with gr.Tab(label="Coordonnées GPS", scale=1):
+                                lat = gr.Number(
+                                    label="Latitude",
+                                    info="Latitude de votre projet",
+                                )
+                                lon = gr.Number(
+                                    label="Longitude",
+                                    info="Longitude de votre projet",
+                                )
+                            place_btn = gr.Button(
+                                value="Valider la localisation", size="sm"
+                            )
+                            place_cancel_btn = gr.Button(
+                                value="Réinitialiser la localisation", size="sm"
+                            )
+
+                        with gr.Row(elem_classes="box"):
+                            culture = gr.Textbox(
+                                label="Culture", scale=1, elem_classes="culture_box"
+                            )
+
+                    with gr.Column(variant="panel", scale=3):
+                        map = gr.HTML()
+
+                simulation_btn = gr.Button(value="Lancer la simulation", size="lg")
+
+            go_to_page_2_btn = gr.Button("Aller aux résultats", visible=False)
+
+        page_2 = gr.Column(visible=False)
+        with page_2:
+            with gr.Blocks() as results:
+                results.title = "Résultats"
+                gr.HTML("<h2 style='padding: 20px'>Résultats de la simulation</h2>")
                 with gr.Row(equal_height=True, elem_classes="box"):
-                    with gr.Tab(label="Adresse", scale=1):
-                        address = gr.Textbox(
-                            label="Addresse",
-                            info="Adresse de votre projet",
-                        )
-                    with gr.Tab(label="Coordonnées GPS", scale=1):
-                        lat = gr.Number(
-                            label="Latitude",
-                            info="Latitude de votre projet",
-                        )
-                        lon = gr.Number(
-                            label="Longitude",
-                            info="Longitude de votre projet",
-                        )
-                    place_btn = gr.Button(value="Valider la localisation", size="sm")
-                    place_cancel_btn = gr.Button(
-                        value="Réinitialiser la localisation", size="sm"
-                    )
-
-                with gr.Row(elem_classes="box"):
-                    culture = gr.Textbox(
-                        label="Culture", scale=1, elem_classes="culture_box"
-                    )
-
-            with gr.Column(variant="panel", scale=3):
-                map = gr.HTML()
-            
-        simulation_btn = gr.Button(value="Lancer la simulation", size="lg")
+                    with gr.Tab(label="Analyse générale", scale=1):
+                        with gr.Row(elem_classes="box"):
+                            with gr.Column():
+                                gr.HTML("<h2>Synthèse</h2>")
+                                current_situation_summary = gr.TextArea(
+                                    placeholder="Synthèse de la simulation", label="", show_label=None
+                                )
+                        with gr.Row(elem_classes="box"):
+                            with gr.Column():
+                                gr.HTML("<h2>Déficit hydrique</h2>")
+                                gr.Plot()
+                            with gr.Column():
+                                gr.HTML("<h2>Rendements</h2>")
+                                gr.Plot()
+                        with gr.Column(elem_classes="box"):
+                            with gr.Row():
+                                gr.HTML("<h2>Bilan climatique</h2>")
+                            with gr.Row():
+                                with gr.Column():
+                                    gr.HTML("<h3>Précipitations</h2>")
+                                    gr.Plot()
+                                with gr.Column():
+                                    gr.HTML("<h3>Evapotranspiration</h2>")
+                                    gr.Plot()
+                                with gr.Column():
+                                    gr.HTML("<h3>Irradiance</h2>")
+                                    gr.Plot()
+                    with gr.Tab(label="Analyse avec AgriPv", scale=1):
+                        with gr.Row(elem_classes="box"):
+                            with gr.Column():
+                                gr.HTML("<h2>Synthèse</h2>")
+                                agripv_summary = gr.TextArea(
+                                    placeholder="Synthèse de la simulation", label="", show_label=None
+                                )
+                        with gr.Row(elem_classes="box"):
+                            with gr.Column():
+                                gr.HTML("<h2>Déficit hydrique</h2>")
+                                gr.Plot()
+                            with gr.Column():
+                                gr.HTML("<h2>Rendements</h2>")
+                                gr.Plot()
+                go_to_page_1_btn = gr.Button(value="Revenir aux informations du projet", size="lg")
 
     demo.load(on_init, [lat, lon, address], [lat, lon, map])
     place_btn.click(on_init, [lat, lon, address], [lat, lon, map])
     place_cancel_btn.click(on_delete, [lat, lon, map], [lat, lon, address, map])
+    go_to_page_2_btn.click(
+        fn=go_to_page_2,
+        inputs="",
+        outputs=[page_1, page_2],
+    )
+    go_to_page_1_btn.click(
+        fn=go_to_page_1,
+        inputs="",
+        outputs=[page_1, page_2],
+    )
+
     simulation_btn.click(
-        launch_simulation, [lat, lon, address, culture], [lat, lon, address, map]
+        launch_simulation,
+        [lat, lon, address, culture],
+        [
+            current_situation_summary,
+            agripv_summary,
+            page_1,
+            page_2,
+            go_to_page_2_btn
+        ],
     )
 demo.title = "Démo GAIA - Les bénéfices de l'ombrage"
-# demo.description = "Example of simple chatbot with Gradio and Mistral AI via its API"
 demo.launch()

func_utils.py

@@ -1,8 +1,11 @@
 import folium
 import requests
+import gradio as gr
 
+from summary_test import generate_irradiance_trend, get_mocked_summary
 
-# https://huggingface.co/spaces/gaia-mistral/pest-livestock-information
+
+# code from https://huggingface.co/spaces/gaia-mistral/pest-livestock-information
 def get_geolocation(adresse, latitude, longitude):
     """Return latitude, longitude & code INSEE from an adress. Latitude & longitude only if they are not specified in the function.
     Args:
@@ -29,6 +32,7 @@ def get_geolocation(adresse, latitude, longitude):
     return None, None, None
 
 
+# code from https://huggingface.co/spaces/gaia-mistral/pest-livestock-information
 def on_init(lat, lon, address):
     map_html, lat, lon = show_map(lat, lon, address)
     return lat, lon, map_html
@@ -40,6 +44,7 @@ def on_delete(lat, lon, address):
     return lat, lon, address, map_html
 
 
+# code from https://huggingface.co/spaces/gaia-mistral/pest-livestock-information
 def show_map(lat, lon, address):
     if address:
         lat_tmp, lon_tmp, code_insee = get_geolocation(address, None, None)
@@ -57,6 +62,20 @@ def show_map(lat, lon, address):
     return map_html, lat, lon
 
 
+def go_to_page_2():
+    return gr.Column(visible=False), gr.Column(visible=True)
+
+
 def launch_simulation(lat, lon, address, culture):
-    # Do something
-    pass
+    # current_situation_summary = get_mocked_summary("pessimiste")
+    # agripv_summary = get_mocked_summary("modéré")
+    current_situation_summary = "truc"
+    agripv_summary = "bicule"
+    page1, page_2 = go_to_page_2()
+    return (
+        current_situation_summary,
+        agripv_summary,
+        page1,
+        page_2,
+        gr.Button(visible=True),
+    )

requirements.txt

@@ -21,4 +21,4 @@ pvlib
 matplotlib
 xarray
 folium
-netcdf4
+netcdf4

summary_test.py

@@ -3,13 +3,21 @@ import pandas as pd
 import numpy as np
 
 from utils.summary import get_meterological_summary, get_agricultural_yield_comparison
+
 # Générer des dates sur 5 ans (historique) + 5 ans (prévision)
-dates_past = pd.date_range(start="2023-01-01", periods=36, freq='ME')  # 3 ans d'historique
-dates_future = pd.date_range(start="2023-01-01", periods=60, freq='ME')  # 5 ans de prévisions
+dates_past = pd.date_range(
+    start="2023-01-01", periods=36, freq="ME"
+)  # 3 ans d'historique
+dates_future = pd.date_range(
+    start="2023-01-01", periods=60, freq="ME"
+)  # 5 ans de prévisions
+
 
 # Température: Tendance à la hausse selon le scénario
 def generate_temperature_trend(scenario):
-    base_temp = 10 + 10 * np.sin(np.linspace(0, 2 * np.pi, len(dates_past) + len(dates_future)))
+    base_temp = 10 + 10 * np.sin(
+        np.linspace(0, 2 * np.pi, len(dates_past) + len(dates_future))
+    )
     if scenario == "optimiste":
         trend = base_temp + np.linspace(0, 1, len(base_temp))  # Faible réchauffement
     elif scenario == "modéré":
@@ -18,9 +26,12 @@ def generate_temperature_trend(scenario):
         trend = base_temp + np.linspace(0, 3, len(base_temp))  # Fort réchauffement
     return trend
 
+
 # Précipitations: Variation selon le scénario
 def generate_precipitation_trend(scenario):
-    base_rain = 50 + 20 * np.cos(np.linspace(0, 2 * np.pi, len(dates_past) + len(dates_future)))
+    base_rain = 50 + 20 * np.cos(
+        np.linspace(0, 2 * np.pi, len(dates_past) + len(dates_future))
+    )
     if scenario == "optimiste":
         trend = base_rain - np.linspace(0, 5, len(base_rain))  # Légère baisse
     elif scenario == "modéré":
@@ -29,37 +40,53 @@ def generate_precipitation_trend(scenario):
         trend = base_rain - np.linspace(0, 15, len(base_rain))  # Forte baisse
     return trend
 
+
 # Irradiance: Augmentation progressive
 def generate_irradiance_trend(scenario):
-    base_irradiance = 200 + 50 * np.sin(np.linspace(0, 2 * np.pi, len(dates_past) + len(dates_future)))
+    base_irradiance = 200 + 50 * np.sin(
+        np.linspace(0, 2 * np.pi, len(dates_past) + len(dates_future))
+    )
     if scenario == "optimiste":
-        trend = base_irradiance + np.linspace(0, 5, len(base_irradiance))  # Faible augmentation
+        trend = base_irradiance + np.linspace(
+            0, 5, len(base_irradiance)
+        )  # Faible augmentation
     elif scenario == "modéré":
-        trend = base_irradiance + np.linspace(0, 10, len(base_irradiance))  # Augmentation modérée
+        trend = base_irradiance + np.linspace(
+            0, 10, len(base_irradiance)
+        )  # Augmentation modérée
     else:  # pessimiste
-        trend = base_irradiance + np.linspace(0, 20, len(base_irradiance))  # Forte augmentation
+        trend = base_irradiance + np.linspace(
+            0, 20, len(base_irradiance)
+        )  # Forte augmentation
     return trend
 
-# Choix du scénario
-scenario = "modéré"  # Changer entre "optimiste", "modéré" et "pessimiste"
 
-# Créer les DataFrames
-temperature_df = pd.DataFrame({"Date": dates_past.tolist() + dates_future.tolist(), 
-                               "Température (°C)": generate_temperature_trend(scenario)})
+def get_mocked_summary(scenario):
+    # Choix du scénario
+    # scenario = "modéré"  # Changer entre "optimiste", "modéré" et "pessimiste"
 
-rain_df = pd.DataFrame({"Date": dates_past.tolist() + dates_future.tolist(), 
-                        "Précipitations (mm)": generate_precipitation_trend(scenario)})
+    # Créer les DataFrames
+    temperature_df = pd.DataFrame(
+        {
+            "Date": dates_past.tolist() + dates_future.tolist(),
+            "Température (°C)": generate_temperature_trend(scenario),
+        }
+    )
 
-irradiation_df = pd.DataFrame({"Date": dates_past.tolist() + dates_future.tolist(), 
-                               "Irradiance (W/m²)": generate_irradiance_trend(scenario)})
+    rain_df = pd.DataFrame(
+        {
+            "Date": dates_past.tolist() + dates_future.tolist(),
+            "Précipitations (mm)": generate_precipitation_trend(scenario),
+        }
+    )
+
+    irradiation_df = pd.DataFrame(
+        {
+            "Date": dates_past.tolist() + dates_future.tolist(),
+            "Irradiance (W/m²)": generate_irradiance_trend(scenario),
+        }
+    )
 
-# Afficher un extrait
-print("Température (extrait) :")
-print(temperature_df.head(3))
-print("\nPrécipitations (extrait) :")
-print(rain_df.head(3))
-print("\nIrradiance (extrait) :")
-print(irradiation_df.head(3))
 
 if __name__ == "__main__":
     # summary = get_meterological_summary(scenario, temperature_df, rain_df, irradiation_df)
@@ -70,6 +97,7 @@ if __name__ == "__main__":
     import numpy as np
 
     from utils.soil_utils import find_nearest_point
+
     city = "Bourgogne Franche Comté"
     closest_soil_features = find_nearest_point(city)
     print(closest_soil_features)
@@ -79,7 +107,7 @@ if __name__ == "__main__":
     end_date = "2029-12"
 
     # Générer une série de dates mensuelles
-    dates = pd.date_range(start=start_date, end=end_date, freq='M')
+    dates = pd.date_range(start=start_date, end=end_date, freq="M")
 
     # Générer des données fictives de rendement (en tonnes par hectare)
     np.random.seed(42)  # Pour reproductibilité
@@ -88,28 +116,37 @@ if __name__ == "__main__":
     trend = np.linspace(2.5, 3.2, len(dates))  # Augmente légèrement sur les années
 
     # Ajout de variations saisonnières et aléatoires
-    seasonality = 0.3 * np.sin(np.linspace(0, 12 * np.pi, len(dates)))  # Effet saisonnier
+    seasonality = 0.3 * np.sin(
+        np.linspace(0, 12 * np.pi, len(dates))
+    )  # Effet saisonnier
     random_variation = np.random.normal(0, 0.1, len(dates))  # Bruit aléatoire
 
     # Calcul du rendement sans ombrage
     yield_no_shade = trend + seasonality + random_variation
 
     # Appliquer un effet d'ombrage (réduction de 10-20% du rendement)
-    shade_factor = np.random.uniform(0.1, 0.2, len(dates))  # Entre 10% et 20% de réduction
+    shade_factor = np.random.uniform(
+        0.1, 0.2, len(dates)
+    )  # Entre 10% et 20% de réduction
     yield_with_shade = yield_no_shade * (1 - shade_factor)
 
     # Créer le DataFrame
-    df = pd.DataFrame({
-        "date": dates,
-        "yield_no_shade": yield_no_shade,
-        "yield_with_shade": yield_with_shade
-    })
+    df = pd.DataFrame(
+        {
+            "date": dates,
+            "yield_no_shade": yield_no_shade,
+            "yield_with_shade": yield_with_shade,
+        }
+    )
     water_deficit_data = pd.DataFrame()
     climate_data = pd.DataFrame()
-    
-    print(get_agricultural_yield_comparison(culture="orge", 
-                                            region="bourgogne franche comté",
-                                            water_df=water_deficit_data, 
-                                            climate_df=climate_data,
-                                            soil_df=closest_soil_features, 
-                                            agri_yield_df=df))
+
+    summary = get_agricultural_yield_comparison(
+        culture="orge",
+        region="bourgogne franche comté",
+        water_df=water_deficit_data,
+        climate_df=climate_data,
+        soil_df=closest_soil_features,
+        agri_yield_df=df,
+    )
+    print(summary)