Remove KNN classification map from results

app.py

@@ -3,7 +3,6 @@ import numpy as np
 import matplotlib
 matplotlib.use('Agg')
 import matplotlib.pyplot as plt
-import matplotlib.patches as mpatches
 import rasterio
 import json
 import os
@@ -25,18 +24,6 @@ CLASSES = {
 
 CLASS_CHOICES = [f"{k} - {v}" for k, v in CLASSES.items()]
 
-# Distinctive color palette per class (index 0 = background)
-COLORS_RGB = np.array([
-    [20,  20,  20 ],   # 0 background
-    [0,   100, 220],   # 1 eau
-    [0,   160,  60],   # 2 vergers
-    [120, 220, 100],   # 3 cultures delta
-    [220,  50,  50],   # 4 zones bâties
-    [255, 165,   0],   # 5 cultures irriguées désert
-    [160,  90,  30],   # 6 cultures non irriguées
-    [240, 230, 140],   # 7 zones sableuses
-], dtype=np.uint8)
-
 BASE_DIR = os.path.dirname(os.path.abspath(__file__))
 
 # ─────────────────────────────────────────────────────────────────────────────
@@ -48,10 +35,8 @@ def load_data():
         with rasterio.open(path) as src:
             return src.read(1)
 
-    training_ids  = read_tif('training_polygons.tif')
     ground_truth  = read_tif('ground_truth.tif')
     knn_result    = read_tif('knn_result.tif')
-    teacher_lbl   = read_tif('teacher_labels.tif')
 
     with open(os.path.join(BASE_DIR, 'data', 'polygon_teacher_classes.json')) as f:
         polygon_teacher = {int(k): v for k, v in json.load(f).items()}
@@ -64,25 +49,10 @@ def load_data():
     np.add.at(knn_matrix, (gt_flat[valid] - 1, knn_flat[valid] - 1), 1)
     knn_oa = knn_matrix.diagonal().sum() / knn_matrix.sum()
 
-    # Downsample rasters to ~500 px height for display
-    from PIL import Image as PILImage
-    h, w = training_ids.shape
-    new_h, new_w = 500, int(w * 500 / h)
-
-    def small(arr):
-        return np.array(PILImage.fromarray(arr).resize((new_w, new_h), PILImage.NEAREST))
-
     return dict(
-        training_ids   = training_ids,
-        ground_truth   = ground_truth,
-        knn_result     = knn_result,
-        teacher_lbl    = teacher_lbl,
         polygon_teacher= polygon_teacher,
         knn_matrix     = knn_matrix,
         knn_oa         = knn_oa,
-        tr_small       = small(training_ids),
-        gt_small       = small(ground_truth),
-        knn_small      = small(knn_result),
     )
 
 DATA = load_data()
@@ -90,38 +60,6 @@ DATA = load_data()
 # ─────────────────────────────────────────────────────────────────────────────
 # Visualization helpers
 # ─────────────────────────────────────────────────────────────────────────────
-def raster_to_rgb(arr):
-    rgb = COLORS_RGB[arr]
-    return rgb
-
-def legend_patches():
-    return [mpatches.Patch(color=COLORS_RGB[i]/255, label=f"{i} – {CLASSES[i]}")
-            for i in range(1, 8)]
-
-def fig_maps(student_labels):
-    fig, axes = plt.subplots(1, 3, figsize=(18, 6))
-    fig.patch.set_facecolor('#f5f5f5')
-
-    student_map = np.zeros_like(DATA['tr_small'])
-    for pid, label in enumerate(student_labels, 1):
-        if label is not None:
-            student_map[DATA['tr_small'] == pid] = label
-
-    titles = [
-        "Votre interprétation\n(polygones d'entraînement)",
-        "Classification KNN\n(carte produite par l'IA)",
-        "Vérité terrain\n(zones de validation)",
-    ]
-    rasters = [student_map, DATA['knn_small'], DATA['gt_small']]
-    for ax, rast, title in zip(axes, rasters, titles):
-        ax.imshow(raster_to_rgb(rast), interpolation='nearest')
-        ax.set_title(title, fontsize=11, fontweight='bold', pad=8)
-        ax.axis('off')
-
-    fig.legend(handles=legend_patches(), loc='lower center', ncol=4,
-               bbox_to_anchor=(0.5, -0.06), fontsize=9, framealpha=0.9)
-    plt.tight_layout()
-    return fig
 
 def fig_knn_matrix():
     matrix = DATA['knn_matrix']
@@ -305,7 +243,6 @@ puis soumettez vos réponses pour générer la carte et la matrice de confusion.
             with gr.Row(visible=False) as row_plots_student:
                 student_matrix_plot = gr.Plot(label="Votre interprétation vs Enseignant")
 
-            maps_plot    = gr.Plot(label="Cartes", visible=False)
             knn_plot     = gr.Plot(label="Matrice de confusion KNN vs Vérité terrain", visible=False)
 
     # ─────────────────────────────────────────────────────────────────────
@@ -369,17 +306,15 @@ puis soumettez vos réponses pour générer la carte et la matrice de confusion.
 
         table = build_results_table(current_labels)
 
-        maps_fig    = fig_maps(current_labels)
         knn_fig     = fig_knn_matrix()
         student_fig = fig_student_matrix(current_labels)
 
         return (
-            gr.update(value=""),                          # hide placeholder
+            gr.update(value=""),
             gr.update(value=acc_text,   visible=True),
             gr.update(value=table,      visible=True),
             gr.update(visible=True),
             gr.update(value=student_fig),
-            gr.update(value=maps_fig,   visible=True),
             gr.update(value=knn_fig,    visible=True),
         )
 
@@ -412,7 +347,6 @@ puis soumettez vos réponses pour générer la carte et la matrice de confusion.
             results_tbl,
             row_plots_student,
             student_matrix_plot,
-            maps_plot,
             knn_plot,
         ],
     )