nounouille commited on
Commit
5ae7114
·
1 Parent(s): a0ce4c5

� Revert : retour à version stable sans export CSV

Browse files
Files changed (1) hide show
  1. app.py +0 -24
app.py CHANGED
@@ -40,12 +40,8 @@ def generate_dummy_legend():
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  return img
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- # legend_img = Image.new("RGB", (300, 50), color=(128, 128, 128))
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  legend_img = generate_dummy_legend()
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  history = deque(maxlen=5)
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- # history_csv = []
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-
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-
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  def overlay_mask_on_image(image: Image.Image, mask: Image.Image, alpha: float = 0.5) -> Image.Image:
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  image = image.convert("RGBA").resize(mask.size)
@@ -69,12 +65,6 @@ def segment_image(image: Image.Image, source_name="image_upload.png"):
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  mask_image = Image.open(io.BytesIO(mask_bytes)).convert("RGB")
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  overlay_img = overlay_mask_on_image(image, mask_image)
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- # # Ajout dans l'historique CSV
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- # history_csv.append({
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- # "filename": source_name,
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- # "inference_time": round(float(data["inference_time"]), 3)
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- # })
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-
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  return image, mask_image, overlay_img, f"{data['inference_time']} sec"
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  except RequestException as e:
@@ -104,16 +94,6 @@ def load_image_from_url(filename):
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  print(f"[ERREUR CHARGEMENT IMAGE {filename}] {e}")
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  return None
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- # def export_csv():
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- # if not history_csv:
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- # return gr.File.update(value=None, visible=False)
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-
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- # df = pd.DataFrame(history_csv)
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- # tmp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".csv", mode='w', encoding='utf-8')
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- # df.to_csv(tmp_file.name, index=False)
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- # tmp_file.close()
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-
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- # return gr.File.update(value=tmp_file.name, visible=True, label="📥 Résultats CSV")
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  with gr.Blocks(title="Segmentation d'Images Urbaines") as demo:
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  gr.Markdown("# 🧠 Segmentation d'Images Urbaines")
@@ -137,10 +117,6 @@ with gr.Blocks(title="Segmentation d'Images Urbaines") as demo:
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  btn_url.click(fn=lambda name: segment_image(load_image_from_url(name), source_name=name),
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  inputs=dropdown, outputs=[img_original, img_mask, img_overlay, inf_time])
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- # with gr.Row():
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- # btn_export = gr.Button("📤 Exporter CSV des inférences")
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- # file_output = gr.File(visible=False)
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- # btn_export.click(fn=export_csv, inputs=[], outputs=file_output)
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  with gr.Accordion("🎨 Légende des classes", open=False):
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  gr.Image(value=legend_img, label="Légende (fictive ici)", interactive=False)
 
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  return img
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  legend_img = generate_dummy_legend()
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  history = deque(maxlen=5)
 
 
 
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  def overlay_mask_on_image(image: Image.Image, mask: Image.Image, alpha: float = 0.5) -> Image.Image:
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  image = image.convert("RGBA").resize(mask.size)
 
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  mask_image = Image.open(io.BytesIO(mask_bytes)).convert("RGB")
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  overlay_img = overlay_mask_on_image(image, mask_image)
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  return image, mask_image, overlay_img, f"{data['inference_time']} sec"
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  except RequestException as e:
 
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  print(f"[ERREUR CHARGEMENT IMAGE {filename}] {e}")
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  return None
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  with gr.Blocks(title="Segmentation d'Images Urbaines") as demo:
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  gr.Markdown("# 🧠 Segmentation d'Images Urbaines")
 
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  btn_url.click(fn=lambda name: segment_image(load_image_from_url(name), source_name=name),
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  inputs=dropdown, outputs=[img_original, img_mask, img_overlay, inf_time])
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  with gr.Accordion("🎨 Légende des classes", open=False):
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  gr.Image(value=legend_img, label="Légende (fictive ici)", interactive=False)