reverse app.py to stable version

app.py

@@ -13,8 +13,6 @@ import matplotlib.pyplot as plt
 from transformers import AutoModelForMaskGeneration, AutoProcessor, pipeline
 import gradio as gr
 import spaces
-from plotly import graph_objects as go
-import plotly.express as px
 
 @dataclass
 class BoundingBox:
@@ -186,52 +184,17 @@ def draw_classification_boxes(image_with_insects, detections):
         )
     return image_with_insects
 
-def plot_detections_plotly(image: np.ndarray, detections: List[DetectionResult]) -> str:
-    fig = px.imshow(image)
-    class_colors = {i: f'rgb({random.randint(0, 255)}, {random.randint(0, 255)}, {random.randint(0, 255)})' for i in range(len(detections))}
-    for idx, detection in enumerate(detections):
-        label = detection.label
-        box = detection.box
-        score = detection.score
-        mask = detection.mask
-        polygon = mask_to_polygon(mask)
-        fig.add_trace(go.Scatter(
-            x=[point[0] for point in polygon] + [polygon[0][0]],
-            y=[point[1] for point in polygon] + [polygon[0][1]],
-            mode='lines',
-            line=dict(color=class_colors[idx], width=2),
-            fill='toself',
-            name=f"{label}: {score:.2f}"
-        ))
-        xmin, ymin, xmax, ymax = box.xyxy
-        fig.add_shape(
-            type="rect",
-            x0=xmin, y0=ymin, x1=xmax, y1=ymax,
-            line=dict(color=class_colors[idx])
-        )
-        fig.add_annotation(
-            x=(xmin + xmax) // 2, y=(ymin + ymax) // 2,
-            text=f"{label}: {score:.2f}",
-        )
-    fig.update_layout(xaxis=dict(visible=False), yaxis=dict(visible=False))
-    file_path = "/tmp/plotly_image.html"
-    fig.write_html(file_path)
-    return file_path
-
 def process_image(image):
     labels = ["insect"]
     original_image, detections = grounded_segmentation(image, labels, threshold=0.3, polygon_refinement=True)
     annotated_image = plot_detections(original_image, detections)
     yellow_background_with_insects = create_yellow_background_with_insects(np.array(original_image), detections)
     yellow_background_with_boxes = draw_classification_boxes(yellow_background_with_insects.copy(), detections)
-    plotly_image_path = plot_detections_plotly(original_image, detections)
-    with open(plotly_image_path, 'r') as file:
-        plotly_html = file.read()
-    return annotated_image, yellow_background_with_boxes, plotly_html
+    return annotated_image, yellow_background_with_boxes
 
 gr.Interface(
     fn=process_image,
     inputs=gr.Image(type="pil"),
-    outputs=[gr.Image(type="numpy"), gr.Image(type="numpy"), gr.HTML()],
-    title="🐞 InsectSAM + GroundingDINO Inference"
-).launch()
+    outputs=[gr.Image(type="numpy"), gr.Image(type="numpy")],
+    title="🐞 InsectSAM + GroundingDINO Inference",
+).launch()