Spaces:
Sleeping
Sleeping
File size: 4,058 Bytes
ef3d1e2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 |
import gradio as gr
from baseline.baseline_convnext import predict_convnext
from baseline.baseline_infer import predict_baseline
# --- Placeholder models (for future extensions) ---
def predict_placeholder_1(image):
if image is None:
return "Please upload an image."
return "Model 2 is not available yet. Please check back later."
def predict_placeholder_2(image):
if image is None:
return "Please upload an image."
return "Model 3 is not available yet. Please check back later."
# --- Main Prediction Logic ---
def predict(model_choice, image):
if model_choice == "Herbarium Species Classifier":
# Friend's ConvNeXt mix-stream CNN baseline
return predict_convnext(image)
elif model_choice == "Baseline (DINOv2 + LogReg)":
# Your plant-pretrained DINOv2 + Logistic Regression baseline
return predict_baseline(image)
elif model_choice == "Future Model 1 (Placeholder)":
return predict_placeholder_1(image)
elif model_choice == "Future Model 2 (Placeholder)":
return predict_placeholder_2(image)
else:
return "Invalid model selected."
# --- Gradio Interface ---
with gr.Blocks(theme=gr.themes.Soft(), css="style.css") as demo:
with gr.Column(elem_id="app-wrapper"):
# Header
gr.Markdown(
"""
<div id="app-header">
<h1>🌿 Plant Species Classification</h1>
<h3>AML Group Project – PsychicFireSong</h3>
</div>
""",
elem_id="app-header",
)
# Badges row
gr.Markdown(
"""
<div id="badge-row">
<span class="badge">Herbarium + Field images</span>
<span class="badge">ConvNeXtV2 mix-stream CNN</span>
<span class="badge">DINOv2 + Logistic Regression</span>
</div>
""",
elem_id="badge-row",
)
# Main card
with gr.Row(elem_id="main-card"):
# Left side: model + image
with gr.Column(scale=1, elem_id="left-panel"):
model_selector = gr.Dropdown(
label="Select model",
choices=[
"Herbarium Species Classifier",
"Baseline (DINOv2 + LogReg)",
"Future Model 1 (Placeholder)",
"Future Model 2 (Placeholder)",
],
value="Herbarium Species Classifier",
)
gr.Markdown(
"""
<div id="model-help">
<b>Herbarium Species Classifier</b> – end-to-end ConvNeXtV2 CNN.<br>
<b>Baseline</b> – plant-pretrained DINOv2 features + logistic regression head.
</div>
""",
elem_id="model-help",
)
image_input = gr.Image(
type="pil",
label="Upload plant image",
)
submit_button = gr.Button("Classify 🌱", variant="primary")
# Right side: predictions
with gr.Column(scale=1, elem_id="right-panel"):
output_label = gr.Label(
label="Top 5 predictions",
num_top_classes=5,
)
submit_button.click(
fn=predict,
inputs=[model_selector, image_input],
outputs=output_label,
)
# Optional examples (keep empty if you don't have images)
gr.Examples(
examples=[],
inputs=image_input,
outputs=output_label,
fn=lambda img: predict("Herbarium Species Classifier", img),
cache_examples=False,
)
gr.Markdown(
"Built for the AML course – compare CNN vs. DINOv2 feature-extractor baselines.",
elem_id="footer",
)
if __name__ == "__main__":
demo.launch()
|