Upload app.py and requirements files

app.py

@@ -0,0 +1,109 @@
+import gradio as gr
+import os
+import numpy as np
+import pandas as pd
+import torch
+import timm
+from PIL import Image
+from torchvision import transforms
+import gradio
+import warnings
+warnings.filterwarnings("ignore")
+
+model = timm.create_model('swinv2_cr_tiny_ns_224.sw_in1k', pretrained=True)
+output_shape = 60
+model.classifier = torch.nn.Sequential(
+    torch.nn.Dropout(p=0.2, inplace=True),
+    torch.nn.Linear(in_features=1000,
+                   out_features=output_shape,
+                   bias=True).to('cpu'))
+model.load_state_dict(torch.load('./swin_70_65.pth', map_location=torch.device('cpu')))
+
+preprocess = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
+class_names = [
+    'Ahaetulla_prasina', 'Bitis_arietans', 'Boa_constrictor', 'Boa_imperator',
+    'Bothriechis_schlegelii', 'Bothrops_asper', 'Bothrops_atrox', 'Bungarus_fasciatus',
+    'Chrysopelea_ornata', 'Coelognathus_radiatus', 'Corallus_hortulana', 'Coronella_austriaca',
+    'Crotaphopeltis_hotamboeia', 'Dendrelaphis_pictus', 'Dolichophis_caspius', 'Drymarchon_melanurus',
+    'Drymobius_margaritiferus', 'Elaphe_dione', 'Epicrates_cenchria', 'Erythrolamprus_poecilogyrus',
+    'Eunectes_murinus', 'Fowlea_flavipunctata', 'Gonyosoma_oxycephalum', 'Helicops_angulatus',
+    'Hierophis_viridiflavus', 'Imantodes_cenchoa', 'Indotyphlops_braminus', 'Laticauda_colubrina',
+    'Leptodeira_annulata', 'Leptodeira_ornata', 'Leptodeira_septentrionalis', 'Leptophis_ahaetulla',
+    'Leptophis_mexicanus', 'Lycodon_capucinus', 'Malayopython_reticulatus', 'Malpolon_insignitus',
+    'Mastigodryas_boddaerti', 'Natrix_helvetica', 'Natrix_maura', 'Natrix_natrix', 'Natrix_tessellata',
+    'Ninia_sebae', 'Ophiophagus_hannah', 'Oxybelis_aeneus', 'Oxybelis_fulgidus', 'Oxyrhopus_petolarius',
+    'Phrynonax_poecilonotus', 'Psammodynastes_pulverulentus', 'Ptyas_korros', 'Ptyas_mucosa',
+    'Python_bivittatus', 'Rhabdophis_tigrinus', 'Sibon_nebulatus', 'Spilotes_pullatus',
+    'Tantilla_melanocephala', 'Trimeresurus_albolabris', 'Vipera_ammodytes', 'Vipera_aspis',
+    'Vipera_berus', 'Zamenis_longissimus'
+]
+
+
+def predict(image):
+    if image is None:
+        return "No image provided."
+
+    try:
+        input_tensor = preprocess(image)
+    except Exception as e:
+        return f"Error in preprocessing: {str(e)}"
+
+    input_batch = input_tensor.unsqueeze(0).to('cpu')
+
+    try:
+        with torch.no_grad():
+            output = model(input_batch)
+    except Exception as e:
+        return f"Error in model inference: {str(e)}"
+
+    probabilities = torch.nn.functional.softmax(output, dim=1)
+    percentages = probabilities[0].cpu().numpy() * 100
+
+    top_n = 5
+    combined = list(zip(class_names, percentages))
+    sorted_combined = sorted(combined, key=lambda x: x[1], reverse=True)
+    top_predictions = sorted_combined[:top_n]
+    
+    # Generate HTML for progress bars with numbers above
+    html_content = "<div style='font-family: Arial, sans-serif;'>"
+    for class_label, confidence in top_predictions:
+        html_content += f"""
+        <div style='margin-bottom: 10px; position: relative;'>
+            <div style='display: flex; align-items: center;'>
+                <strong style='flex: 1;'>{class_label}</strong>
+                <span style='flex-shrink: 0; color: black; margin-left: 10px;'>
+                    {confidence:.2f}%
+                </span>
+            </div>
+            <div style='background-color: #f3f3f3; border-radius: 5px; width: 100%; height: 20px; margin-top: 5px;'>
+                <div style='background-color: #4CAF50; height: 100%; width: {confidence:.2f}%; border-radius: 5px;'></div>
+            </div>
+        </div>
+        """
+    html_content += "</div>"
+    
+    return html_content
+
+
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type='pil'),
+    outputs=gr.HTML(),
+    title="Snake Species Classification (SnakeCLEF2024)",
+    description =  """
+    <div style='font-family: Arial, sans-serif; line-height: 1.6;'>
+        <p>Datasets and classes are referenced from: <a href="https://www.imageclef.org/node/319" target="_blank">ImageCLEF</a> and for more details: <a href="https://github.com/Tanaanan/SnakeCLEF2024_MLCS" target="_blank">GitHub repository</a></p>
+        <p style='font-size: smaller;'>This project is part of the course 'Machine Learning Systems (01418262).'</p>
+        <p style='font-size: smaller;'>Developed by Tanaanan, Narakorn, Department of Computer Science, Kasetsart University.</p>
+    </div>
+    """,
+    live=True
+)
+
+if __name__ == "__main__":
+    interface.launch()

requirements.txt

@@ -0,0 +1,7 @@
+torch
+timm
+gradio
+os
+Pillow
+torchvision
+