Upload 4 files

app.py

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+import gradio as gr
+import torch
+import os
+import torch.nn as nn
+from torchvision import transforms
+from sklearn.model_selection import GroupKFold
+import cv2
+from skimage import io
+import torch
+from torch import nn
+import os
+from datetime import datetime
+import time
+import random
+import cv2
+import pandas as pd
+import numpy as np
+import albumentations as A
+from albumentations.pytorch.transforms import ToTensorV2
+import sklearn
+from efficientnet_pytorch import EfficientNet
+
+def get_net():
+    net = EfficientNet.from_pretrained('efficientnet-b4')
+    net._fc = nn.Linear(in_features=1792, out_features=4, bias=True)
+    return net
+
+def get_test_transforms(mode):
+    if mode == 0:
+        return A.Compose([
+                A.Resize(height=512, width=512, p=1.0),
+                ToTensorV2(p=1.0),
+            ], p=1.0)
+    elif mode == 1:
+        return A.Compose([
+                A.HorizontalFlip(p=1),
+                A.Resize(height=512, width=512, p=1.0),
+                ToTensorV2(p=1.0),
+            ], p=1.0)
+    elif mode == 2:
+        return A.Compose([
+                A.VerticalFlip(p=1),
+                A.Resize(height=512, width=512, p=1.0),
+                ToTensorV2(p=1.0),
+            ], p=1.0)
+    else:
+        return A.Compose([
+                A.HorizontalFlip(p=1),
+                A.VerticalFlip(p=1),
+                A.Resize(height=512, width=512, p=1.0),
+                ToTensorV2(p=1.0),
+            ], p=1.0)
+
+def preprocess_image(image_path, transforms=None):
+    image = cv2.imread(image_path, cv2.IMREAD_COLOR)
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB).astype(np.float32)
+    image /= 255.0  # Normalize to [0, 1]
+    if transforms:
+        sample = {'image': image}
+        sample = transforms(**sample)
+        image = sample['image']
+    image = torch.tensor(image, dtype=torch.float32).unsqueeze(0)
+    return image
+
+net = get_net()
+checkpoint = torch.load('model.bin', map_location="cpu")
+net.load_state_dict(checkpoint['model_state_dict']);
+net.eval()
+
+path = "uploads"  # Define path for uploads
+os.makedirs(path, exist_ok=True)  # Ensure directory exists
+
+def predict_image(img):
+    img_path = os.path.join(path, img.name)
+    img.save(img_path)
+
+    image_tensor = preprocess_image(img_path, get_test_transforms(224))
+    pred = net(image_tensor)
+    y_pred = 1 - nn.functional.softmax(pred, dim=1).data.cpu().numpy()[:, 0]
+    return float(y_pred.item())
+
+iface = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="file"),
+    outputs="number",
+    title="Image Prediction App",
+    description="Upload an image and get a model prediction."
+)
+
+iface.launch()

model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:30a5fe4104ba332c3398854d8d0aec0bf81f8ed5b06b3ea75b6dd60ee0372546
+size 211430648

requirements.txt

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+gradio
+numpy
+pandas
+matplotlib
+scikit-learn
+opencv-python
+scikit-image
+torch
+torchvision
+timm
+efficientnet-pytorch
+albumentations