Cancer Detector App

.gitignore.txt

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+flagged/
+*.pt
+*.png
+*.jpg
+*.mp4
+*.mkv
+gradio_cached_examples/

app.py

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+import gradio as gr
+import cv2
+import requests
+import os
+
+import os
+from PIL import Image
+import timm
+from torchvision import datasets
+import torchvision
+import torch
+from torchvision.transforms import transforms
+import numpy as np
+from PIL import ImageFile
+import matplotlib.pyplot as plt
+import json
+import warnings
+import time
+import glob
+import shutil
+
+warnings.filterwarnings("ignore")
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+
+
+def predict(image, model, device, class_name):
+
+    prediction_transform = transforms.Compose([transforms.Resize(size=(224, 224)),
+                                               transforms.ToTensor(),
+                                               transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
+    try:
+      image = prediction_transform(image)[:3,:,:].unsqueeze(0)
+    except:
+      image = image.convert('RGB')
+      image = prediction_transform(image)[:3,:,:].unsqueeze(0)
+
+    if device == 'cuda':
+        if torch.cuda.is_available():
+            image = image.cuda()
+        else:
+            print("You don't have cuda")
+
+    with torch.no_grad():
+      model.eval()
+      pred = model(image)
+
+
+    idx = torch.argmax(pred)
+
+    prob = pred[0][idx].item()*100
+
+    return prob, class_name[idx]
+
+
+model = timm.create_model('resnet50', pretrained=True)
+
+model.fc = torch.nn.Sequential(torch.nn.Linear(2048, 256),
+                                    torch.nn.Dropout(0.2),
+                                    torch.nn.ReLU(),
+                                    torch.nn.Linear(256, 64),
+                                    torch.nn.Dropout(0.2),
+                                    torch.nn.ReLU(),
+                                    torch.nn.Linear(64, 32),
+                                    torch.nn.Dropout(0.2),
+                                    torch.nn.ReLU(),
+                                    torch.nn.Linear(32, 4),
+                                    torch.nn.Softmax()
+                                    )
+
+model.load_state_dict(torch.load('model_ResNet50_acc_max.pt',map_location=torch.device('cpu')))
+
+
+
+class_name = ['adenocarcinoma',
+             'large.cell.carcinoma',
+             'normal',
+             'squamous.cell.carcinoma']
+
+
+display_prob = True
+show=True
+path = glob.glob('*.png')
+
+def show_preds_image(path):
+    for image in path:
+        img = Image.open(image)
+        if show:
+            plt.imshow(img)
+            plt.show()
+        class_name = ['adenocarcinoma',
+                    'large.cell.carcinoma',
+                    'normal',
+                    'squamous.cell.carcinoma']
+        prob, result = predict(img, model, 'cpu', class_name)
+        if display_prob:
+            print('Probability of {} : {:.6f}'.format(result, prob))
+   
+    return prob, result
+ 
+inputs_image = [
+    gr.components.Image(type="filepath", label="Input Image"),
+]
+
+interface_image = gr.Interface(
+    fn=show_preds_image,
+    inputs=inputs_image,
+    title="Cancer detector",
+    cache_examples=False,
+)

requirement.txt.txt

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+timm>=0.9.2
+opencv-python>=4.8.0.74