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import os
os.environ["TRANSFORMERS_CACHE"] = "/tmp/huggingface_cache" # Set cache directory to a writable location
from flask import Flask, request, render_template, jsonify
from transformers import ViTForImageClassification, ViTFeatureExtractor
import torch
import torch.nn as nn
import torchvision.transforms as transforms
from PIL import Image
import io
app = Flask(__name__)
# Load the ViT model and its feature extractor
model_name = "google/vit-base-patch16-224-in21k"
model = ViTForImageClassification.from_pretrained(model_name)
feature_extractor = ViTFeatureExtractor.from_pretrained(model_name)
# Load the trained model weights
num_classes = 7
model.classifier = nn.Linear(model.config.hidden_size, num_classes)
model.load_state_dict(torch.load("skin_cancer_model.pth", map_location=torch.device('cpu')))
model.eval()
# Define class labels
class_labels = ['benign_keratosis-like_lesions', 'basal_cell_carcinoma', 'actinic_keratoses', 'vascular_lesions', 'melanocytic_Nevi', 'melanoma', 'dermatofibroma']
# Define optimal thresholds
thresholds = [0.88134295, 0.43095806, 0.39622146, 0.90647435, 0.8128958, 0.05310565, 0.15926854]
# Define image transformations
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
])
@app.route('/')
def index():
return render_template('index.html', appName="Skin Cancer Classification Application")
def model_predict(image):
image = transform(image).unsqueeze(0) # Add batch dimension
with torch.no_grad():
outputs = model(image)
return outputs
@app.route('/predictApi', methods=["POST"])
def api():
try:
if 'fileup' not in request.files:
return jsonify({'Error': "Please try again. The Image doesn't exist"})
file = request.files.get('fileup')
image = Image.open(io.BytesIO(file.read()))
result = model_predict(image)
probabilities = torch.softmax(result.logits, dim=1).cpu().numpy()[0]
predicted_idx = torch.argmax(torch.tensor(probabilities)).item()
max_prob = probabilities[predicted_idx]
threshold = thresholds[predicted_idx]
if max_prob < threshold:
return jsonify({'Error': 'No cancer detected or benign lesion.'})
prediction = class_labels[predicted_idx]
return jsonify({'prediction': prediction})
except Exception as e:
return jsonify({'Error': 'An error occurred', 'Message': str(e)})
@app.route('/predict', methods=['GET', 'POST'])
def predict():
if request.method == 'POST':
try:
if 'fileup' not in request.files:
return render_template('index.html', prediction='No file selected.', appName="Skin Cancer Classification Application")
file = request.files['fileup']
image = Image.open(io.BytesIO(file.read()))
result = model_predict(image)
probabilities = torch.softmax(result.logits, dim=1).cpu().numpy()[0]
predicted_idx = torch.argmax(torch.tensor(probabilities)).item()
max_prob = probabilities[predicted_idx]
threshold = thresholds[predicted_idx]
if max_prob < threshold:
return render_template('index.html', prediction='No cancer detected or benign lesion.', appName="Skin Cancer Classification Application")
prediction = class_labels[predicted_idx]
return render_template('index.html', prediction=prediction, appName="Skin Cancer Classification Application")
except Exception as e:
return render_template('index.html', prediction='Error: ' + str(e), appName="Skin Cancer Classification Application")
else:
return render_template('index.html', appName="Skin Cancer Classification Application")
if __name__ == '__main__':
app.run(debug=True)
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