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LICENSE

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+MIT License
+
+Copyright (c) 2024 Dhinesh kumar A
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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----
-license: mit
----
+# vit
+multiple sclerosis it can classify

app.py

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+import os
+import torch
+from flask import Flask, render_template, request, redirect, url_for
+from transformers import ViTForImageClassification, ViTFeatureExtractor
+from PIL import Image
+from werkzeug.utils import secure_filename
+
+app = Flask(__name__, static_folder="static", template_folder="templates")
+
+# Load feature extractor and model
+feature_extractor = ViTFeatureExtractor.from_pretrained(
+    'google/vit-base-patch16-224-in21k'
+)
+model = ViTForImageClassification.from_pretrained(
+    'google/vit-base-patch16-224-in21k', num_labels=4
+)
+
+# Path to custom trained weights
+model_path = r'C:\Users\Dhinesh kumar A\Downloads\vit-main\vit-main\vit_multiple_sclerosis_final.pth'
+model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
+model.eval()
+
+# Class mapping
+class_mapping = {
+    0: 'Control-Axial',
+    1: 'Control-Sagittal',
+    2: 'MS-Axial',
+    3: 'MS-Sagittal'
+}
+
+# Upload folder
+UPLOAD_FOLDER = os.path.join(app.static_folder, "uploads")
+os.makedirs(UPLOAD_FOLDER, exist_ok=True)
+
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+
+@app.route('/predict', methods=['GET', 'POST'])
+def upload_and_predict():
+    if request.method == 'POST':
+        if 'file' not in request.files or request.files['file'].filename == '':
+            return redirect(request.url)
+
+        file = request.files['file']
+        filename = secure_filename(file.filename)
+        filepath = os.path.join(UPLOAD_FOLDER, filename)
+        file.save(filepath)
+
+        # Preprocess image
+        image = Image.open(filepath).convert('RGB')
+        inputs = feature_extractor(images=image, return_tensors="pt")
+        pixel_values = inputs['pixel_values']
+
+        # Inference
+        with torch.no_grad():
+            outputs = model(pixel_values=pixel_values)
+            logits = outputs.logits
+            predicted_class_idx = logits.argmax(-1).item()
+            predicted_class = class_mapping[predicted_class_idx]
+
+        # Serve uploaded image via /static/uploads
+        image_url = url_for('static', filename=f'uploads/{filename}')
+
+        return render_template(
+            'result.html',
+            prediction=predicted_class,
+            uploaded_image=image_url
+        )
+
+    return render_template('upload.html')
+
+
+if __name__ == '__main__':
+    app.run(debug=True)

requirements.txt

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+Flask
+torch
+transformers
+Pillow
+numpy
+lime
+scikit-image
+matplotlib
+Werkzeug