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app.py

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+from flask import Flask, render_template, request
+import tensorflow as tf
+import numpy as np
+from keras.models import load_model
+from tensorflow.keras.preprocessing.image import load_img, img_to_array
+from io import BytesIO
+import base64
+
+app = Flask(__name__)
+
+# Load the model
+incept_model = load_model('best_model_2.h5')
+IMAGE_SHAPE = (224, 224)
+classes = ['benign', 'malignant', 'normal']
+
+# Function to prepare the image
+def prepare_image(file):
+    img = load_img(BytesIO(file.read()), target_size=IMAGE_SHAPE)
+    img_array = img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    return tf.keras.applications.efficientnet.preprocess_input(img_array)
+
+@app.route('/')
+def home():
+    return render_template('index.html')
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    if 'file' not in request.files:
+        return redirect(request.url)
+    file = request.files['file']
+    if file.filename == '':
+        return redirect(request.url)
+
+    # Prepare the image for prediction
+    img = prepare_image(file)
+    res = incept_model.predict(img)
+    pred = classes[np.argmax(res)]
+
+    # Encode image to display in the result page
+    file.seek(0)  # Reset file pointer to the beginning
+    img_bytes = file.read()
+    img_base64 = base64.b64encode(img_bytes).decode('utf-8')
+    img_data = f"data:image/jpeg;base64,{img_base64}"
+
+    return render_template('result.html', prediction=pred, image_data=img_data)
+
+if __name__ == '__main__':
+    app.run(debug=True)

best_model_2.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:45614d481307c61e7c367907c13bfbaa974bac4c29da00960c7759ae2fadf655
+size 419012240