Create app.py

app.py

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+from flask import Flask, render_template, request, redirect, url_for
+
+from keras.models import load_model
+
+from keras.preprocessing import image
+
+import numpy as np
+
+import cv2
+
+import io
+
+import base64
+
+from pymongo import MongoClient
+
+
+
+app = Flask(__name__)
+
+
+
+# Load the trained model
+
+model = load_model('weights.hdf5')
+
+model.compile(loss='binary_crossentropy',
+
+              optimizer='rmsprop',
+
+              metrics=['accuracy'])
+
+
+
+# MongoDB connection
+
+client = MongoClient('mongodb://localhost:27017/')
+
+db = client['userfeedback']
+
+feedback_collection = db['feedback']
+
+
+
+@app.route('/', methods=['GET', 'POST'])
+
+def index():
+
+    if request.method == 'POST':
+
+        return redirect(url_for('upload'))
+
+    return render_template('index.html')
+
+
+
+@app.route('/upload', methods=['GET', 'POST'])
+
+def upload():
+
+    if request.method == 'POST':
+
+        # Get the uploaded image file
+
+        img_file = request.files['file']
+
+        if img_file:
+
+            # Read the image file
+
+            img_bytes = img_file.stream.read()
+
+            # Convert bytes to numpy array
+
+            img_np = np.frombuffer(img_bytes, np.uint8)
+
+            # Decode numpy array to image
+
+            img = cv2.imdecode(img_np, cv2.IMREAD_COLOR)
+
+            # Resize the image to match the input shape expected by the model
+
+            img_resized = cv2.resize(img, (150, 150))
+
+            # Expand the dimensions to match the input shape expected by the model
+
+            x = np.expand_dims(img_resized, axis=0)
+
+            # Normalize the image data
+
+            x = x / 255.0
+
+            # Predict probabilities for each class
+
+            probabilities = model.predict(x)
+
+            # Find the index of the class with the highest probability
+
+            predicted_class_index = np.argmax(probabilities)
+
+            # Determine the class label
+
+            if predicted_class_index == 1:
+
+                prediction = "Cancer"
+
+                # Swap red and violet colors
+
+                img[:,:,0], img[:,:,2] = img[:,:,2], img[:,:,0].copy()
+
+            else:
+
+                prediction = "Normal"
+
+                img[:,:,0], img[:,:,2] = img[:,:,2], img[:,:,0].copy()
+
+                
+
+            # Convert the swapped image to base64 format for HTML rendering
+
+            _, img_encoded = cv2.imencode('.png', img)
+
+            swapped_img_base64 = base64.b64encode(img_encoded).decode()
+
+            # Render the result template with the prediction and swapped image
+
+            return render_template('result.html', prediction=prediction, swapped_img_base64=swapped_img_base64)
+
+    return render_template('upload.html')
+
+
+
+@app.route('/submit', methods=['POST'])
+
+def submit_feedback():
+
+    if request.method == 'POST':
+
+        feedback = request.form.get('feedback')
+
+        if feedback:
+
+            feedback_collection.insert_one({'feedback': feedback})
+
+            # Show alert for successful submission
+
+            return '''
+
+                <script>
+
+                    alert('Thank you for submitting your feedback!');
+
+                    window.location.href = '/';
+
+                </script>
+
+            '''
+
+    return redirect(url_for('index'))
+
+
+
+if __name__ == '__main__':
+
+    app.run(debug=True)