LovnishVerma commited on
Commit
509d7a7
·
verified ·
1 Parent(s): 63f0f6c

Update main.py

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Files changed (1) hide show
  1. main.py +3 -26
main.py CHANGED
@@ -88,7 +88,6 @@ def resultbt():
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  flash('Image successfully uploaded and displayed below')
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-
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  try:
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  # Process the image
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  img = cv2.imread(temp_file.name)
@@ -96,37 +95,16 @@ def resultbt():
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  img = crop_imgs([img])
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  img = img.reshape(img.shape[1:]) # Reshape to (height, width, channels)
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  img = preprocess_imgs([img], (128, 128)) # Resize to (128, 128, 3)
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-
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  # Ensure the input shape matches the model's expectation
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  img = img[0] # Remove unnecessary extra dimension
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  img = np.expand_dims(img, axis=0) # Add batch dimension to match (1, 128, 128, 3)
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-
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  # Make prediction
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  pred = braintumor_model.predict(img)
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  prediction = 'Tumor Detected' if pred[0][0] >= 0.5 else 'No Tumor Detected'
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  confidence_score = float(pred[0][0])
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- # Prepare data for MongoDB
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- result = {
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- "firstname": firstname,
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- "lastname": lastname,
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- "email": email,
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- "phone": phone,
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- "gender": gender,
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- "age": age,
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- "image_name": filename,
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- "prediction": prediction,
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- "confidence_score": confidence_score,
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- "timestamp": datetime.utcnow()
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- }
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-
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- # Insert data into MongoDB
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- collection.insert_one(result)
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-
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- # Return the result to the user
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- return render_template('resultbt.html', filename=filename, fn=firstname, ln=lastname, age=age, r=prediction, gender=gender)
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-
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-
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  # Prepare data for MongoDB
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  result = {
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  "firstname": firstname,
@@ -152,7 +130,6 @@ def resultbt():
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  flash('Allowed image types are - png, jpg, jpeg')
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  return redirect(request.url)
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-
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  @app.route('/dbresults')
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  def dbresults():
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  """Fetch all results from MongoDB, show aggregated data, and render in a template."""
@@ -185,4 +162,4 @@ def dbresults():
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  if __name__ == '__main__':
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- app.run(debug=True)
 
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  flash('Image successfully uploaded and displayed below')
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  try:
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  # Process the image
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  img = cv2.imread(temp_file.name)
 
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  img = crop_imgs([img])
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  img = img.reshape(img.shape[1:]) # Reshape to (height, width, channels)
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  img = preprocess_imgs([img], (128, 128)) # Resize to (128, 128, 3)
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+
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  # Ensure the input shape matches the model's expectation
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  img = img[0] # Remove unnecessary extra dimension
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  img = np.expand_dims(img, axis=0) # Add batch dimension to match (1, 128, 128, 3)
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+
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  # Make prediction
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  pred = braintumor_model.predict(img)
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  prediction = 'Tumor Detected' if pred[0][0] >= 0.5 else 'No Tumor Detected'
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  confidence_score = float(pred[0][0])
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  # Prepare data for MongoDB
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  result = {
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  "firstname": firstname,
 
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  flash('Allowed image types are - png, jpg, jpeg')
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  return redirect(request.url)
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  @app.route('/dbresults')
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  def dbresults():
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  """Fetch all results from MongoDB, show aggregated data, and render in a template."""
 
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  if __name__ == '__main__':
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+ app.run(debug=True)