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 license: mit
 ---

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 license: mit
 ---
+# Practical Alien Detector
+Finetuned resnet50 for detecting if the image you show it is an alien or not.
+You can try it out as an API by following the `How to run an API and test your model` instructions.
+This model was created for fun!
+## Installation
+```shell
+pip3 install fastbook
+pip3 install fastai
+pip3 install flask
+```
+## How to build your model
+```shell
+python3 aliens_model_builder.py
+```
+## How to test your model
+![Python Terminal](./img/python-terminal.png)
+After running it wil ask the image path.
+```shell
+python3 aliens_model_test.py
+```
+## How to run an API and test your model
+![Python API](./img/python-api.png)
+After running it, visit `http://localhost:5000/`
+```shell
+python3 aliens_api.py
+```

aliens_api.py ADDED Viewed

+from flask import Flask, request, jsonify, send_file
+from fastai.vision.all import *
+app = Flask(__name__)
+# Load the trained model
+model_path = './model.pkl'
+learn = load_learner(model_path)
+@app.route('/')
+def index():
+    return send_file('index.html')
+@app.route('/predict', methods=['POST'])
+def predict():
+    # Check if an image file is present in the request
+    if 'image' not in request.files:
+        return jsonify({'error': 'No image file found'}), 400
+    image_file = request.files['image']
+    # Save the uploaded image temporarily
+    image_path = 'uploaded_image.jpg'
+    image_file.save(image_path)
+    # Prepare the image for prediction
+    img = PILImage.create(image_path)
+    # Make predictions
+    pred_class, pred_idx, probs = learn.predict(img)
+    # Prepare the response
+    response = {
+        'predicted_class': pred_class,
+        'predicted_probabilities': probs.tolist()
+    }
+    # Remove the temporary image file
+    os.remove(image_path)
+    return jsonify(response)
+if __name__ == '__main__':
+    app.run()

aliens_model_builder.py ADDED Viewed

+from fastbook import *
+from fastai.vision.all import *
+from fastai.vision.widgets import *
+# Step 1: Gather the data
+# You can use APIs or manually download the data
+# The name of the path where the two folders
+# with the data are located at
+# ie. ./alien_or_not/alien for alien images
+# ie. ./alien_or_not/not_alien for human images
+path = Path('alien_or_not')
+# Step 2: Establish your Data Loader object
+aliens = DataBlock(
+    blocks=(ImageBlock, CategoryBlock),
+    get_items=get_image_files,
+    splitter=RandomSplitter(valid_pct=0.2, seed=42),
+    get_y=parent_label,
+    item_tfms=Resize(128))
+dls = aliens.dataloaders(path)
+dls.valid.show_batch(max_n=4, nrows=1)
+# Step 3: Fine tune your model
+# models you can use:
+# resnet18
+# resnet50
+learn = vision_learner(dls, resnet50, metrics=error_rate)
+learn.fine_tune(500)
+# Step 4: Mistakes and Cleaning them
+# Step 5: Use the model
+is_alien, _, probs = learn.predict(PILImage.create('test_images/4.jpg'))
+print(f"This is a: {is_alien}.")
+print(f"Probability it's an alien: {probs[0]:.4f}")
+# Step 6: Export the model
+learn.export('model.pkl')

aliens_model_test.py ADDED Viewed

+from fastai.vision.all import *
+# Load the trained model
+model_path = './model.pkl'
+learn = load_learner(model_path)
+# Prompt the user to enter the image path
+test_image_path = input("Enter the path to the test image: ")
+# Prepare your test data
+img = PILImage.create(test_image_path)
+# Make predictions
+is_alien, _, probs = learn.predict(img)
+# Print the predicted class and probabilities
+if is_alien == "aliens":
+    print(f"This is an alien.")
+else:
+    print(f"This is not an alien.")
+print(f"Probability it's an alien: {probs[0]:.4f}")

img/python-api.png ADDED Viewed

img/python-terminal.png ADDED Viewed

index.html ADDED Viewed

+<!DOCTYPE html>
+<html>
+<head>
+    <title>Practical Alien Detector</title>
+    <script src="https://code.jquery.com/jquery-3.6.0.min.js"></script>
+    <script>
+        function uploadImage() {
+            var formData = new FormData();
+            formData.append('image', $('#imageUpload')[0].files[0]);
+            $.ajax({
+                url: '/predict',
+                type: 'POST',
+                data: formData,
+                processData: false,
+                contentType: false,
+                success: function(response) {
+                    if (response.predicted_class == "aliens") {
+                        $('#result').text('This is an alien.');
+                    } else {
+                        $('#result').text('This is NOT an alien.');
+                    }
+                    $('#prob').text('Probability its an alien: '+response.predicted_probabilities);
+                },
+                error: function(xhr, status, error) {
+                    console.log(error);
+                }
+            });
+        }
+    </script>
+</head>
+<body>
+    <h1>Practical Alien Detector</h1>
+    <input type="file" id="imageUpload">
+    <button onclick="uploadImage()">Predict</button>
+    <div id="result"></div>
+    <div id="prob"></div>
+</body>
+</html>

model.pkl ADDED Viewed

+version https://git-lfs.github.com/spec/v1
+oid sha256:5caf29df5c2e2c5bafffeb078f41ea86a80ad2ae7ad15db01d9a95fb6b16fbb8
+size 102906478