ironhack-labs · Dudokic · Oct 18, 2024 · Oct 18, 2024 · Oct 18, 2024
diff --git a/CFM.png b/CFM.png
diff --git a/Deep-Learning_CNN.pptx b/Deep-Learning_CNN.pptx
diff --git a/Image processing.ipynb b/Image processing.ipynb
diff --git a/README.md b/README.md
diff --git a/Report.docx b/Report.docx
diff --git a/Report_Image_Processing_project.pdf b/Report_Image_Processing_project.pdf
diff --git a/app.py b/app.py
@@ -0,0 +1,66 @@
+from flask import Flask, request, render_template
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# Initialize the Flask app (must be before defining routes)
+app = Flask(__name__)
+
+# Load your pre-trained model (make sure to specify the correct path)
+model = tf.keras.models.load_model("model.h5")  # Adjust to your model's path
+
+# Class labels (for example, CIFAR-10)
+labels = [
+    "airplane", "automobile", "bird", "cat", "deer",
+    "dog", "frog", "horse", "ship", "truck"
+]
+
+# Define a route for the homepage
+@app.route('/')
+def home():
+    return render_template('upload.html')  # Render the upload.html template
+
+# Define a route for handling image uploads and predictions
+@app.route('/predict', methods=['POST'])
+def predict():
+    if 'file' not in request.files:
+        return "No file uploaded. Please select a file."
+
+    file = request.files['file']
+
+    if file.filename == '':
+        return "No file selected. Please select a valid image file."
+
+    if file:
+        try:
+            # Open the image and convert to RGB to ensure it has 3 channels
+            img = Image.open(file).convert('RGB')
+
+            # Resize to match the model's input size
+            img = img.resize((32, 32))
+
+            # Convert the image to a NumPy array and normalize it
+            img_array = np.array(img)
+            img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+            img_array = img_array / 255.0  # Normalize the image
+
+            # Make predictions
+            predictions = model.predict(img_array)
+            predicted_class = np.argmax(predictions)
+            predicted_label = labels[predicted_class]
+
+            # Get probabilities for each class
+            probabilities = predictions[0]
+            class_probabilities = {label: f"{prob:.2f}%" for label, prob in zip(labels, probabilities * 100)}
+
+            # Return the predicted class and probabilities
+            return render_template('result.html', predicted_label=predicted_label, class_probabilities=class_probabilities)
+
+        except Exception as e:
+            return f"Error processing the image: {str(e)}"
+
+    return "Something went wrong. Please try again."
+
+# Run the app
+if __name__ == '__main__':
+    app.run(debug=True, host='0.0.0.0', port=5000)
diff --git a/batches.meta b/batches.meta
diff --git a/data_batch_1 b/data_batch_1
diff --git a/data_batch_2 b/data_batch_2
diff --git a/data_batch_3 b/data_batch_3
diff --git a/data_batch_4 b/data_batch_4
diff --git a/data_batch_5 b/data_batch_5
diff --git a/model.h5 b/model.h5
diff --git a/requirements.txt b/requirements.txt
@@ -0,0 +1,5 @@
+Flask==2.1.1
+tensorflow==2.17.0
+pillow==10.0.0
+numpy==1.26.4
+Werkzeug==2.0.3  # Downgraded for Flask compatibility
diff --git a/result b/result
@@ -0,0 +1,36 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Prediction Result</title>
+    <!-- Bootstrap CSS for styling -->
+    <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0-alpha1/dist/css/bootstrap.min.css" rel="stylesheet">
+</head>
+<body>
+    <div class="container mt-5">
+        <div class="row justify-content-center">
+            <div class="col-md-6">
+                <div class="card shadow-sm">
+                    <div class="card-body text-center">
+                        <h3 class="card-title">Prediction Result</h3>
+                        <p class="lead">Predicted Class: <strong>{{ predicted_label }}</strong></p>
+                        <h5>Class Probabilities:</h5>
+                        <ul class="list-group">
+                            {% for label, probability in class_probabilities.items() %}
+                                <li class="list-group-item d-flex justify-content-between align-items-center">
+                                    {{ label }}
+                                    <span class="badge bg-primary rounded-pill">{{ probability }}</span>
+                                </li>
+                            {% endfor %}
+                        </ul>
+                        <a href="/" class="btn btn-primary mt-3">Upload Another Image</a>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+    <!-- Bootstrap JS (optional) -->
+    <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0-alpha1/dist/js/bootstrap.bundle.min.js"></script>
+</body>
+</html>
diff --git a/static/css/style.css.txt b/static/css/style.css.txt
@@ -0,0 +1,23 @@
+body {
+    background-color: #f7f7f7;
+    font-family: Arial, sans-serif;
+}
+
+.card {
+    margin-top: 50px;
+    padding: 20px;
+}
+
+h3 {
+    margin-bottom: 20px;
+}
+
+.btn-primary {
+    background-color: #007bff;
+    border-color: #007bff;
+}
+
+.btn-primary:hover {
+    background-color: #0056b3;
+    border-color: #004085;
+}