Create app.py

app.py

@@ -0,0 +1,44 @@
+import os
+import numpy as np
+import tensorflow as tf
+import streamlit as st
+from tensorflow.keras.preprocessing.image import load_img, img_to_array
+from flask import Flask, request, jsonify
+
+app = Flask(__name__)
+st.title("Nova'23 Classification Model API")
+st.write("Listening...")
+
+@app.route('/predict', methods=['POST'])
+def predict_image():
+    # Check if an image was uploaded
+    if 'file' not in request.files:
+        return 'No file uploaded'
+    file = request.files['file']
+
+    # Save the image to a temporary file
+    file_path = 'temp_image.jpg'
+    file.save(file_path)
+
+    # Load and preprocess the image
+    height = 180
+    width = 180
+    channels = 3
+    img = load_img(file_path, target_size=(height, width))
+    img_array = img_to_array(img)
+    img_array = img_array / 255.0
+    img_array = tf.reshape(img_array, [1, height, width, channels])
+
+    # Load the model and make a prediction
+    model = tf.keras.models.load_model('nova.h5')
+    prediction = model.predict(img_array)
+    st.write("Prediction: ", prediction)
+    
+    # Delete the temporary file
+    os.remove(file_path)
+
+    # Return the prediction as JSON
+    return jsonify({'prediction': 'Pneumonia' if prediction[0][0] > 0.5 else 'Normal'})
+
+if __name__ == '__main__':
+    app.run()