Update app.py

app.py

@@ -1,26 +1,59 @@
-import gradio as gr
-import tensorflow as tf
+from flask import Flask, render_template, request, redirect, url_for
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
 import numpy as np
+import os
 from PIL import Image
 
-# Load the model (make sure the file is named my_modal.h5)
-model = tf.keras.models.load_model("my_model.h5")
-class_names = ['Class 1', 'Class 2', 'Class 3']  # Update with your real classes
-
-# Define prediction function
-def predict(image):
-    image = image.resize((224, 224))  # Resize to model input shape
-    img_array = np.array(image) / 255.0
-    img_array = img_array.reshape((1, 224, 224, 3))
-    prediction = model.predict(img_array)
-    predicted_class = class_names[np.argmax(prediction)]
-    confidence = float(np.max(prediction))
-    return {predicted_class: confidence}
-
-# Create Gradio interface
-gr.Interface(
-    fn=predict,
-    inputs=gr.Image(type="pil"),
-    outputs=gr.Label(num_top_classes=3),
-    title="My ML Model"
-).launch()
+# Initialize the Flask app
+app = Flask(__name__)
+
+# Load trained model
+MODEL_PATH = 'my_model.h5'
+model = load_model(MODEL_PATH)
+
+# List of class names (from LabelEncoder's `classes_`)
+class_names = ['Acacia', 'Acer', 'Alnus', 'Anadenanthera', 'Betula', 'Celtis', 'Chamaerops', 
+               'Corylus', 'Eucalyptus', 'Fagus', 'Fraxinus', 'Juglans', 'Laurus', 'Morus',
+               'Pinus', 'Platanus', 'Populus', 'Quercus', 'Salix', 'Tamarix', 'Tilia', 
+               'Ulmus', 'Zea']
+
+# Home route
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+# Predict route
+@app.route('/predict', methods=['POST'])
+def predict():
+    if 'file' not in request.files:
+        return redirect(request.url)
+
+    file = request.files['file']
+    if file.filename == '':
+        return redirect(request.url)
+
+    if file:
+        # Save the uploaded file
+        filepath = os.path.join('static', file.filename)
+        file.save(filepath)
+
+        # Load image
+        img = Image.open(filepath).convert("RGB")
+        img = img.resize((128, 128))
+        img_array = np.array(img) / 255.0
+        img_array = np.expand_dims(img_array, axis=0)
+
+        # Predict
+        predictions = model.predict(img_array)
+        class_index = np.argmax(predictions)
+        predicted_label = class_names[class_index]
+        confidence = round(100 * np.max(predictions), 2)
+
+        return render_template('result.html', label=predicted_label, confidence=confidence, image_path=filepath)
+
+    return redirect(url_for('index'))
+
+# Run the app
+if __name__ == '__main__':
+    app.run(debug=True)