Update app.py

app.py

@@ -1,63 +1,63 @@
-import base64
-import cv2
-import numpy as np
-from flask import Flask, request, jsonify, render_template
-import keras._tf_keras.keras as keras
-import sys
-import io
-
-# Set the default encoding to utf-8
-sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
-
-app = Flask(__name__)
-
-# Load your pre-trained model
-model = keras.models.load_model(r'model\fresh_model.keras')
-vegetables = [
-    "banana", "beans broad", "beans cluster", "beans haricot", "beetroot",
-    "bitter guard", "bottle guard", "brinjal long", "brinjal[purple]", "cabbage",
-    "capsicum green", "carrot", "cauliflower", "chilli green", "colocasia arvi",
-    "corn", "cucumber", "drumstick", "garlic", "ginger", "ladies finger",
-    "lemons", "Onion red", "potato", "sweet potato", "tomato", "Zuchini"
-]
-
-@app.route('/')
-def index():
-    return render_template('index.html')
-
-@app.route('/predict', methods=['POST'])
-def predict():
-    try:
-        # Extract and decode image data
-        data = request.json
-        image_data = data['image'].split(',')[1]  # Remove data:image/jpeg;base64,
-
-        # Decode the base64 string into a NumPy array
-        nparr = np.frombuffer(base64.b64decode(image_data), np.uint8)
-
-        # Convert the NumPy array into an OpenCV image (BGR format)
-        image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
-
-        # Convert to RGB format
-        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-
-        # Resize the image to the expected input size of the model (e.g., 225x225)
-        image = cv2.resize(image, (225, 225))
-
-        # Normalize the image
-        image = image.astype('float32') / 255.0  # Normalize to [0, 1]
-        image = np.expand_dims(image, axis=0)  # Add batch dimension
-
-        # Make a prediction using the model
-        predictions = model.predict(image)
-        predicted_class = np.argmax(predictions, axis=1)[0]
-
-        # Map the class index to the vegetable label
-        prediction_label = vegetables[predicted_class]
-
-        return jsonify({'prediction': prediction_label})
-    except Exception as e:
-        return jsonify({'error': str(e)}), 500
-
-if __name__ == '__main__':
-    app.run(debug=True)
+import base64
+import cv2
+import numpy as np
+from flask import Flask, request, jsonify, render_template
+import keras._tf_keras.keras as keras
+import sys
+import io
+
+# Set the default encoding to utf-8
+sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
+
+app = Flask(__name__)
+
+# Load your pre-trained model
+model = keras.models.load_model(r'model\fresh_model.keras')
+vegetables = [
+    "banana", "beans broad", "beans cluster", "beans haricot", "beetroot",
+    "bitter guard", "bottle guard", "brinjal long", "brinjal[purple]", "cabbage",
+    "capsicum green", "carrot", "cauliflower", "chilli green", "colocasia arvi",
+    "corn", "cucumber", "drumstick", "garlic", "ginger", "ladies finger",
+    "lemons", "Onion red", "potato", "sweet potato", "tomato", "Zuchini"
+]
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    try:
+        # Extract and decode image data
+        data = request.json
+        image_data = data['image'].split(',')[1]  # Remove data:image/jpeg;base64,
+
+        # Decode the base64 string into a NumPy array
+        nparr = np.frombuffer(base64.b64decode(image_data), np.uint8)
+
+        # Convert the NumPy array into an OpenCV image (BGR format)
+        image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+
+        # Convert to RGB format
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+        # Resize the image to the expected input size of the model (e.g., 225x225)
+        image = cv2.resize(image, (225, 225))
+
+        # Normalize the image
+        image = image.astype('float32') / 255.0  # Normalize to [0, 1]
+        image = np.expand_dims(image, axis=0)  # Add batch dimension
+
+        # Make a prediction using the model
+        predictions = model.predict(image)
+        predicted_class = np.argmax(predictions, axis=1)[0]
+
+        # Map the class index to the vegetable label
+        prediction_label = vegetables[predicted_class]
+
+        return jsonify({'prediction': prediction_label})
+    except Exception as e:
+        return jsonify({'error': str(e)}), 500
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=5000, debug=True)