Update app.py

app.py

@@ -1,18 +1,25 @@
 import base64
-import numpy as np
 import cv2
-from flask import Flask, request, jsonify, render_template
+import numpy as np
+from flask import Flask, request, jsonify, render_template 
 from tensorflow import keras
-import torch
-from ultralytics import YOLO
+import sys
+import io
 
-app = Flask(__name__)
+# Set the default encoding to utf-8
+sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
 
-# Load the Keras model
-model_keras = keras.models.load_model('fresh_model.keras')
+app = Flask(__name__)
 
-# Load the YOLO model
-model_yolo = YOLO('another_model.pt')
+# Load your pre-trained model
+model = keras.models.load_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():
@@ -27,27 +34,30 @@ def predict():
 
         # 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)
 
-        # Prepare image for Keras model
-        image_keras = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-        image_keras = cv2.resize(image_keras, (225, 225))
-        image_keras = image_keras.astype('float32') / 255.0
-        image_keras = np.expand_dims(image_keras, axis=0)
+        # 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 prediction with Keras model
-        keras_predictions = model_keras.predict(image_keras)
-        predicted_class = np.argmax(keras_predictions, axis=1)[0]
+        # Make a prediction using the model
+        predictions = model.predict(image)
+        predicted_class = np.argmax(predictions, axis=1)[0]
 
-        # Prepare image for YOLO model
-        yolo_predictions = model_yolo.predict(image)  # YOLO prediction
+        # Map the class index to the vegetable label
+        prediction_label = vegetables[predicted_class]
 
-        return jsonify({
-            'keras-prediction': predicted_class,
-            'yolo-prediction': yolo_predictions
-        })
+        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=7860, debug=True)
+    app.run(host='0.0.0.0', port=7860, debug=True)