Update app.py

app.py

@@ -1,58 +1,57 @@
-from flask_cors import CORS
-app = Flask(__name__)
-CORS(app)
-
-from flask import Flask, request, jsonify
-from tensorflow.keras.models import load_model
-import numpy as np
-from PIL import Image
-import io
-
-app = Flask(__name__)
-model = load_model("final_resnet.h5")
-class_names = {
-    0: 'FreshApple', 1: 'FreshBanana', 2: 'FreshGrape', 3: 'FreshGuava',
-    4: 'FreshJujube', 5: 'FreshOrange', 6: 'FreshPomegranate', 7: 'FreshStrawberry',
-    8: 'RottenApple', 9: 'RottenBanana', 10: 'RottenGrape', 11: 'RottenGuava',
-    12: 'RottenJujube', 13: 'RottenOrange', 14: 'RottenPomegranate', 15: 'RottenStrawberry'
-}
-
-def preprocess_image(img_bytes):
-    img = Image.open(io.BytesIO(img_bytes)).resize((224, 224))
-    img = np.array(img) / 255.0
-    return np.expand_dims(img, axis=0)
-
-@app.route('/')
-def home():
-    return "API is running!"
-
-@app.route('/predict', methods=['POST'])
-def predict():
-    file = request.files.get('image')
-    if not file:
-        return jsonify({'error': 'No image provided'}), 400
-
-    img = preprocess_image(file.read())
-    pred = model.predict(img)[0]
-    sorted_indices = np.argsort(pred)[::-1]
-    top1, top2 = sorted_indices[:2]
-    top1_label = class_names[top1]
-    top2_label = class_names[top2]
-    top1_conf = pred[top1] * 100
-    top2_conf = pred[top2] * 100
-
-    result = {}
-
-    if top1_conf >= 80:
-        result['message'] = 'Fresh' if 'Fresh' in top1_label else 'Rotten'
-    elif ('Fresh' in top1_label and 'Rotten' in top2_label) or ('Rotten' in top1_label and 'Fresh' in top2_label):
-        result['message'] = f"{top1_label} ({top1_conf:.1f}%) vs {top2_label} ({top2_conf:.1f}%)"
-    elif 'Rotten' in top1_label:
-        result['message'] = '⚠️ Do not eat this fruit.'
-    elif 'Fresh' in top1_label and top1_conf < 80:
-        result['message'] = '🍃 Seems fresh, but be cautious.'
-
-    result['confidence'] = round(top1_conf, 2)
-    result['fruitType'] = top1_label
-    result['fresh'] = 'Fresh' in top1_label
-    return jsonify(result)
+from flask import Flask, request, jsonify
+from flask_cors import CORS
+from tensorflow.keras.models import load_model
+import numpy as np
+from PIL import Image
+import io
+
+app = Flask(__name__)
+CORS(app)
+
+model = load_model("final_resnet.h5")
+class_names = {
+    0: 'FreshApple', 1: 'FreshBanana', 2: 'FreshGrape', 3: 'FreshGuava',
+    4: 'FreshJujube', 5: 'FreshOrange', 6: 'FreshPomegranate', 7: 'FreshStrawberry',
+    8: 'RottenApple', 9: 'RottenBanana', 10: 'RottenGrape', 11: 'RottenGuava',
+    12: 'RottenJujube', 13: 'RottenOrange', 14: 'RottenPomegranate', 15: 'RottenStrawberry'
+}
+
+def preprocess_image(img_bytes):
+    img = Image.open(io.BytesIO(img_bytes)).resize((224, 224))
+    img = np.array(img) / 255.0
+    return np.expand_dims(img, axis=0)
+
+@app.route('/')
+def home():
+    return "API is running!"
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    file = request.files.get('image')
+    if not file:
+        return jsonify({'error': 'No image provided'}), 400
+
+    img = preprocess_image(file.read())
+    pred = model.predict(img)[0]
+    sorted_indices = np.argsort(pred)[::-1]
+    top1, top2 = sorted_indices[:2]
+    top1_label = class_names[top1]
+    top2_label = class_names[top2]
+    top1_conf = pred[top1] * 100
+    top2_conf = pred[top2] * 100
+
+    result = {}
+
+    if top1_conf >= 80:
+        result['message'] = 'Fresh' if 'Fresh' in top1_label else 'Rotten'
+    elif ('Fresh' in top1_label and 'Rotten' in top2_label) or ('Rotten' in top1_label and 'Fresh' in top2_label):
+        result['message'] = f"{top1_label} ({top1_conf:.1f}%) vs {top2_label} ({top2_conf:.1f}%)"
+    elif 'Rotten' in top1_label:
+        result['message'] = '⚠️ Do not eat this fruit.'
+    elif 'Fresh' in top1_label and top1_conf < 80:
+        result['message'] = '🍃 Seems fresh, but be cautious.'
+
+    result['confidence'] = round(top1_conf, 2)
+    result['fruitType'] = top1_label
+    result['fresh'] = 'Fresh' in top1_label
+    return jsonify(result)