Update app.py

app.py

@@ -1,71 +1,72 @@
-from flask import Flask, render_template, request
-from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing import image
-import numpy as np
-import os
-import uuid
-import tensorflow as tf
-import random
-
-# Fix randomness for reproducibility
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
-tf.random.set_seed(42)
-np.random.seed(42)
-random.seed(42)
-
-app = Flask(__name__)
-
-# Load the model (only one model now)
-model = load_model("model/cat_dog_neither_classifier_new.h5", compile=False)
-  # <-- your model file
-
-class_names = ['cat', 'dog', 'neither']
-UPLOAD_FOLDER = 'static/uploads'
-os.makedirs(UPLOAD_FOLDER, exist_ok=True)
-
-def preprocess_image(img_path):
-    img = image.load_img(img_path, target_size=(224, 224))  # Ensure matches model input
-    img_array = image.img_to_array(img) / 255.0
-    img_array = np.expand_dims(img_array, axis=0)
-    return img_array
-
-@app.route('/', methods=['GET'])
-def index():
-    return render_template('upload.html')
-
-@app.route('/predict', methods=['POST'])
-def predict():
-    if 'file' not in request.files:
-        return "No file part", 400
-
-    file = request.files['file']
-    if file.filename == '':
-        return "No selected file", 400
-
-    filename = str(uuid.uuid4()) + os.path.splitext(file.filename)[1]
-    img_path = os.path.join(UPLOAD_FOLDER, filename)
-    file.save(img_path)
-
-    # Preprocess image
-    processed = preprocess_image(img_path)
-
-    # Predict
-    prediction = model.predict(processed)[0]
-    prediction /= np.sum(prediction)  # normalize
-
-    class_index = int(np.argmax(prediction))
-    confidence = round(float(np.max(prediction)) * 100, 2)
-    final_class = class_names[class_index]
-
-    return render_template(
-        'result.html',
-        prediction=final_class,
-        confidence=confidence,
-        img_path='/' + img_path
-    )
-
-if __name__ == '__main__':
-    import os
-    port = int(os.environ.get("PORT", 5000))  # Render sets PORT
-    app.run(host='0.0.0.0', port=port, debug=False)
-
+from flask import Flask, render_template, request
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+import numpy as np
+import os
+import uuid
+import tensorflow as tf
+import random
+
+# Fix randomness for reproducibility
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+tf.random.set_seed(42)
+np.random.seed(42)
+random.seed(42)
+
+app = Flask(__name__)
+
+# Load the model (only one model now)
+model = load_model("model/cat_dog_neither_classifier_new.h5", compile=False)
+  # <-- your model file
+
+class_names = ['cat', 'dog', 'neither']
+UPLOAD_FOLDER = 'static/uploads'
+os.makedirs(UPLOAD_FOLDER, exist_ok=True)
+
+def preprocess_image(img_path):
+    img = image.load_img(img_path, target_size=(224, 224))  # Ensure matches model input
+    img_array = image.img_to_array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+    return img_array
+
+@app.route('/', methods=['GET'])
+def index():
+    return render_template('upload.html')
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    if 'file' not in request.files:
+        return "No file part", 400
+
+    file = request.files['file']
+    if file.filename == '':
+        return "No selected file", 400
+
+    filename = str(uuid.uuid4()) + os.path.splitext(file.filename)[1]
+    img_path = os.path.join(UPLOAD_FOLDER, filename)
+    file.save(img_path)
+
+    # Preprocess image
+    processed = preprocess_image(img_path)
+
+    # Predict
+    prediction = model.predict(processed)[0]
+    prediction /= np.sum(prediction)  # normalize
+
+    class_index = int(np.argmax(prediction))
+    confidence = round(float(np.max(prediction)) * 100, 2)
+    final_class = class_names[class_index]
+
+    return render_template(
+        'result.html',
+        prediction=final_class,
+        confidence=confidence,
+        img_path='/' + img_path
+    )
+
+if __name__ == '__main__':
+    import os
+    # Hugging Face uses 7860 by default. 
+    # This line checks for a PORT variable but falls back to 7860.
+    port = int(os.environ.get("PORT", 7860)) 
+    app.run(host='0.0.0.0', port=port, debug=False)