Update app.py

app.py

@@ -5,12 +5,20 @@ import numpy as np
 import tensorflow_hub as hub
 from tensorflow.keras.models import load_model
 import cv2
+import os
+
+# Disable GPU (optional, for CPU-only execution)
+os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
 
 # Define a dictionary to map the custom layer to its implementation
 custom_objects = {'KerasLayer': hub.KerasLayer}
 
 # Load your model (ensure the path is correct)
-model = load_model('bird_model.h5', custom_objects=custom_objects)
+try:
+    model = load_model('bird_model.h5', custom_objects=custom_objects)
+except Exception as e:
+    print(f"Model loading failed with error: {e}")
+    exit(1)
 
 # Define your class labels or categories for predictions
 train_info = []  # Replace with your actual class labels
@@ -19,19 +27,19 @@ train_info = []  # Replace with your actual class labels
 with open('labelwithspace.txt', 'r') as file:
     train_info = [line.strip() for line in file.read().splitlines()]
 
-
+# Preprocess and predict function
 def predict_image(image):
-    img = cv2.resize(image, (224, 224))
-    img = img / 255.0
-    predictions = model.predict(img[np.newaxis, ...])[0]
-    top_classes = np.argsort(predictions)[-3:][::-1]
-    top_class = top_classes[0]  # Get the index of the top prediction
+    img = cv2.resize(image, (224, 224))  # Resize to match model input size
+    img = img / 255.0  # Normalize pixel values
+    img = img[np.newaxis, ...]  # Add batch dimension
+    predictions = model.predict(img)[0]  # Get predictions
+    top_class = np.argmax(predictions)  # Get the index of the top prediction
     label = train_info[top_class]  # Use the index to retrieve the label
     return label
 
-
 # Define Gradio interface
-input_image = gr.inputs.Image(shape=(224, 224))
-output_label = gr.outputs.Label()
+input_image = gr.Image(shape=(224, 224), label="Input Image")
+output_label = gr.Label(label="Predicted Bird Species")
 
-gr.Interface(fn=predict_image, inputs=input_image, outputs=output_label, capture_session=True).launch()
+# Launch Gradio
+gr.Interface(fn=predict_image, inputs=input_image, outputs=output_label).launch()