Update app.py

app.py

@@ -1,38 +1,36 @@
 # -*- coding: utf-8 -*-
-
-
 import gradio as gr
 import numpy as np
+import tensorflow as tf
 import tensorflow_hub as hub
-from tensorflow.keras.models import load_model
 import cv2
 
 # 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_model4.h5', custom_objects=custom_objects)
-
-# Define your class labels or categories for predictions
-train_info = []  # Replace with your actual class labels
+# Load your model (ensure the path to the model file is correct)
+model = tf.keras.models.load_model('bird_model4.h5', custom_objects=custom_objects)
 
-# Read image names from the text file
+# Load the class labels from the text file
+train_info = []
 with open('labelwithspace.txt', 'r') as file:
-    train_info = [line.strip() for line in file.read().splitlines()]
-
+    train_info = [line.strip() for line in file.readlines()]
 
+# Function to preprocess the image and make predictions
 def predict_image(image):
+    # Resize the image to the model's input size
     img = cv2.resize(image, (224, 224))
-    img = img / 255.0
+    img = img / 255.0  # Normalize the image
+    # Make predictions using the model
     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
-    label = train_info[top_class]  # Use the index to retrieve the label
+    top_classes = np.argsort(predictions)[-3:][::-1]  # Get indices of top 3 predictions
+    top_class = top_classes[0]  # Index of the top prediction
+    label = train_info[top_class]  # Retrieve the label using the index
     return label
 
-
-# Define Gradio interface
+# Define the Gradio interface
 input_image = gr.inputs.Image(shape=(224, 224))
 output_label = gr.outputs.Label()
 
-gr.Interface(fn=predict_image, inputs=input_image, outputs=output_label, capture_session=True).launch()
+# Launch the Gradio app
+gr.Interface(fn=predict_image, inputs=input_image, outputs=output_label, capture_session=True).launch()