Create app.py

app.py

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+# -*- coding: utf-8 -*-
+
+
+import gradio as gr
+import numpy as np
+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_model.h5', custom_objects=custom_objects)
+
+# Define your class labels or categories for predictions
+train_info = []  # Replace with your actual class labels
+
+# Read image names from the text file
+with open('label.txt', 'r') as file:
+    train_info = [line.strip() for line in file.read().splitlines()]
+
+
+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
+    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()
+
+gr.Interface(fn=predict_image, inputs=input_image, outputs=output_label, capture_session=True).launch()