Upload app.py

app.py

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+# -*- coding: utf-8 -*-
+"""Bird_Species_Interface.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1phGfuDAxvDjzxX7jYYCg92VjPhua9u1_
+"""
+
+
+import gradio as gr
+import numpy as np
+import tensorflow_hub as hub
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+import cv2
+
+import gradio as gr
+import tensorflow as tf
+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) and provide the custom_objects dictionary
+model = tf.keras.models.load_model('model.h5', custom_objects=custom_objects)
+
+# Define a function to preprocess the image
+def preprocess_image(image):
+    img = cv2.resize(image, (224, 224))
+    img = img / 255.0  # Normalize pixel values to [0, 1]
+    return img
+
+# Define the prediction function
+def predict_image(image):
+    img = preprocess_image(image)
+    img = img[np.newaxis, ...]  # Add batch dimension
+    prediction = model.predict(img)
+    predicted_class = tf.argmax(prediction, axis=1).numpy()[0]
+    confidence = tf.reduce_max(prediction).numpy()
+    return f"Class: {predicted_class}, Confidence: {confidence:.4f}"
+
+# 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,
+    live=True
+).launch()