Delete app.py

app.py

@@ -1,53 +0,0 @@
-# -*- 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()