Update app.py

app.py

@@ -1,45 +1,43 @@
-# -*- coding: utf-8 -*-
+# app.py
+
 import gradio as gr
 import numpy as np
 import tensorflow as tf
+from tensorflow.keras.models import load_model
 import cv2
-import tensorflow_hub as hub
 
-# Load class labels from the text file
+# Load the pre-trained model
+model = load_model('bird_model.h5')
+
+# Load class labels from your text file
 train_info = []
 with open('labelwithspace.txt', 'r') as file:
-    train_info = [line.strip() for line in file.readlines()]
-
-# Load your actual model from the .h5 file
-def load_real_model():
-    try:
-        # Register KerasLayer from TensorFlow Hub if used
-        custom_objects = {'KerasLayer': hub.KerasLayer}
-        model = tf.keras.models.load_model('bird_model4.h5', custom_objects=custom_objects)
-    except Exception as e:
-        print("Error loading the model:", e)
-        exit()
-    return model
+    train_info = [line.strip() for line in file.read().splitlines()]
 
-# Initialize the real model
-model = load_real_model()
-
-# Function to preprocess the image and make predictions
-def predict_image(image):
-    # Resize and normalize the image
+# Function to preprocess the input image
+def preprocess_image(image):
+    # Resize the image to the input size expected by the model
     img = cv2.resize(image, (224, 224))
-    img = img / 255.0  # Normalize to [0, 1] range
+    img = img / 255.0  # Normalize the image
+    return img
 
-    # Make predictions using the loaded model
-    predictions = model.predict(img[np.newaxis, ...])[0]
-    top_classes = np.argsort(predictions)[-3:][::-1]  # Indices of top 3 predictions
-    top_class = top_classes[0]  # Index of the highest probability class
-    label = train_info[top_class]  # Get the corresponding label
+# Prediction function
+def predict_image(image):
+    # Preprocess the image
+    img = preprocess_image(image)
+    # Expand dimensions to match the model's input shape
+    img = np.expand_dims(img, axis=0)
+    # Get model predictions
+    predictions = model.predict(img)[0]
+    # Find the top prediction
+    top_class = np.argmax(predictions)
+    # Get the label for the top prediction
+    label = train_info[top_class]
     return label
 
 # Define the Gradio interface
-input_image = gr.Image()
-output_label = gr.Label()
+input_image = gr.Image(shape=(224, 224), label="Input Image")
+output_label = gr.Label(label="Predicted Bird Species")
 
-# Launch the Gradio interface for Hugging Face deployment
-gr.Interface(fn=predict_image, inputs=input_image, outputs=output_label).launch()
+# Launch the Gradio app
+gr.Interface(fn=predict_image, inputs=input_image, outputs=output_label, capture_session=True).launch()