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2343e68 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | from keras.models import load_model
from PIL import Image, ImageOps
import numpy as np
import gradio as gr
# Load model and labels (once at startup)
np.set_printoptions(suppress=True)
model = load_model("keras_model.h5", compile=False)
class_names = open("labels.txt", "r").readlines()
def classify_image(input_img):
"""Classifies the input image using the loaded Keras model."""
if input_img is None:
return "No image provided"
size = (224, 224)
image = ImageOps.fit(Image.fromarray(np.uint8(input_img)), size, Image.Resampling.LANCZOS).convert("RGB")
image_array = np.asarray(image)
normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1
data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
data[0] = normalized_image_array
prediction = model.predict(data, verbose=0)
index = np.argmax(prediction)
class_name = class_names[index][2:].strip() # Remove index and strip whitespace
confidence_score = prediction[0][index]
return f"Class: {class_name}, Confidence: {confidence_score:.4f}"
demo = gr.Interface(
classify_image,
gr.Image(label="Upload image for classification", type="numpy"), # Input: Image upload, numpy type for efficiency
gr.Text(label="Classification Result") # Output: Text classification result
)
demo.launch() |