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import gradio as gr | |
from keras.preprocessing import image | |
from keras.applications.vgg16 import preprocess_input, decode_predictions | |
import numpy as np | |
import numpy as np | |
import pandas as pd | |
import matplotlib.pyplot as plt | |
from glob import glob | |
# loading the directories | |
# importing the libraries | |
import tensorflow as tf | |
from tensorflow.keras.models import Model | |
from tensorflow.keras.layers import Flatten, Dense | |
from tensorflow.keras.applications import VGG16 | |
#from keras.preprocessing import image | |
num_classes=10 | |
IMAGE_SHAPE = [224, 224] | |
class_labels = ['exterior_building','icons','interior_building','landscapes','layouts','others','people','scanned_documents','signatures','under_construction'] | |
def greet(name): | |
return "Hello " + name + "!!" | |
model = tf.keras.models.load_model("./classification_model.h5") | |
class_labels = ['exterior_building','icons','interior_building','landscapes','layouts','others','people','scanned_documents','signatures','under_construction'] | |
def predict_image(image): | |
# img_path = '/Users/balamuruga/Desktop/Screenshot 2023-11-08 at 9.22.52 PM.png' | |
# img = image.load_img(img_path, target_size=(224, 224)) | |
# x = image.img_to_array(img) | |
# x = np.expand_dims(x, axis=0) | |
# x = preprocess_input(x) | |
image = image.reshape((-1, 224, 224, 3)) | |
# preds=model.predict(image) | |
prediction = model.predict(image).flatten() | |
print(prediction) | |
return {class_labels[i]: float(prediction[i]) for i in range(10)} | |
# create a list containing the class labels | |
# # find the index of the class with maximum score | |
# pred = np.argmax(preds, axis=-1) | |
# # print the label of the class with maximum score | |
# print(class_labels[pred[0]]) | |
# return {class_labels[i]: float(pred[i]) for i in range(10)} | |
# img_4d=img.reshape(-1,256,256,3) | |
# prediction=model.predict(img_4d)[0] | |
# return {class_names[i]: float(prediction[i]) for i in range(5)} | |
# iface = gr.Interface(fn=predict_image, inputs="text", outputs="text") | |
# iface.launch() | |
image = gr.inputs.Image(shape = (224, 224)) | |
label = gr.outputs.Label(num_top_classes = 10) | |
gr.Interface(fn=predict_image, inputs=image, outputs=label,interpretation='default').launch(debug='True') | |