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b17cf68
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Parent(s):
a27336c
Create app.py
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app.py
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import gradio as gr
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from keras.preprocessing import image
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from keras.applications.vgg16 import preprocess_input, decode_predictions
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import numpy as np
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from glob import glob
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# loading the directories
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# importing the libraries
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import tensorflow as tf
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from tensorflow.keras.models import Model
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from tensorflow.keras.layers import Flatten, Dense
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from tensorflow.keras.applications import VGG16
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#from keras.preprocessing import image
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num_classes=10
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IMAGE_SHAPE = [224, 224]
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class_labels = ['exterior_building','icons','interior_building','landscapes','layouts','others','people','scanned_documents','signatures','under_construction']
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def greet(name):
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return "Hello " + name + "!!"
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model = tf.keras.models.load_model("./classification_model.h5")
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class_labels = ['exterior_building','icons','interior_building','landscapes','layouts','others','people','scanned_documents','signatures','under_construction']
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def predict_image(image):
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# img_path = '/Users/balamuruga/Desktop/Screenshot 2023-11-08 at 9.22.52 PM.png'
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# img = image.load_img(img_path, target_size=(224, 224))
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# x = image.img_to_array(img)
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# x = np.expand_dims(x, axis=0)
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# x = preprocess_input(x)
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image = image.reshape((-1, 224, 224, 3))
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# preds=model.predict(image)
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prediction = model.predict(image).flatten()
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print(prediction)
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return {class_labels[i]: float(prediction[i]) for i in range(10)}
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# create a list containing the class labels
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# # find the index of the class with maximum score
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# pred = np.argmax(preds, axis=-1)
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# # print the label of the class with maximum score
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# print(class_labels[pred[0]])
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# return {class_labels[i]: float(pred[i]) for i in range(10)}
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# img_4d=img.reshape(-1,256,256,3)
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# prediction=model.predict(img_4d)[0]
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# return {class_names[i]: float(prediction[i]) for i in range(5)}
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# iface = gr.Interface(fn=predict_image, inputs="text", outputs="text")
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# iface.launch()
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image = gr.inputs.Image(shape = (224, 224))
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label = gr.outputs.Label(num_top_classes = 10)
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gr.Interface(fn=predict_image, inputs=image, outputs=label,interpretation='default').launch(debug='True')
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