Spaces:
Sleeping
Sleeping
File size: 2,537 Bytes
284eba0 3e7dbba 42ff0a6 284eba0 0de8536 284eba0 0de8536 05b1421 0de8536 284eba0 0de8536 284eba0 0de8536 15f8afb 7c5859b 021cf70 834fb23 284eba0 7c5859b 834fb23 e980426 7c5859b 834fb23 0de8536 e980426 05b1421 834fb23 05b1421 0de8536 cbb0a6b 3e7dbba f160696 284eba0 4191bbd 5eab787 f160696 834fb23 |
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 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
import gradio as gr
import tensorflow as tf
import gdown
from PIL import Image
import pillow_avif
input_shape = (32, 32, 3)
resized_shape = (224, 224, 3)
num_classes = 10
labels = {
0: "plane",
1: "car",
2: "bird",
3: "cat",
4: "deer",
5: "dog",
6: "frog",
7: "horse",
8: "ship",
9: "truck",
}
# Download the model file
def download_model():
url = "https://drive.google.com/uc?id=12700bE-pomYKoVQ214VrpBoJ7akXcTpL"
output = "modelV2Lmixed.keras"
gdown.download(url, output, quiet=False)
return output
model_file = download_model()
# Load the model
model = tf.keras.models.load_model(model_file)
# Perform image classification for single class output
# def predict_class(image):
# img = tf.cast(image, tf.float32)
# img = tf.image.resize(img, [input_shape[0], input_shape[1]])
# img = tf.expand_dims(img, axis=0)
# prediction = model.predict(img)
# class_index = tf.argmax(prediction[0]).numpy()
# predicted_class = labels[class_index]
# return predicted_class
# Perform image classification for multy class output
def predict_class(image):
img = tf.cast(image, tf.float32)
img = tf.image.resize(img, [input_shape[0], input_shape[1]])
img = tf.expand_dims(img, axis=0)
prediction = model.predict(img)
return prediction[0]
# UI Design for single class output
# def classify_image(image):
# predicted_class = predict_class(image)
# output = f"<h2>Predicted Class: <span style='text-transform:uppercase';>{predicted_class}</span></h2>"
# return output
# UI Design for multy class output
def classify_image(image):
results = predict_class(image)
print(results)
output = {labels.get(i): float(results[i]) for i in range(len(results))}
result = output if max(output.values()) >=0.98 else {"NO_CIFAR10_CLASS": 1}
return result
inputs = gr.components.Image(type="pil", label="Upload an image")
# outputs = gr.outputs.HTML() #uncomment for single class output
outputs = gr.components.Label(num_top_classes=4)
title = "<h1 style='text-align: center;'>Image Classifier</h1>"
description = "Upload an image and get the predicted class."
# css_code='body{background-image:url("file=wave.mp4");}'
gr.Interface(fn=classify_image,
inputs=inputs,
outputs=outputs,
title=title,
examples=[["00_plane.jpg"], ["01_car.jpg"], ["02_house.jpg"], ["03_cat.jpg"], ["04_deer.jpg"]],
# css=css_code,
description=description).launch()
|