Spaces:
Runtime error
Runtime error
File size: 2,822 Bytes
3ac7250 c970970 3ac7250 c970970 3ac7250 c970970 b27d380 c970970 4809d91 c970970 b27d380 4809d91 c970970 3ac7250 4809d91 3ac7250 c970970 7987c06 c970970 4809d91 db8bdd5 c970970 |
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 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 |
from hubconf import custom
model = custom(path_or_model='best.pt') # custom example
model.eval()
# model = create(name='yolov7', pretrained=True, channels=3, classes=80, autoshape=True) # pretrained example
# Verify inference
import numpy as np
import torch
from PIL import Image
import gradio as gr
# imgs = [np.zeros((640, 480, 3))]
# imgs = 'inference/images/meal.jpg'
# results = model(imgs) # batched inference
# results.print()
# results.save()
def total_price(predicted):
price = 0
for name, confidence in predicted:
if name == "side dish":
price += 10
elif name == "purple rice" or name == "white rice" or name == "brown rice":
price += 20
elif name == "40dollars meal":
price += 40
elif name == "30dollars meal":
price += 30
elif name == "25dollars meal":
price += 25
return price
def predict(input_image):
"""
Predict model output
"""
# Disable gradient computation
with torch.no_grad():
results = model(input_image)
results_pd = results.pandas().xyxy[0]
name, confidence = results_pd["name"], results_pd["confidence"]
predicted = list(zip(name, confidence))
output_image = results.render()[0]
price = total_price(predicted)
# Return the output image and price
return [output_image, price]
with gr.Blocks() as demo:
# Title
gr.HTML(
"""
<h1 align="center">Group 29 - AI Cafeteria Price Evaluator</h1>
""")
examples = ["./examples/img_1.jpg", "./examples/img_2.jpg", "./examples/img_3.jpg",
"./examples/img_4.jpg", "./examples/img_5.jpg", "./examples/img_6.jpg"]
# gr.Interface(inputs=["image"],outputs=["image"],fn=lambda img:model(img).render()[0]).launch()
gr.Interface(inputs=["image"], outputs=["image", "text"], fn=predict, examples=examples)
gr.HTML(
"""
<style>
table.center {
margin-left: auto;
margin-right: auto;
}
.center th{
text-align: center;
}
</style>
<h1 align="center">Price List</h1>
<h3>
<table class="center">
<tr>
<th style="text-align: center">Menu</th>
<th style="text-align: center">Price</th>
</tr>
<tr>
<td>White/Brown/Purple Rice</td>
<td>20</td>
</tr>
<tr>
<td>Side Dish</td>
<td>10</td>
</tr>
<tr>
<td>25 Dollar Meal</td>
<td>25</td>
</tr>
<tr>
<td>30 Dollar Meal</td>
<td>30</td>
</tr>
<tr>
<td>40 Dollar Meal</td>
<td>40</td>
</tr>
</table>
</h3>
""")
if __name__ == "__main__":
demo.launch()
|