Upload app.py
Browse files
app.py
ADDED
@@ -0,0 +1,29 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import os
|
2 |
+
|
3 |
+
import cv2
|
4 |
+
import gradio as gr
|
5 |
+
import numpy as np
|
6 |
+
import tensorflow as tf
|
7 |
+
from huggingface_hub import from_pretrained_keras
|
8 |
+
|
9 |
+
MODEL_NAME = os.getenv("MODEL_NAME")
|
10 |
+
HF_TOKEN = os.getenv("HF_TOKEN")
|
11 |
+
|
12 |
+
model = from_pretrained_keras(MODEL_NAME, token=HF_TOKEN)
|
13 |
+
|
14 |
+
|
15 |
+
def classify_image(inp):
|
16 |
+
inp = cv2.resize(inp, (299, 299))
|
17 |
+
inp = np.expand_dims(inp, axis=0)
|
18 |
+
prediction = model.predict(inp)
|
19 |
+
pred = tf.nn.sigmoid(prediction).numpy().squeeze()
|
20 |
+
confidences = {"Application": 1 - pred, "Product": pred.item()}
|
21 |
+
return confidences
|
22 |
+
|
23 |
+
|
24 |
+
gr.Interface(
|
25 |
+
fn=classify_image,
|
26 |
+
inputs=gr.Image(),
|
27 |
+
outputs=gr.Label(num_top_classes=2),
|
28 |
+
allow_flagging="never",
|
29 |
+
).launch(debug=True, enable_queue=True)
|