Spaces:
Sleeping
Sleeping
Update app.py
Browse files
app.py
CHANGED
@@ -1,3 +1,51 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
def predict(data):
|
2 |
try:
|
3 |
image_input = data.get('image', None)
|
|
|
1 |
+
import gradio as gr
|
2 |
+
import json
|
3 |
+
import torch
|
4 |
+
from torch import nn
|
5 |
+
from torchvision import models, transforms
|
6 |
+
from huggingface_hub import hf_hub_download
|
7 |
+
from PIL import Image
|
8 |
+
import requests
|
9 |
+
import base64
|
10 |
+
from io import BytesIO
|
11 |
+
import os
|
12 |
+
|
13 |
+
# Define the number of classes
|
14 |
+
num_classes = 2
|
15 |
+
|
16 |
+
# Download model from Hugging Face
|
17 |
+
def download_model():
|
18 |
+
try:
|
19 |
+
model_path = hf_hub_download(repo_id="jays009/Restnet50", filename="pytorch_model.bin")
|
20 |
+
return model_path
|
21 |
+
except Exception as e:
|
22 |
+
print(f"Error downloading model: {e}")
|
23 |
+
return None
|
24 |
+
|
25 |
+
# Load the model from Hugging Face
|
26 |
+
def load_model(model_path):
|
27 |
+
try:
|
28 |
+
model = models.resnet50(pretrained=False)
|
29 |
+
model.fc = nn.Linear(model.fc.in_features, num_classes)
|
30 |
+
model.load_state_dict(torch.load(model_path, map_location=torch.device("cpu")))
|
31 |
+
model.eval()
|
32 |
+
return model
|
33 |
+
except Exception as e:
|
34 |
+
print(f"Error loading model: {e}")
|
35 |
+
return None
|
36 |
+
|
37 |
+
# Download the model and load it
|
38 |
+
model_path = download_model()
|
39 |
+
model = load_model(model_path) if model_path else None
|
40 |
+
|
41 |
+
# Define the transformation for the input image
|
42 |
+
transform = transforms.Compose([
|
43 |
+
transforms.Resize(256),
|
44 |
+
transforms.CenterCrop(224),
|
45 |
+
transforms.ToTensor(),
|
46 |
+
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
|
47 |
+
])
|
48 |
+
|
49 |
def predict(data):
|
50 |
try:
|
51 |
image_input = data.get('image', None)
|