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7689141 169d309 7689141 169d309 05f67c3 f23bb1e 05f67c3 f23bb1e 05f67c3 f23bb1e 7689141 f23bb1e 169d309 f23bb1e ce22489 82b7788 ce22489 169d309 7689141 a174a2c 7689141 3c39f03 7689141 8200b06 7689141 f23bb1e | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 | import gradio as gr
import torch
import torch.nn as nn
from torchvision import transforms
from torchvision.models import swin_t
from PIL import Image
# 🔧 Model definition
class MMIM(nn.Module):
def __init__(self, num_classes=36):
super(MMIM, self).__init__()
self.backbone = swin_t(weights='IMAGENET1K_V1')
self.backbone.head = nn.Identity()
self.classifier = nn.Sequential(
nn.Linear(768, 512),
nn.ReLU(),
nn.Dropout(0.3),
nn.Linear(512, num_classes)
)
def forward(self, x):
features = self.backbone(x)
return self.classifier(features)
# ✅ Load model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = MMIM(num_classes=36)
# 🧠 Load only matching weights from checkpoint (skip classifier mismatch)
checkpoint = torch.load("MMIM_best.pth", map_location=device)
filtered_checkpoint = {
k: v for k, v in checkpoint.items() if k in model.state_dict() and model.state_dict()[k].shape == v.shape
}
model.load_state_dict(filtered_checkpoint, strict=False)
model.to(device)
model.eval()
# ✅ class_names mapped according to confusion matrix order
class_names = [
"Chinee apple", # class1
"Black grass", # class14
"Charlock", # class15
"Cleavers", # class16
"Common Chickweed", # class17
"Common Wheat", # class18
"Fat Hen", # class19
"Lanthana", # class2
"Loose Silky bent", # class20
"Maize", # class21
"Scentless Mayweed", # class22
"Shepherds Purse", # class23
"Small-Flowered Cranesbill", # class24
"Sugar beet", # class25
"Carpetweeds", # class26
"Crabgrass",# class27
"Eclipta", # class28
"Goosegrass", # class29
"Negative", # class3
"Morningglory", # class30
"Nutsedge", # class31
"Palmer Amarnath", # class32
"Prickly Sida", # class33
"Purslane", # class34
"Ragweed", # class35
"Sicklepod", # class36
"SpottedSpurge", # class37
"SpurredAnoda", # class38
"Swinecress", # class39
"Parkinsonia", # class4
"Waterhemp", # class40
"Parthenium", # class5
"Prickly acacia", # class6
"Rubber vine", # class7
"Siam weed", # class8
"Snake weed", # class9
]
# 🔁 Image transform
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor()
])
# 🔍 Prediction function
def predict(img):
img = img.convert('RGB')
img_tensor = transform(img).unsqueeze(0).to(device)
with torch.no_grad():
outputs = model(img_tensor)
probs = torch.softmax(outputs, dim=1)
conf, pred = torch.max(probs, 1)
predicted_class = class_names[pred.item()]
confidence = conf.item() * 100
if predicted_class.lower() == "negative":
return f"⚠️ This image is predicted as Negative.\nConfidence: {confidence:.2f}%"
return f"✅ Predicted class: {predicted_class}\nConfidence: {confidence:.2f}%"
# 🎨 Gradio Interface
interface = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil"),
outputs="text",
title="Weed Image Classifier",
description="Upload a weed image to predict its class."
)
interface.launch() |