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eeaaec3 | 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 | import gradio as gr
import torch
import torch.nn as nn
import cv2
import numpy as np
# --------------------
# Model Definition
# --------------------
class FireCNN(nn.Module):
def __init__(self, num_classes=3):
super(FireCNN, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 16, 3, padding=1),
nn.BatchNorm2d(16),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Conv2d(16, 32, 3, padding=1),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Conv2d(32, 64, 3, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Conv2d(64, 128, 3, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.MaxPool2d(2),
)
self.classifier = nn.Sequential(
nn.Flatten(),
nn.Linear(128 * 8 * 8, 128),
nn.ReLU(),
nn.Dropout(0.3),
nn.Linear(128, num_classes)
)
def forward(self, x):
x = self.features(x)
x = self.classifier(x)
return x
# --------------------
# Load Model
# --------------------
checkpoint = torch.load("fire_model.pth", map_location="cpu")
model = FireCNN()
model.load_state_dict(checkpoint["model_state_dict"])
model.eval()
IMG_SIZE = checkpoint["img_size"]
# --------------------
# Prediction Function
# --------------------
def predict(image):
img = cv2.resize(image, (IMG_SIZE, IMG_SIZE))
img = img / 255.0
img = np.transpose(img, (2, 0, 1))
img = torch.tensor(img, dtype=torch.float32).unsqueeze(0)
with torch.no_grad():
outputs = model(img)
probs = torch.softmax(outputs, dim=1).squeeze().numpy()
return {
"fire": float(probs[0]),
"smoke": float(probs[1]),
"non_fire": float(probs[2])
}
# --------------------
# Gradio Interface
# --------------------
demo = gr.Interface(
fn=predict,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(num_top_classes=3),
title="🔥 Fire / Smoke Detection",
description="Upload an image to detect Fire, Smoke, or Non-Fire"
)
demo.launch() |