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import gradio as gr
import cv2
import numpy as np
from ultralytics import YOLO
import pandas as pd
import pickle
import random
import torch.serialization
# Load the Random Forest model directly from the Space repository
try:
with open("model.pkl", "rb") as f:
rf_model = pickle.load(f)
except Exception as e:
raise Exception(f"Failed to load Random Forest model: {str(e)}")
# Load the YOLO model with safe globals directly from the Space repository
try:
from ultralytics.nn.tasks import DetectionModel
import torch.nn as nn
torch.serialization.add_safe_globals([DetectionModel, nn.Sequential])
yolo_model = YOLO("best.pt")
except Exception as e:
raise Exception(f"Failed to load YOLO model: {str(e)}")
# Simulated sensor data (from simulated_sensors.py)
def simulate_sensor_data(scenario="Safe"):
if scenario == "Safe":
data = {
"temperature": random.uniform(20, 30),
"humidity": random.uniform(30, 50),
"mq2_smoke": random.uniform(0, 100),
"mq135_gas": random.uniform(0, 100),
"flame_detected": False
}
elif scenario == "Gas Leak":
data = {
"temperature": random.uniform(20, 30),
"humidity": random.uniform(30, 50),
"mq2_smoke": random.uniform(100, 300),
"mq135_gas": random.uniform(600, 1000),
"flame_detected": False
}
elif scenario == "Fire Detected":
data = {
"temperature": random.uniform(40, 50),
"humidity": random.uniform(30, 50),
"mq2_smoke": random.uniform(300, 600),
"mq135_gas": random.uniform(100, 300),
"flame_detected": True
}
elif scenario == "Warning":
data = {
"temperature": random.uniform(30, 40),
"humidity": random.uniform(50, 70),
"mq2_smoke": random.uniform(200, 400),
"mq135_gas": random.uniform(300, 500),
"flame_detected": False
}
elif scenario == "Evacuate Immediately":
data = {
"temperature": random.uniform(45, 50),
"humidity": random.uniform(30, 50),
"mq2_smoke": random.uniform(600, 1000),
"mq135_gas": random.uniform(400, 600),
"flame_detected": True
}
else:
data = {
"temperature": 0,
"humidity": 0,
"mq2_smoke": 0,
"mq135_gas": 0,
"flame_detected": False
}
return data
# YOLO inference function
def detect_fire_smoke_from_image(image):
if image is None:
return {"cv_flame_score": 0.0, "cv_smoke_score": 0.0, "person_detected": 0}, image
results = yolo_model(image)
flame_score = 0.0
smoke_score = 0.0
person_detected = 0
for result in results:
for box in result.boxes:
label = result.names[int(box.cls)]
conf = float(box.conf)
if "fire" in label.lower():
flame_score = max(flame_score, conf)
elif "smoke" in label.lower():
smoke_score = max(smoke_score, conf)
elif "person" in label.lower():
person_detected = 1
x1, y1, x2, y2 = map(int, box.xyxy[0])
cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
cv2.putText(image, f"{label} {conf:.2f}", (x1, y1 - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
return {
"cv_flame_score": round(flame_score, 3),
"cv_smoke_score": round(smoke_score, 3),
"person_detected": person_detected
}, image
# Combined prediction function
def predict_system(scenario, image=None):
# Simulate sensor data
sensor_data = simulate_sensor_data(scenario)
# Perform YOLO inference if an image is provided
if image is not None:
vision_data, annotated_image = detect_fire_smoke_from_image(image)
else:
vision_data = {"cv_flame_score": 0.0, "cv_smoke_score": 0.0, "person_detected": 0}
annotated_image = np.zeros((480, 640, 3), dtype=np.uint8)
cv2.putText(annotated_image, "No Image Uploaded", (50, 240),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2)
# Combine sensor and vision data
combined_data = {**sensor_data, **vision_data}
# Prepare features for Random Forest model
features = pd.DataFrame([[
combined_data["temperature"],
combined_data["humidity"],
combined_data["mq2_smoke"],
combined_data["mq135_gas"],
1 if combined_data["flame_detected"] else 0,
combined_data["cv_flame_score"],
combined_data["cv_smoke_score"],
1 if combined_data["person_detected"] else 0
]], columns=[
"temperature", "humidity", "mq2_smoke", "mq135_gas",
"flame_detected", "cv_flame_score", "cv_smoke_score", "person_detected"
])
# Predict threat level
prediction = rf_model.predict(features)[0]
# Format the output
output = f"""
**Threat Level:** {prediction}
**Sensor Data:**
- Temperature: {combined_data["temperature"]:.2f} °C
- Humidity: {combined_data["humidity"]:.2f} %
- MQ2 Smoke: {combined_data["mq2_smoke"]:.2f} ppm
- MQ135 Gas: {combined_data["mq135_gas"]:.2f} ppm
- Flame Detected: {"Yes" if combined_data["flame_detected"] else "No"}
**Vision Data:**
- CV Flame Score: {combined_data["cv_flame_score"] * 100:.2f}%
- CV Smoke Score: {combined_data["cv_smoke_score"] * 100:.2f}%
- Person Detected: {"Yes" if combined_data["person_detected"] else "No"}
"""
return output, annotated_image
# Create Gradio interface
inputs = [
gr.Dropdown(choices=["Safe", "Gas Leak", "Fire Detected", "Warning", "Evacuate Immediately"], label="Scenario", value="Safe"),
gr.Image(type="numpy", label="Upload Image for Vision Detection (Optional)")
]
outputs = [
gr.Textbox(label="System Output"),
gr.Image(type="numpy", label="Annotated Image")
]
gr.Interface(
fn=predict_system,
inputs=inputs,
outputs=outputs,
title="Fire & Gas Leak Detection System",
description="Simulate sensor data and upload an image for vision detection to predict the threat level using a Random Forest model and YOLOv10."
).launch() |