Spaces:
Sleeping
Sleeping
File size: 3,404 Bytes
8fb7712 |
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 |
import gradio as gr
import pandas as pd
import joblib
import matplotlib.pyplot as plt
import seaborn as sns
# Load pre-trained models and scalers
scaler_initial = joblib.load("scaler_initial.pkl")
scaler_with_cluster = joblib.load("scaler_with_cluster.pkl")
kmeans = joblib.load("kmeans.pkl")
linear_model = joblib.load("linear_model.pkl")
poly_model = joblib.load("poly_model.pkl")
ridge_model = joblib.load("ridge_model.pkl")
rf_regressor = joblib.load("rf_regressor.pkl")
logistic_model = joblib.load("logistic_model.pkl")
rf_classifier = joblib.load("rf_classifier.pkl")
# Prediction function
def predict_aqi(pm25, pm10, no2, co, temp, humidity):
# Create input dataframe with initial features
input_data = pd.DataFrame([[pm25, pm10, no2, co, temp, humidity]],
columns=["PM2.5", "PM10", "NO2", "CO", "Temperature", "Humidity"])
# Scale initial features for clustering
input_scaled_initial = scaler_initial.transform(input_data)
# Apply K-means clustering
cluster = kmeans.predict(input_scaled_initial)[0]
input_data['Cluster'] = cluster
# Scale data with Cluster feature
input_scaled_with_cluster = scaler_with_cluster.transform(input_data)
# Regression predictions
linear_pred = linear_model.predict(input_scaled_with_cluster)[0]
poly_pred = poly_model.predict(input_scaled_with_cluster)[0]
ridge_pred = ridge_model.predict(input_scaled_with_cluster)[0]
rf_pred = rf_regressor.predict(input_scaled_with_cluster)[0]
# Classification predictions
logistic_class = logistic_model.predict(input_scaled_with_cluster)[0]
rf_class = rf_classifier.predict(input_scaled_with_cluster)[0]
# Create performance plot
models = ["Linear", "Polynomial", "Ridge", "Random Forest"]
predictions = [linear_pred, poly_pred, ridge_pred, rf_pred]
plt.figure(figsize=(8, 4))
sns.barplot(x=models, y=predictions)
plt.title("AQI Predictions by Model")
plt.ylabel("Predicted AQI")
plt.savefig("aqi_plot.png")
plt.close()
output_text = (
f"Linear Regression AQI: {linear_pred:.2f}\n"
f"Polynomial Regression AQI: {poly_pred:.2f}\n"
f"Ridge Regression AQI: {ridge_pred:.2f}\n"
f"Random Forest AQI: {rf_pred:.2f}\n"
f"Logistic Classification: {'Safe' if logistic_class == 0 else 'Unsafe'}\n"
f"Random Forest Classification: {'Safe' if rf_class == 0 else 'Unsafe'}"
)
return output_text, "aqi_plot.png"
# Gradio interface
iface = gr.Interface(
fn=predict_aqi,
inputs=[
gr.Slider(0, 200, label="PM2.5 (µg/m³)", value=50),
gr.Slider(0, 300, label="PM10 (µg/m³)", value=80),
gr.Slider(0, 100, label="NO2 (µg/m³)", value=20),
gr.Slider(0, 10, label="CO (mg/m³)", value=1),
gr.Slider(-10, 40, label="Temperature (°C)", value=20),
gr.Slider(0, 100, label="Humidity (%)", value=50)
],
outputs=[
gr.Textbox(label="Predictions"),
gr.Image(label="Model Comparison Plot")
],
title="Air Quality Prediction and Classification",
description="Enter pollutant levels and weather conditions to predict AQI and classify air quality. Built with multiple machine learning models to address urban air pollution."
)
if __name__ == "__main__":
iface.launch() |