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import kagglehub
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.ensemble import RandomForestClassifier
import gradio as gr
import os
print("Downloading Data from Kaggle...")
path = kagglehub.dataset_download("ahmeduzaki/earthquake-alert-prediction-dataset")
# print("Path to dataset files:", path)
csv_files = [f for f in os.listdir(path) if f.endswith('.csv')]
if not csv_files:
raise FileNotFoundError("No CSV file found in the downloaded dataset folder")
filepath = os.path.join(path,csv_files[0])
print(f"Using dataset file: {filepath}")
df = pd.read_csv(filepath)
print("Dataset loaded Succsessfully")
print("Columns: ", df.columns.tolist())
X = df.drop("alert", axis=1)
y = df["alert"]
label_encoder = LabelEncoder()
y_encoded = label_encoder.fit_transform(y)
X_train, X_test, y_train, y_test = train_test_split(X, y_encoded, test_size=0.2, random_state=42)
rf_model = RandomForestClassifier(
n_estimators=100,
random_state=42,
max_depth=8
)
rf_model.fit(X_train, y_train)
accouracy = rf_model.score(X_test, y_test)
print(f"Model Accouracy: {accouracy * 100:.2f}%")
def Earthquak_predection(magnitude,depth,cdi,mmi,sig):
user_data_input = pd.DataFrame([[magnitude, depth, cdi, mmi, sig]], columns=['magnitude','depth','cdi','mmi','sig'])
pred_encoded = rf_model.predict(user_data_input)[0]
pred_label = label_encoder.inverse_transform([pred_encoded])[0]
return pred_label
interface = gr.Interface(
fn=Earthquak_predection,
inputs=[
gr.Number(label="Magnitude"),
gr.Number(label="Depth"),
gr.Number(label="Community Intensity (cdi)"),
gr.Number(label="Modified Mercalli Intensity (mmi)"),
gr.Number(label="Significance")
],
outputs=gr.Textbox(label="Predicted Earthquake Alert"),
title="Earthquake Alert Prediction",
description="Enter the earthquake parameters to predict the alert level using Random Forest Classifier Model."
)
if __name__ == '__main__':
interface.launch()
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