Update app.py

app.py

@@ -3,45 +3,58 @@ import pandas as pd
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import LabelEncoder
 from sklearn.ensemble import RandomForestClassifier
-import os 
+import os
 import gradio as gr
 
-print("Downloading dataset from kagglehub...")
+print("📥 Downloading dataset from KaggleHub...")
 path = kagglehub.dataset_download("ahmeduzaki/earthquake-alert-prediction-dataset")
-csv_files = [f for f in os.listdir(path) if f.endswidth(".csv")]
+
+# ✅ FIX 1: Typo - endswith (not endswidth)
+csv_files = [f for f in os.listdir(path) if f.endswith(".csv")]
+
 if not csv_files:
-    raise FileNotFoundError("No CSV file in the downloaded dataset folder")
+    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}")
+print(f"✅ Using dataset file: {filepath}")
 
+# ✅ FIX 2: Load the dataset safely
 data = pd.read_csv(filepath)
-print("Dataset loaded successfully")
-print("Columns: ", data.columns.tolist())
-
+print("✅ Dataset loaded successfully")
+print("📊 Columns:", data.columns.tolist())
 
+# ✅ FIX 3: y should be a Series, not DataFrame
 X = data[['magnitude', 'depth', 'cdi', 'mmi', 'sig']]
-y = data[['alert']]
+y = data['alert']
 
+# Encode labels
 label_encoder = LabelEncoder()
 y_encoded = label_encoder.fit_transform(y)
+
+# Split data
 X_train, X_test, y_train, y_test = train_test_split(X, y_encoded, test_size=0.2, random_state=42)
+
+# Train model
 rf_model = RandomForestClassifier(
     n_estimators=100,
     random_state=42,
     max_depth=8
 )
 rf_model.fit(X_train, y_train)
-accuracy = rf_model.score(X_test, y_test)
-print(f"Model Accuracy: {accuracy*100:.2f}%")
 
+# Evaluate accuracy
+accuracy = rf_model.score(X_test, y_test)
+print(f"🎯 Model Accuracy: {accuracy * 100:.2f}%")
 
+# Prediction function
 def predict_earthquake_alert(magnitude, depth, cdi, mmi, sig):
-  user_input = pd.DataFrame([[magnitude, depth, cdi, mmi, sig]], columns=['magnitude', 'depth', 'cdi', 'mmi', 'sig'])
-  pred_encoded = rf_model.predict(user_input)[0]
-  pred_label = label_encoder.inverse_transform([pred_encoded])[0]
-  return f"Predicted Eathquake Alert Level: {pred_label}"
-
+    user_input = pd.DataFrame([[magnitude, depth, cdi, mmi, sig]], 
+                              columns=['magnitude', 'depth', 'cdi', 'mmi', 'sig'])
+    pred_encoded = rf_model.predict(user_input)[0]
+    pred_label = label_encoder.inverse_transform([pred_encoded])[0]
+    return f"Predicted Earthquake Alert Level: {pred_label}"
 
+# Gradio interface
 interface = gr.Interface(
     fn=predict_earthquake_alert,
     inputs=[
@@ -51,11 +64,10 @@ interface = gr.Interface(
         gr.Number(label="MMI"),
         gr.Number(label="SIG")
     ],
-    outputs=gr.Textbox(label="prediction"),
-    title="Earthquake Alert Prediction",
-    description="Enter earthquake parameters to predict the alert level using Random Forest Classifier model"
+    outputs=gr.Textbox(label="Prediction"),
+    title="🌍 Earthquake Alert Prediction",
+    description="Enter earthquake parameters to predict the alert level using a Random Forest Classifier model."
 )
 
-
 if __name__ == "__main__":
     interface.launch()