Delete app.py

app.py

@@ -1,69 +0,0 @@
-# -*- coding: utf-8 -*-
-"""2.7 (Optional DBS).ipynb
-
-Automatically generated by Colab.
-
-Original file is located at
-    https://colab.research.google.com/drive/1Js2DNDDuusJx06SBvdh-lmKbuCBTBw9B
-
-chatgpt prompt:
-Please create a regression model to predict DBS base on SGD exchange rate, create the model and then use gradio for the interface
-"""
-
-# Install dependencies (uncomment if needed)
-# !pip install pandas scikit-learn gradio
-
-import pandas as pd
-from sklearn.model_selection import train_test_split
-from sklearn.linear_model import LinearRegression
-from sklearn.metrics import mean_squared_error, r2_score
-import gradio as gr
-
-# 1. LOAD DATA
-# Make sure the CSV file is in the same folder, or give full path
-df = pd.read_csv("DBS_SingDollar.csv (1)")
-
-# If your header row is exactly: Date,DBS,SGD this will work directly
-# Keep only needed columns and drop missing values
-df = df[["DBS", "SGD"]].dropna()
-
-X = df[["SGD"]]   # feature
-y = df["DBS"]     # target
-
-# 2. TRAIN / TEST SPLIT
-X_train, X_test, y_train, y_test = train_test_split(
-    X, y, test_size=0.2, random_state=42
-)
-
-# 3. TRAIN MODEL (Linear Regression)
-model = LinearRegression()
-model.fit(X_train, y_train)
-
-# 4. EVALUATE (optional, shows in console)
-y_pred = model.predict(X_test)
-rmse = mean_squared_error(y_test, y_pred) ** 0.5
-r2 = r2_score(y_test, y_pred)
-
-print(f"RMSE: {rmse:.4f}")
-print(f"R²:   {r2:.4f}")
-
-# 5. PREDICTION FUNCTION FOR GRADIO
-def predict_dbs_price(sgd_rate: float) -> str:
-    """
-    Input: SGD exchange rate (float)
-    Output: predicted DBS price (string for display)
-    """
-    pred = model.predict([[sgd_rate]])[0]
-    return f"Predicted DBS price: {pred:.2f}"
-
-# 6. GRADIO INTERFACE
-iface = gr.Interface(
-    fn=predict_dbs_price,
-    inputs=gr.Number(label="SGD Exchange Rate"),
-    outputs=gr.Textbox(label="Predicted DBS Price"),
-    title="DBS Price Predictor",
-    description="Enter the SGD exchange rate to predict the DBS share price (trained with historical DBS & SGD data)."
-)
-
-iface.launch()
-