Update app.py

app.py

@@ -3,6 +3,12 @@ import streamlit as st
 import pandas as pd
 import matplotlib.pyplot as plt
 import io
+import numpy as np
+from sklearn.linear_model import LinearRegression
+from sklearn.preprocessing import PolynomialFeatures
+from sklearn.pipeline import make_pipeline
+from sklearn.svm import SVR
+from sklearn.ensemble import RandomForestRegressor
 
 st.title("Webcam Color Detection Charting")
 
@@ -22,6 +28,15 @@ time_frame_options = [
 ]
 time_frame = st.selectbox("Data Time Frame", time_frame_options)
 
+
+regression_options = [
+    "None",
+    "Linear Regression",
+    "Polynomial Regression",
+    "SVR (Support Vector Regression)",
+    "Random Forest Regression",
+]
+regression_type = st.selectbox("Regression Analysis Type", regression_options)
 if uploaded_file is not None:
     # CSV 파일 읽기
     data = pd.read_csv(uploaded_file)
@@ -50,7 +65,57 @@ if uploaded_file is not None:
     axes[0].plot(data['R'], 'r', label='R')
     axes[0].plot(data['G'], 'g', label='G')
     axes[0].plot(data['B'], 'b', label='B')
-    axes[0].legend(loc='upper right')
+    
+# 회귀 분석 수행
+X = np.arange(len(data)).reshape(-1, 1)
+
+# 선형 회귀
+if regression_type == "Linear Regression":
+    model = LinearRegression()
+    model.fit(X, data['R'])
+    axes[0].plot(X, model.predict(X), 'r--')
+    st.write(f"R: y = {model.coef_[0]} * x + {model.intercept_}")
+    model.fit(X, data['G'])
+    axes[0].plot(X, model.predict(X), 'g--')
+    st.write(f"G: y = {model.coef_[0]} * x + {model.intercept_}")
+    model.fit(X, data['B'])
+    axes[0].plot(X, model.predict(X), 'b--')
+    st.write(f"B: y = {model.coef_[0]} * x + {model.intercept_}")
+
+# 다항 회귀
+elif regression_type == "Polynomial Regression":
+    polynomial_features = PolynomialFeatures(degree=2)
+    model = make_pipeline(polynomial_features, LinearRegression())
+    model.fit(X, data['R'])
+    axes[0].plot(X, model.predict(X), 'r--')
+    model.fit(X, data['G'])
+    axes[0].plot(X, model.predict(X), 'g--')
+    model.fit(X, data['B'])
+    axes[0].plot(X, model.predict(X), 'b--')
+    st.write("Polynomial regression equation is not easily representable.")
+
+# SVR (Support Vector Regression)
+elif regression_type == "SVR (Support Vector Regression)":
+    model = SVR()
+    model.fit(X, data['R'])
+    axes[0].plot(X, model.predict(X), 'r--')
+    model.fit(X, data['G'])
+    axes[0].plot(X, model.predict(X), 'g--')
+    model.fit(X, data['B'])
+    axes[0].plot(X, model.predict(X), 'b--')
+    st.write("SVR equation is not easily representable.")
+
+# Random Forest Regression
+elif regression_type == "Random Forest Regression":
+    model = RandomForestRegressor()
+    model.fit(X, data['R'])
+    axes[0].plot(X, model.predict(X), 'r--')
+    model.fit(X, data['G'])
+    axes[0].plot(X, model.predict(X), 'g--')
+    model.fit(X, data['B'])
+    axes[0].plot(X, model.predict(X), 'b--')
+    st.write("Random Forest equation is not easily representable.")
+axes[0].legend(loc='upper right')
     axes[0].set_title('RGB Values')
 
     # HSV 차트