Update app.py

app.py

@@ -2,7 +2,12 @@
 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")
 
@@ -22,11 +27,20 @@ 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
+    # Read CSV file
     data = pd.read_csv(uploaded_file)
 
-    # 시간 프레임에 따른 데이터 필터링
+    # Filter data according to the time frame
     if time_frame != "All":
         seconds = {
             "1 second": 1,
@@ -43,21 +57,59 @@ if uploaded_file is not None:
         data.set_index('timestamp', inplace=True)
         data = data.resample(f"{seconds[time_frame]}S").mean().dropna().reset_index()
 
-    # 차트 생성
+    # Create charts
     fig, axes = plt.subplots(2, 1, figsize=(10, 8))
 
-    # RGB 차트
+    # RGB chart
     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')
-    axes[0].set_title('RGB Values')
 
-    # HSV 차트
+    # HSV chart
     axes[1].plot(data['H'], 'r', label='H')
     axes[1].plot(data['S'], 'g', label='S')
     axes[1].plot(data['V'], 'b', label='V')
+
+    axes[0].legend(loc='upper right')
+    axes[0].set_title('RGB Values')
     axes[1].legend(loc='upper right')
     axes[1].set_title('HSV Values')
 
+    # Perform regression analysis if selected
+    if regression_type != "None":
+        X = np.arange(len(data)).reshape(-1, 1)
+
+        # Linear Regression
+        if regression_type == "Linear Regression":
+            model = LinearRegression()
+            for color, code in zip(['R', 'G', 'B'], ['r', 'g', 'b']):
+                model.fit(X, data[color])
+                axes[0].plot(X, model.predict(X), f'{code}--')
+                st.write(f"{color}: y = {model.coef_[0]} * x + {model.intercept_}")
+
+        # Polynomial Regression
+        elif regression_type == "Polynomial Regression":
+            polynomial_features = PolynomialFeatures(degree=2)
+            model = make_pipeline(polynomial_features, LinearRegression())
+            for color, code in zip(['R', 'G', 'B'], ['r', 'g', 'b']):
+                model.fit(X, data[color])
+                axes[0].plot(X, model.predict(X), f'{code}--')
+            st.write("Polynomial regression equation is not easily representable.")
+
+        # SVR (Support Vector Regression)
+        elif regression_type == "SVR (Support Vector Regression)":
+            model = SVR()
+            for color, code in zip(['R', 'G', 'B'], ['r', 'g', 'b']):
+                model.fit(X, data[color])
+                axes[0].plot(X, model.predict(X), f'{code}--')
+            st.write("SVR equation is not easily representable.")
+
+        # Random Forest Regression
+        elif regression_type == "Random Forest Regression":
+            model = RandomForestRegressor()
+            for color, code in zip(['R', 'G', 'B'], ['r', 'g', 'b']):
+                model.fit(X, data[color])
+                axes[0].plot(X, model.predict(X), f'{code}--')
+            st.write("Random Forest equation is not easily representable.")
+
     st.pyplot(fig)