Update app.py

app.py

@@ -1,4 +1,3 @@
-
 import streamlit as st
 import pandas as pd
 import matplotlib.pyplot as plt
@@ -125,4 +124,117 @@ elif regression_type == "Random Forest Regression":
     axes[1].legend(loc='upper right')
     axes[1].set_title('HSV Values')
 
-    st.pyplot(fig)
+    st.pyplot(figst.pyplot(fig)uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
+
+time_frame_options = [
+    "All",
+    "1 second",
+    "5 seconds",
+    "10 seconds",
+    "30 seconds",
+    "1 minute",
+    "5 minutes",
+    "10 minutes",
+    "30 minutes",
+    "60 minutes",
+]
+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)
+
+    # 시간 프레임에 따른 데이터 필터링
+    if time_frame != "All":
+        seconds = {
+            "1 second": 1,
+            "5 seconds": 5,
+            "10 seconds": 10,
+            "30 seconds": 30,
+            "1 minute": 60,
+            "5 minutes": 300,
+            "10 minutes": 600,
+            "30 minutes": 1800,
+            "60 minutes": 3600,
+        }
+        data['timestamp'] = pd.to_datetime(data['timestamp'], unit='ms')
+        data.set_index('timestamp', inplace=True)
+        data = data.resample(f"{seconds[time_frame]}S").mean().dropna().reset_index()
+
+    # 차트 생성
+    fig, axes = plt.subplots(2, 1, figsize=(10, 8))
+
+    # RGB 차트
+    axes[0].plot(data['R'], 'r', label='R')
+    axes[0].plot(data['G'], 'g', label='G')
+    axes[0].plot(data['B'], 'b', label='B')
+    
+# 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
+    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[1].legend(loc='upper right')
+    axes[1].set_title('HSV Values')
+
+