rollback

app.py

@@ -3,12 +3,6 @@ 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")
 
@@ -28,128 +22,6 @@ 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)
-
-    # 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')
-
-    st.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)
@@ -178,81 +50,6 @@ 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')
-    
-# 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')
-
-    
-
-if uploaded_file is not None:
-    # CSV 파일 읽기
-    data = pd.read_csv(uploaded_file)
-    st.write("Data Preview:")
-    st.write(data.head())
-
-    # 데이터 시각화 (RGB & HSV)
-    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')
     axes[0].legend(loc='upper right')
     axes[0].set_title('RGB Values')
 
@@ -264,18 +61,3 @@ if uploaded_file is not None:
     axes[1].set_title('HSV Values')
 
     st.pyplot(fig)
-
-    X = np.arange(len(data)).reshape(-1, 1)
-    
-    # Linear Regression
-    if regression_type == "Linear Regression":
-        model = 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(f"Linear equation: y = {model.coef_[0]} * x + {model.intercept_}")
-    
-    # Other regression types...