Update app.py

app.py

@@ -2,12 +2,6 @@
 import streamlit as st
 import pandas as pd
 import matplotlib.pyplot as plt
-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")
 
@@ -27,15 +21,6 @@ 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:
     # Read CSV file
     data = pd.read_csv(uploaded_file)
@@ -57,59 +42,13 @@ if uploaded_file is not None:
         data.set_index('timestamp', inplace=True)
         data = data.resample(f"{seconds[time_frame]}S").mean().dropna().reset_index()
 
-    # Create charts
-    fig, axes = plt.subplots(2, 1, figsize=(10, 8))
-
-    # 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')
-
-    # 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.")
+    # Let the user select the columns
+    selected_columns = st.multiselect("Select Columns", options=['R', 'G', 'B', 'H', 'S', 'V'])
 
-        # 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.")
+    # Create charts based on selected columns
+    fig, ax = plt.subplots(figsize=(10, 5))
+    for col in selected_columns:
+        ax.plot(data[col], label=col)
 
+    ax.legend(loc='upper left')
     st.pyplot(fig)