Update app.py

app.py

@@ -1,67 +1,122 @@
+
 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
-import matplotlib.pyplot as plt
+from sklearn.pipeline import make_pipeline
+from sklearn.svm import SVR
+from sklearn.ensemble import RandomForestRegressor
+
+st.title("Webcam Color Detection Charting")
+
+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:
+    # Read CSV file
+    data = pd.read_csv(uploaded_file)
+
+    # Filter data according to the time frame
+    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()
+
+    # Create charts
+    fig, axes = plt.subplots(2, 1, figsize=(10, 8))
+
+    # RGB chart
+    
+color_space_options = ["RGB", "HSV"]
+color_space = st.selectbox("Select Color Space", color_space_options)
+
+# Depending on the selection, plot the corresponding values
+if color_space == "RGB":
+    axes[0].plot(data['R'], 'r', label='R')
+    axes[0].plot(data['G'], 'g', label='G')
+    axes[0].plot(data['B'], 'b', label='B')
+elif color_space == "HSV":
+    axes[0].plot(data['H'], 'r', label='H')
+    axes[0].plot(data['S'], 'g', label='S')
+    axes[0].plot(data['V'], 'b', label='V')
+axes[0].legend(loc='upper right')
+
+
+    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.")
 
-def load_data(file):
-    return pd.read_csv(file)
-
-def plot_data(data, x_values, y_values, model=None, prediction=None):
-    plt.scatter(x_values, y_values, label='Data')
-    if model is not None and prediction is not None:
-        plt.plot(x_values, prediction, color='red', label='Model')
-    plt.xlabel('Index')
-    plt.ylabel('Value')
-    plt.legend()
-    plt.show()
-
-def fit_model(data, model_type, x_values, y_values):
-    if model_type == 'Linear Regression':
-        model = LinearRegression()
-        x_values = x_values.reshape(-1, 1)
-        model.fit(x_values, y_values)
-        prediction = model.predict(x_values)
-        equation = f'y = {model.coef_[0]:.4f}x + {model.intercept_:.4f}'
-    elif model_type == 'Polynomial Regression':
-        polynomial_features = PolynomialFeatures(degree=2)
-        x_values_poly = polynomial_features.fit_transform(x_values.reshape(-1, 1))
-        model = LinearRegression()
-        model.fit(x_values_poly, y_values)
-        prediction = model.predict(x_values_poly)
-        equation = 'Polynomial equation (degree 2)'
-    else:
-        model = None
-        prediction = None
-        equation = "No model selected"
-    return model, prediction, equation
-
-def app():
-    st.title('RGB and HSV Analysis and Prediction')
-    uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
-
-    if uploaded_file is not None:
-        data = load_data(uploaded_file)
-        st.dataframe(data.head())
-        
-        # Selecting R, G, B, H, S, V
-        color_component = st.selectbox("Select color component", ['R', 'G', 'B', 'H', 'S', 'V'])
-        st.write(f"Selected component: {color_component}")
-        selected_data = data[color_component].values
-        
-        # Selecting regression model
-        regression_model = st.selectbox("Select a regression model", ["None", "Linear Regression", "Polynomial Regression"])
-        
-        x_values = np.arange(len(selected_data))
-        y_values = selected_data
-
-        # Fitting the selected model
-        model, prediction, equation = fit_model(data, regression_model, x_values, y_values)
-        st.write(f"Equation: {equation}")
-        
-        # Plotting the data and model
-        plot_data(data, x_values, y_values, model, prediction)
-
-# Uncomment the next line to run the app locally
-# app()
+    st.pyplot(fig)