Update app.py

app.py

@@ -1,115 +1,68 @@
 
 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")
-
-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
-    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.")
-
-        # 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.")
+import matplotlib.pyplot as plt
 
-    st.pyplot(fig)
+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)
+
+# Running the app (uncomment this line to run the app locally)
+# app()