Update app.py

app.py

@@ -2,6 +2,11 @@
 import streamlit as st
 import pandas as pd
 import matplotlib.pyplot as plt
+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")
 
@@ -52,3 +57,38 @@ if uploaded_file is not None:
 
     ax.legend(loc='upper left')
     st.pyplot(fig)
+
+    # Selecting target and features
+    target_column = st.selectbox("Select Target Column", options=selected_columns)
+    feature_columns = st.multiselect("Select Feature Columns", options=[col for col in selected_columns if col != target_column])
+
+    # Defining models
+    models = {
+        "Linear Regression": LinearRegression(),
+        "Polynomial Regression": make_pipeline(PolynomialFeatures(degree=2), LinearRegression()),
+        "SVR (Support Vector Regression)": SVR(),
+        "Random Forest Regression": RandomForestRegressor()
+    }
+
+    # Selecting model
+    selected_model = st.selectbox("Select Regression Model", options=list(models.keys()))
+
+    # Fitting the model
+    if st.button("Fit Model"):
+        if feature_columns: # Check if feature columns are selected
+            X = data[feature_columns]
+            y = data[target_column]
+            model = models[selected_model]
+            model.fit(X, y)
+
+            # Predicting and plotting
+            predictions = model.predict(X)
+            fig, ax = plt.subplots(figsize=(10, 5))
+            ax.plot(y, label="Actual")
+            ax.plot(predictions, label="Predicted")
+            ax.legend(loc='upper left')
+            st.pyplot(fig)
+        else:
+            st.error("Please select at least one feature column.")
+else:
+    st.warning("Please upload a CSV file.")