Update app.py

app.py

@@ -239,3 +239,43 @@ elif regression_type == "Random Forest Regression":
     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')
+
+    # 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)
+
+    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...