Update app.py

app.py

@@ -7,18 +7,17 @@ from sklearn.decomposition import PCA
 from scipy.signal import savgol_filter
 from math import pi
 
-# Ensure interactive backend for plotting
-plt.switch_backend('agg')
+plt.switch_backend('agg')  # Required for headless environments
 
 # Load dataset
 df = pd.read_csv("milk_absorbance.csv")
 df.rename(columns={df.columns[0]: 'Label'}, inplace=True)
 
-# Gradio plot functions
+# Function to generate all plots
 def plot_all():
     plots = []
 
-    # Plot 1: Mean Spectra per Class
+    # 1. Mean Spectra per Class
     fig1 = plt.figure(figsize=(12, 6))
     for label in df['Label'].unique():
         class_df = df[df['Label'] == label]
@@ -32,7 +31,7 @@ def plot_all():
     plt.tight_layout()
     plots.append(fig1)
 
-    # Plot 2: Offset Mean Spectra
+    # 2. Offset Mean Spectra
     fig2 = plt.figure(figsize=(12, 6))
     offset_step = 0.1
     for i, label in enumerate(df['Label'].unique()):
@@ -40,15 +39,15 @@ def plot_all():
         mean_spectrum = class_df.iloc[:, 1:].mean()
         offset = i * offset_step
         plt.plot(mean_spectrum.index.astype(int), mean_spectrum + offset, label=f'Label {label}')
-    plt.title('Mean NIR Spectrum per Milk Ratio Class (with Offset)')
+    plt.title('Offset Mean NIR Spectra')
     plt.xlabel('Wavelength (nm)')
-    plt.ylabel('Absorbance (Offset Applied)')
-    plt.legend(title='Class (Milk Ratio)')
+    plt.ylabel('Offset Absorbance')
+    plt.legend()
     plt.grid(True)
     plt.tight_layout()
     plots.append(fig2)
 
-    # Plot 3: Radar Plot
+    # 3. Radar Plot
     fig3 = plt.figure(figsize=(8, 8))
     ax = plt.subplot(111, polar=True)
     subset_cols = df.columns[1:][::20]
@@ -63,12 +62,12 @@ def plot_all():
         ax.fill(angles, values, alpha=0.1)
     ax.set_xticks(angles[:-1])
     ax.set_xticklabels(subset_cols.astype(int))
-    plt.title('Radar Plot of Mean Spectra (Subset Wavelengths)')
+    plt.title('Radar Plot of Mean Spectra (Subset)')
     plt.legend(loc='upper right', bbox_to_anchor=(1.3, 1.1))
     plt.tight_layout()
     plots.append(fig3)
 
-    # Plot 4: Cumulative PCA Explained Variance
+    # 4. PCA Cumulative Variance
     fig4 = plt.figure(figsize=(8, 5))
     X = df.iloc[:, 1:].values
     X_scaled = StandardScaler().fit_transform(X)
@@ -78,14 +77,14 @@ def plot_all():
     plt.plot(range(1, 21), explained, marker='o')
     plt.axhline(y=0.95, color='r', linestyle='--', label='95% Variance')
     plt.title('Cumulative Explained Variance by PCA')
-    plt.xlabel('Number of Principal Components')
+    plt.xlabel('Principal Components')
     plt.ylabel('Cumulative Variance')
     plt.legend()
     plt.grid(True)
     plt.tight_layout()
     plots.append(fig4)
 
-    # Plot 5: Derivative + Normalized Spectra
+    # 5. Derivative + Normalized
     fig5 = plt.figure(figsize=(16, 8))
     y_vals = df['Label'].values
     wavelengths = df.columns[1:].astype(float)
@@ -99,38 +98,49 @@ def plot_all():
         indices = np.where(y_vals == label)[0]
         for i in indices:
             plt.plot(wavelengths, X_deriv_norm[i], color=color, alpha=0.3, label=f'Milk {label}' if i == indices[0] else '')
-    plt.title("All Spectra After First Derivative + Normalization")
+    plt.title("Spectra After 1st Derivative + Normalization")
     plt.xlabel("Wavelength (nm)")
-    plt.ylabel("Normalized First Derivative")
-    plt.legend(title="Group")
+    plt.ylabel("Normalized Derivative")
+    plt.legend(title="Class")
     plt.grid(True)
     plt.tight_layout()
     plots.append(fig5)
 
-    # Plot 6: Derivative Only (No Norm)
+    # 6. Derivative Only (No Norm)
     fig6 = plt.figure(figsize=(16, 8))
     for label, color in zip(unique_labels, colors):
         indices = np.where(y_vals == label)[0]
         for i in indices:
             plt.plot(wavelengths, X_deriv[i], color=color, alpha=0.3, label=f'Milk {label}' if i == indices[0] else '')
-    plt.title("All Spectra After First Derivative (No Normalization)")
+    plt.title("Spectra After 1st Derivative (No Normalization)")
     plt.xlabel("Wavelength (nm)")
-    plt.ylabel("First Derivative Absorbance")
-    plt.legend(title="Group")
+    plt.ylabel("Derivative Absorbance")
+    plt.legend(title="Class")
     plt.grid(True)
     plt.tight_layout()
     plots.append(fig6)
 
-    return [gr.Plot(plt_fig) for plt_fig in plots]
+    return plots
 
-# Gradio UI for Dataset Description
+# Gradio UI
 with gr.Blocks() as demo:
     gr.Markdown("# 🧪 Dataset Description")
-    gr.DataFrame(df.head(50), label="Preview of Raw Data")
-    plot_button = gr.Button("Generate Spectroscopy Visualizations")
-    out_gallery = gr.Gallery(label="All Plots", columns=2)
-    plot_button.click(fn=plot_all, inputs=[], outputs=out_gallery)
+    gr.DataFrame(df.head(10), label="📋 Preview of Milk Spectroscopy Data")
 
-demo.launch(server_name="0.0.0.0", server_port=7860, ssr_mode=False)
+    plot_button = gr.Button("📊 Generate Spectroscopy Visualizations")
 
+    # Individual Plot Outputs
+    plot1 = gr.Plot(label="Mean Spectra")
+    plot2 = gr.Plot(label="Offset Mean Spectra")
+    plot3 = gr.Plot(label="Radar Plot")
+    plot4 = gr.Plot(label="PCA Variance")
+    plot5 = gr.Plot(label="Derivative + Normalized")
+    plot6 = gr.Plot(label="Derivative Only")
 
+    plot_button.click(
+        fn=plot_all,
+        inputs=[],
+        outputs=[plot1, plot2, plot3, plot4, plot5, plot6]
+    )
+
+demo.launch(server_name="0.0.0.0", server_port=7860, ssr_mode=False)