Update app.py

app.py

@@ -11,6 +11,7 @@ from sklearn.feature_selection import f_classif
 import matplotlib.pyplot as plt
 import seaborn as sns
 import io
+from PIL import Image  # For converting BytesIO to PIL Image
 
 # Constants for reproducibility and configuration
 RANDOM_STATE = 42
@@ -76,6 +77,10 @@ def analyze_file(file, label_col, n_clusters):
 
     results_text = ""
     model_img = None
+    fi_img = None
+    kmeans_img = None
+    agg_img = None
+    diff_img = None
 
     # Prediction: Regression or Classification
     try:
@@ -102,7 +107,7 @@ def analyze_file(file, label_col, n_clusters):
             plt.savefig(buf, format="png", bbox_inches="tight")
             plt.close()
             buf.seek(0)
-            model_img = buf
+            model_img = Image.open(buf)  # Convert to PIL Image
         else:
             # Classification
             if len(y.unique()) < 2:
@@ -124,9 +129,9 @@ def analyze_file(file, label_col, n_clusters):
             plt.savefig(buf, format="png", bbox_inches="tight")
             plt.close()
             buf.seek(0)
-            model_img = buf
+            model_img = Image.open(buf)  # Convert to PIL Image
     except Exception as e:
-        return (f"Error during model training: {e}", None, None, None, None, None)
+        results_text += f"\nError during model training: {e}"
 
     # Feature Importance
     try:
@@ -140,9 +145,8 @@ def analyze_file(file, label_col, n_clusters):
         plt.savefig(buf, format="png", bbox_inches="tight")
         plt.close()
         buf.seek(0)
-        fi_img = buf
+        fi_img = Image.open(buf)  # Convert to PIL Image
     except Exception as e:
-        fi_img = None
         results_text += f"\nWarning: Could not compute feature importance: {e}"
 
     # KMeans Clustering
@@ -162,9 +166,8 @@ def analyze_file(file, label_col, n_clusters):
         plt.savefig(buf, format="png", bbox_inches="tight")
         plt.close()
         buf.seek(0)
-        kmeans_img = buf
+        kmeans_img = Image.open(buf)  # Convert to PIL Image
     except Exception as e:
-        kmeans_img = None
         results_text += f"\nWarning: KMeans clustering failed: {e}"
 
     # Agglomerative Clustering
@@ -181,9 +184,8 @@ def analyze_file(file, label_col, n_clusters):
         plt.savefig(buf, format="png", bbox_inches="tight")
         plt.close()
         buf.seek(0)
-        agg_img = buf
+        agg_img = Image.open(buf)  # Convert to PIL Image
     except Exception as e:
-        agg_img = None
         results_text += f"\nWarning: Agglomerative clustering failed: {e}"
 
     # Differentiating Features
@@ -199,9 +201,8 @@ def analyze_file(file, label_col, n_clusters):
         plt.savefig(buf, format="png", bbox_inches="tight")
         plt.close()
         buf.seek(0)
-        diff_img = buf
+        diff_img = Image.open(buf)  # Convert to PIL Image
     except Exception as e:
-        diff_img = None
         results_text += f"\nWarning: Could not compute differentiating features: {e}"
 
     return results_text, model_img, fi_img, kmeans_img, agg_img, diff_img