Update app.py

app.py

@@ -12,6 +12,7 @@ import numpy as np
 from sklearn.linear_model import LinearRegression
 from sklearn.metrics import r2_score
 import base64
+import shutil
 
 # Initialize logging
 logging.basicConfig(level=logging.INFO)
@@ -280,15 +281,42 @@ def create_error_message(message):
     </div>
     """
 
-def call_r_script_simplified(input_file, csv_output_path):
+def call_r_script_for_consideration(input_file, csv_output_path):
     """
-    Call R script for Shapley regression analysis on Consideration.
+    Call R script for Shapley regression analysis specifically for Consideration.
     """
+    # Create temporary files for all outputs (even though we only need consideration)
+    temp_dir = os.path.dirname(csv_output_path)
+    text_output_path = os.path.join(temp_dir, "output.txt")
+    csv_output_path_trust = os.path.join(temp_dir, "trust.csv")
+    csv_output_path_nps = os.path.join(temp_dir, "nps.csv")
+    csv_output_path_loyalty = os.path.join(temp_dir, "loyalty.csv")
+    csv_output_path_satisfaction = os.path.join(temp_dir, "satisfaction.csv")
+    csv_output_path_trustbuilder = os.path.join(temp_dir, "trustbuilder.csv")
+    
+    # Set the boolean flags - we only want consideration analysis
+    nps_present = False
+    loyalty_present = False
+    consideration_present = True  # This is what we want
+    satisfaction_present = False
+    trustbuilder_present = False
+
     command = [
         "Rscript",
         "process_data.R",
         input_file,
-        csv_output_path
+        text_output_path,
+        csv_output_path_trust,
+        csv_output_path_nps,
+        csv_output_path_loyalty,
+        csv_output_path,  # This is our consideration output
+        csv_output_path_satisfaction,
+        csv_output_path_trustbuilder,
+        str(nps_present).upper(),  # Convert to "TRUE"/"FALSE"
+        str(loyalty_present).upper(),
+        str(consideration_present).upper(),
+        str(satisfaction_present).upper(),
+        str(trustbuilder_present).upper(),
     ]
 
     try:
@@ -382,13 +410,13 @@ def analyze_prospects_data(file_path):
         temp_dir = tempfile.mkdtemp()
         csv_output_path = os.path.join(temp_dir, "consideration_results.csv")
         
-        # Call R script
-        r_success = call_r_script_simplified(file_path, csv_output_path)
+        # Call R script with proper parameters
+        r_success = call_r_script_for_consideration(file_path, csv_output_path)
         
         if not r_success:
             # Clean up and return error
             try:
-                os.rmdir(temp_dir)
+                shutil.rmtree(temp_dir)
             except:
                 pass
             return create_error_message("R script failed to execute. Shapley analysis not available."), None, None, None
@@ -396,7 +424,7 @@ def analyze_prospects_data(file_path):
         # Check if R script produced output file
         if not os.path.exists(csv_output_path):
             try:
-                os.rmdir(temp_dir)
+                shutil.rmtree(temp_dir)
             except:
                 pass
             return create_error_message("R script did not produce expected output file."), None, None, None
@@ -407,8 +435,7 @@ def analyze_prospects_data(file_path):
         except Exception as e:
             logger.error(f"Error reading R script output: {e}")
             try:
-                os.remove(csv_output_path)
-                os.rmdir(temp_dir)
+                shutil.rmtree(temp_dir)
             except:
                 pass
             return create_error_message(f"Error reading R script output: {e}"), None, None, None
@@ -417,8 +444,7 @@ def analyze_prospects_data(file_path):
         if "Predictor" not in results_df.columns or "Importance" not in results_df.columns:
             logger.error("R script output missing required columns")
             try:
-                os.remove(csv_output_path)
-                os.rmdir(temp_dir)
+                shutil.rmtree(temp_dir)
             except:
                 pass
             return create_error_message("R script output is invalid - missing required columns."), None, None, None
@@ -440,8 +466,7 @@ def analyze_prospects_data(file_path):
         
         # Clean up
         try:
-            os.remove(csv_output_path)
-            os.rmdir(temp_dir)
+            shutil.rmtree(temp_dir)
         except Exception as e:
             logger.error(f"Error cleaning up temp files: {e}")