Validation logs

app.py

@@ -53,20 +53,64 @@ def compile_model(model_name, vmem_value, lpmem_value, uploaded_model):
         vmem_size_limit = int(vmem_value * 1000)
         lpmem_size_limit = int(lpmem_value * 1000)
         
-        # Run the model fitter
-        success, results = sr100_model_compiler.sr100_model_optimizer(
-            model_file=model_path,
-            vmem_size_limit=vmem_size_limit,
-            lpmem_size_limit=lpmem_size_limit
-        )
-        print(results)
+        # Run the model fitter with better error handling
+        try:
+            print(f"Starting model optimization for {model_path}")
+            print(f"VMEM limit: {vmem_size_limit}, LPMEM limit: {lpmem_size_limit}")
+            
+            success, results = sr100_model_compiler.sr100_model_optimizer(
+                model_file=model_path,
+                vmem_size_limit=vmem_size_limit,
+                lpmem_size_limit=lpmem_size_limit
+            )
+            
+            print(f"Optimization complete. Success: {success}")
+            print(f"Results: {results}")
+            
+            # Check if results is None or missing expected keys
+            if not results:
+                return (
+                    "<div style='color:#d32f2f; font-weight:bold; font-size:1.2em;'>"
+                    "❌ ERROR: Optimization returned empty results</div>"
+                )
+                
+        except Exception as e:
+            error_message = str(e)
+            print(f"Exception during model optimization: {error_message}")
+            
+            return (
+                "<div style='color:#d32f2f; font-weight:bold; font-size:1.2em;'>"
+                "❌ ERROR: Model optimization failed</div>"
+                "<div style='margin-top:0.5em;color:#000;'>Error details:</div>"
+                f"<pre style='white-space:pre-wrap; background:#f6f8fa; padding:8px; border-radius:6px; color:#000;'>{html.escape(error_message)}</pre>"
+                "<div style='margin-top:0.5em;color:#000;'>"
+                "Common reasons for failure:"
+                "<ul style='margin:0.5em 0 0 1.2em;'>"
+                "<li>Model contains unsupported operations for SR100 NPU</li>"
+                "<li>Model is not properly quantized to INT8</li>"
+                "<li>Memory limits are too restrictive for the model size</li>"
+                "</ul>"
+                "</div>"
+            )
 
     output = []
     
-    if results['cycles_npu'] == 0:
+    # Check for specific failure cases from results
+    if not success:
+        print(f"Optimization reported failure. Reason: {results.get('failure_reason', 'Unknown')}")
+    
+    # Check if NPU cycles is zero (CPU-only model)
+    npu_zero = results.get('cycles_npu', 0) == 0
+    
+    if npu_zero:
+        output.append(
+            "<div style='color:#e65100; font-weight:bold; font-size:1.2em;'>"
+            "⚠️ CPU-ONLY: Model fits in memory but no operators mapped to the NPU</div>"
+        )
         output.append(
-            "<div style='color:#d32f2f; font-weight:bold; font-size:1.2em;'>"
-            "❌ FAILURE: This model does not utilize NPU, Please see if model has supported ops, thus cannot optimize for SR100</div>"
+            "<div style='color:#000; margin-top:0.25em;'>"
+            "This typically means the model contains ops not supported by the SR100 NPU. "
+            "Please review/convert unsupported ops or choose an NPU-friendly model.</div>"
         )
         output.append("<div style='margin-top:0.5em;color:#000;'>Compiler log:</div>")
         output.append(