fix: patch B1, B5, B7, B8, B9 (state guards, dead callback, face size tuple, dashboard loss, esc key)

app.py

@@ -242,6 +242,19 @@ if 'history' not in st.session_state:
 if 'dark_mode' not in st.session_state:
     st.session_state.dark_mode = True
 
+# App state lifecycle management
+for key, default in [
+    ('processing', False),
+    ('enhanced_img', None),
+    ('processing_error', None),
+    ('process_duration', None),
+    ('start_time', None),
+    ('last_run_params', None),
+    ('history_added_for', None),
+]:
+    if key not in st.session_state:
+        st.session_state[key] = default
+
 def apply_theme():
     """Apply dark/light theme based on user preference."""
     if not st.session_state.dark_mode:
@@ -269,16 +282,6 @@ def apply_theme():
         </style>
         """, unsafe_allow_html=True)
 
-def progress_callback(stage, progress, message):
-    """Callback function to update progress state from pipeline."""
-    if not st.session_state.progress_state.get('cancelled', False):
-        st.session_state.progress_state = {
-            'stage': stage,
-            'progress': progress,
-            'message': message,
-            'active': True,
-            'cancelled': False
-        }
 
 @st.cache_resource(show_spinner=False, hash_funcs={LocalAIEnhancerPipeline: lambda _: None})
 def get_pipeline():
@@ -322,7 +325,7 @@ document.addEventListener('keydown', function(e) {
     }
     // Esc: Cancel processing
     if (e.key === 'Escape') {
-        const cancelButton = document.querySelector('button:contains("Cancel")');
+        const cancelButton = Array.from(document.querySelectorAll('button')).find(b => b.textContent.includes('Cancel'));
         if (cancelButton) {
             cancelButton.click();
         }
@@ -358,7 +361,12 @@ def get_training_status():
             if "iter:" in line and "epoch:" in line:
                 match = re.search(r"epoch:\s*(\d+),\s*iter:\s*([\d,]+)", line)
                 eta_match = re.search(r"eta:\s*([\d:]+)", line)
-                loss_match = re.search(r"cross_entropy_loss:\s*([\d.e+-]+)", line)
+                loss_match = (
+                    re.search(r"cross_entropy_loss:\s*([\d.e+-]+)", line) or
+                    re.search(r"l_g_pix:\s*([\d.e+-]+)", line) or
+                    re.search(r"l_g_percep:\s*([\d.e+-]+)", line) or
+                    re.search(r"loss:\s*([\d.e+-]+)", line)
+                )
                 
                 if match:
                     epoch = int(match.group(1))

pipeline.py

@@ -438,6 +438,10 @@ class LocalAIEnhancerPipeline:
         h, w_img, _ = face_helper.input_img.shape
         h_up, w_up = int(h * upscale), int(w_img * upscale)
         
+        # Normalize face size to tuple just in case it is an integer in some facexlib versions
+        fs = face_helper.face_size
+        raw_face_size = fs if isinstance(fs, tuple) else (fs, fs)
+        
         # Initialize background image (upsampled background)
         if bg_img is None:
             upsample_img = cv2.resize(face_helper.input_img, (w_up, h_up), interpolation=cv2.INTER_LANCZOS4)
@@ -456,16 +460,16 @@ class LocalAIEnhancerPipeline:
                     restored_face_up = self.bg_upsampler_instance.enhance(restored_face, outscale=upscale)[0]
                 else:
                     # Fallback to Lanczos if no Real-ESRGAN instance loaded or face_upsample is disabled
-                    restored_face_up = cv2.resize(restored_face, (face_helper.face_size[0] * upscale, face_helper.face_size[1] * upscale), interpolation=cv2.INTER_LANCZOS4)
+                    restored_face_up = cv2.resize(restored_face, (raw_face_size[0] * upscale, raw_face_size[1] * upscale), interpolation=cv2.INTER_LANCZOS4)
                 
                 # Blend with original cropped face to preserve original high-resolution details when w > 0
                 if w > 0.0:
-                    original_face_up = cv2.resize(cropped_face, (face_helper.face_size[0] * upscale, face_helper.face_size[1] * upscale), interpolation=cv2.INTER_LANCZOS4)
+                    original_face_up = cv2.resize(cropped_face, (raw_face_size[0] * upscale, raw_face_size[1] * upscale), interpolation=cv2.INTER_LANCZOS4)
                     restored_face_up = cv2.addWeighted(original_face_up, w, restored_face_up, 1.0 - w, 0.0)
                 
                 inv_aff /= upscale
                 inv_aff[:, 2] *= upscale
-                face_size = (face_helper.face_size[0] * upscale, face_helper.face_size[1] * upscale)
+                face_size = (raw_face_size[0] * upscale, raw_face_size[1] * upscale)
                 inv_restored = cv2.warpAffine(restored_face_up, inv_aff, (w_up, h_up))
             else:
                 # Blend with original cropped face to preserve original high-resolution details when w > 0
@@ -475,7 +479,7 @@ class LocalAIEnhancerPipeline:
                 # Add an offset to inverse affine matrix, for more precise back alignment
                 extra_offset = 0
                 inv_aff[:, 2] += extra_offset
-                face_size = face_helper.face_size
+                face_size = raw_face_size
                 inv_restored = cv2.warpAffine(restored_face, inv_aff, (w_up, h_up))
             
             # Create boundary mask