[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -7,7 +7,7 @@ ci:
 
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v5.0.0
+    rev: v6.0.0
     hooks:
       - id: end-of-file-fixer
         files: (.*\.(py|md|rst|yaml|yml|json|ts|js|html|svelte|sh))$
@@ -26,7 +26,7 @@ repos:
         args: [--fix=lf]
 
   - repo: https://github.com/Lucas-C/pre-commit-hooks
-    rev: v1.5.5
+    rev: v1.5.6
     hooks:
       - id: insert-license
         files: (Dockerfile)$
@@ -74,7 +74,7 @@ repos:
         name: Unused noqa
 
   - repo: https://github.com/pycqa/isort
-    rev: 6.0.1
+    rev: 8.0.1
     hooks:
       - id: isort
 
@@ -99,30 +99,30 @@ repos:
         additional_dependencies:
           - prettier@3.2.5
 
-  - repo: https://github.com/psf/black.git
-    rev: 25.1.0
+  - repo: https://github.com/psf/black-pre-commit-mirror
+    rev: 26.3.1
     hooks:
       - id: black
         files: (.*\.py)$
 
   - repo: https://github.com/asottile/blacken-docs
-    rev: 1.19.1
+    rev: 1.20.0
     hooks:
       - id: blacken-docs
         args: [--line-length=120, --skip-errors]
         additional_dependencies:
           - black==24.10.0
 
   - repo: https://github.com/codespell-project/codespell
-    rev: v2.4.1
+    rev: v2.4.2
     hooks:
       - id: codespell
         args: [-w]
         additional_dependencies:
           - tomli
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.12.2
+    rev: v0.15.9
     hooks:
       - id: ruff
         args: [--fix, --exit-non-zero-on-fix, --no-cache]

AvatarChatbot/ui/gradio/app_gradio_demo_avatarchatbot.py

@@ -195,12 +195,10 @@ def initial_process(audio_input, face_input, model_choice):
         gr.Markdown("<h1 style='font-size: 36px;'>A PyTorch and OPEA based AI Avatar Audio Chatbot</h1>")
         with gr.Row():
             with gr.Column(scale=8):
-                gr.Markdown(
-                    """
+                gr.Markdown("""
                 <p style='font-size: 24px;'>Welcome to our AI Avatar Audio Chatbot! This application leverages PyTorch and <strong>OPEA (Open Platform for Enterprise AI) v0.8</strong> to provide you with a human-like conversational experience. It's run on Intel® Gaudi® AI Accelerator and Intel® Xeon® Processor, with hardware and software optimizations.<br>
                 Please feel free to interact with the AI avatar by choosing your own avatar and talking into the mic.</p>
-                            """
-                )
+                            """)
             with gr.Column(scale=1):
                 # with gr.Row():
                 #     gr.Markdown(f"""
@@ -285,8 +283,7 @@ def initial_process(audio_input, face_input, model_choice):
         # Technical details
         gr.Markdown("<hr>")  # Divider
         with gr.Row():
-            gr.Markdown(
-                """
+            gr.Markdown("""
                 <p style='font-size: 24px;'>OPEA megaservice deployed: <br>
                 <ul style='font-size: 24px;'>
                     <li><strong>AvatarChatbot</strong></li>
@@ -298,33 +295,28 @@ def initial_process(audio_input, face_input, model_choice):
                     <li><strong>TTS</strong> (service: opea/speecht5-gaudi, model: microsoft/speecht5_tts)</li>
                     <li><strong>Animation</strong> (service: opea/animation, model: wav2lip+gfpgan)</li>
                 </ul></p>
-                        """
-            )
+                        """)
         with gr.Row():
             gr.Image("assets/img/flowchart.png", label="Megaservice Flowchart")
         with gr.Row():
-            gr.Markdown(
-                """
+            gr.Markdown("""
             <p style='font-size: 24px;'>The AI Avatar Audio Chatbot is powered by the following Intel® AI software:<br>
                         <ul style='font-size: 24px;'>
                         <li><strong>Intel Gaudi Software v1.17.0</strong></li>
                         <li><strong>PyTorch v2.3.1 (Eager mode + torch.compile) </strong></li>
                         <li><strong>HPU Graph</strong></li>
                         <li><strong>Intel Neural Compressor (INC)</strong></li>
                         </ul></p>
-                        """
-            )
+                        """)
 
         # Disclaimer
         gr.Markdown("<hr>")  # Divider
         gr.Markdown("<h2 style='font-size: 24px;'>Notices & Disclaimers</h1>")
-        gr.Markdown(
-            """
+        gr.Markdown("""
                     <p style='font-size: 20px;'>Intel is committed to respecting human rights and avoiding complicity in human rights abuses. See Intel's Global Human Rights Principles. Intel's products and software are intended only to be used in applications that do not cause or contribute to a violation of an internationally recognized human right.<br></p>
                     <p style='font-size: 20px;'>© Intel Corporation.  Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries.  Other names and brands may be claimed as the property of others.<br></p>
                     <p style='font-size: 20px;'>You may not use or facilitate the use of this document in connection with any infringement or other legal analysis concerning Intel products described herein. You agree to grant Intel a non-exclusive, royalty-free license to any patent claim thereafter drafted which includes subject matter disclosed herein.<br></p>
-                    """
-        )
+                    """)
 
         # State transitions
         # for i in range(len(image_pils)):

ChatQnA/docker_compose/amd/cpu/epyc/milvus.yaml

@@ -424,7 +424,7 @@ dataCoord:
     balanceSilentDuration: 300 # The duration after which the channel manager start background channel balancing
     balanceInterval: 360 # The interval with which the channel manager check dml channel balance status
     checkInterval: 1 # The interval in seconds with which the channel manager advances channel states
-    notifyChannelOperationTimeout: 5 # Timeout notifing channel operations (in seconds).
+    notifyChannelOperationTimeout: 5 # Timeout notifying channel operations (in seconds).
   segment:
     maxSize: 1024 # Maximum size of a segment in MB
     diskSegmentMaxSize: 2048 # Maximum size of a segment in MB for collection which has Disk index

ChatQnA/docker_compose/intel/cpu/xeon/milvus.yaml

@@ -423,7 +423,7 @@ dataCoord:
     balanceSilentDuration: 300 # The duration after which the channel manager start background channel balancing
     balanceInterval: 360 # The interval with which the channel manager check dml channel balance status
     checkInterval: 1 # The interval in seconds with which the channel manager advances channel states
-    notifyChannelOperationTimeout: 5 # Timeout notifing channel operations (in seconds).
+    notifyChannelOperationTimeout: 5 # Timeout notifying channel operations (in seconds).
   segment:
     maxSize: 1024 # Maximum size of a segment in MB
     diskSegmentMaxSize: 2048 # Maximum size of a segment in MB for collection which has Disk index

CogniwareIms/backend/app/services/dbqna_service.py

@@ -45,25 +45,21 @@ async def get_schema(self) -> Dict[str, Any]:
             # Get table information
             with engine.connect() as conn:
                 # Get all tables
-                tables_query = text(
-                    """
+                tables_query = text("""
                     SELECT table_name
                     FROM information_schema.tables
                     WHERE table_schema = 'public'
-                """
-                )
+                """)
                 tables = conn.execute(tables_query).fetchall()
 
                 for (table_name,) in tables:
                     # Get columns for each table
-                    columns_query = text(
-                        """
+                    columns_query = text("""
                         SELECT column_name, data_type
                         FROM information_schema.columns
                         WHERE table_name = :table_name
                         ORDER BY ordinal_position
-                    """
-                    )
+                    """)
                     columns = conn.execute(columns_query, {"table_name": table_name}).fetchall()
 
                     schema["tables"][table_name] = {"columns": [{"name": col, "type": dtype} for col, dtype in columns]}
@@ -146,8 +142,7 @@ async def _get_product_inventory(self, sku: str, warehouse: str) -> Dict[str, An
         try:
             engine = self.get_engine()
             with engine.connect() as conn:
-                query = text(
-                    """
+                query = text("""
                     SELECT
                         p.name as product,
                         p.sku,
@@ -159,8 +154,7 @@ async def _get_product_inventory(self, sku: str, warehouse: str) -> Dict[str, An
                     JOIN products p ON i.product_id = p.id
                     JOIN warehouses w ON i.warehouse_id = w.id
                     WHERE p.sku = :sku AND w.name = :warehouse
-                """
-                )
+                """)
 
                 result = conn.execute(query, {"sku": sku, "warehouse": warehouse})
                 row = result.fetchone()

DocIndexRetriever/docker_compose/intel/cpu/xeon/config/milvus.yaml

@@ -423,7 +423,7 @@ dataCoord:
     balanceSilentDuration: 300 # The duration after which the channel manager start background channel balancing
     balanceInterval: 360 # The interval with which the channel manager check dml channel balance status
     checkInterval: 1 # The interval in seconds with which the channel manager advances channel states
-    notifyChannelOperationTimeout: 5 # Timeout notifing channel operations (in seconds).
+    notifyChannelOperationTimeout: 5 # Timeout notifying channel operations (in seconds).
   segment:
     maxSize: 1024 # Maximum size of a segment in MB
     diskSegmentMaxSize: 2048 # Maximum size of a segment in MB for collection which has Disk index

DocIndexRetriever/docker_compose/intel/hpu/gaudi/config/milvus.yaml

@@ -423,7 +423,7 @@ dataCoord:
     balanceSilentDuration: 300 # The duration after which the channel manager start background channel balancing
     balanceInterval: 360 # The interval with which the channel manager check dml channel balance status
     checkInterval: 1 # The interval in seconds with which the channel manager advances channel states
-    notifyChannelOperationTimeout: 5 # Timeout notifing channel operations (in seconds).
+    notifyChannelOperationTimeout: 5 # Timeout notifying channel operations (in seconds).
   segment:
     maxSize: 1024 # Maximum size of a segment in MB
     diskSegmentMaxSize: 2048 # Maximum size of a segment in MB for collection which has Disk index

EdgeCraftRAG/docker_compose/intel/gpu/arc/milvus-config.yaml

@@ -423,7 +423,7 @@ dataCoord:
     balanceSilentDuration: 300 # The duration after which the channel manager start background channel balancing
     balanceInterval: 360 # The interval with which the channel manager check dml channel balance status
     checkInterval: 1 # The interval in seconds with which the channel manager advances channel states
-    notifyChannelOperationTimeout: 5 # Timeout notifing channel operations (in seconds).
+    notifyChannelOperationTimeout: 5 # Timeout notifying channel operations (in seconds).
   segment:
     maxSize: 1024 # Maximum size of a segment in MB
     diskSegmentMaxSize: 2048 # Maximum size of a segment in MB for collection which has Disk index

EdgeCraftRAG/edgecraftrag/components/agents/deep_search/deep_search.py

@@ -258,7 +258,9 @@ async def check_retrieved(self, state: DeepSearchState) -> str:
                     bold=True,
                 ),
             )
-            await stream_writer(f"\n\n⚠️ **Reached maximum retrievals: {self.cfg.max_retrievals}, stopping search**\n\n")
+            await stream_writer(
+                f"\n\n⚠️ **Reached maximum retrievals: {self.cfg.max_retrievals}, stopping search**\n\n"
+            )
             return "stop"
 
         response = await self.llm_generate_astream_writer(state.request)

FinanceAgent/tools/research_tools.py

@@ -328,17 +328,15 @@ def analyze_balance_sheet(
 
     df_string = "Balance sheet:\n" + balance_sheet
 
-    instruction = dedent(
-        """
+    instruction = dedent("""
         Delve into a detailed scrutiny of the company's balance sheet for the most recent fiscal year, pinpointing
         the structure of assets, liabilities, and shareholders' equity to decode the firm's financial stability and
         operational efficiency. Focus on evaluating the liquidity through current assets versus current liabilities,
         the solvency via long-term debt ratios, and the equity position to gauge long-term investment potential.
         Contrast these metrics with previous years' data to highlight financial trends, improvements, or deteriorations.
         Finalize with a strategic assessment of the company's financial leverage, asset management, and capital structure,
         providing insights into its fiscal health and future prospects in a single paragraph. Less than 130 words.
-        """
-    )
+        """)
 
     prompt = combine_prompt(instruction, df_string)
     return prompt
@@ -365,8 +363,7 @@ def analyze_income_stmt(
     df_string = "Income statement:\n" + income_stmt
 
     # Analysis instruction
-    instruction = dedent(
-        """
+    instruction = dedent("""
         Conduct a comprehensive analysis of the company's income statement for the current fiscal year.
         Start with an overall revenue record, including Year-over-Year or Quarter-over-Quarter comparisons,
         and break down revenue sources to identify primary contributors and trends. Examine the Cost of
@@ -377,8 +374,7 @@ def analyze_income_stmt(
         operational challenges. The output should be a strategic overview of the company’s financial health
         in a single paragraph, less than 130 words, summarizing the previous analysis into 4-5 key points under
         respective subheadings with specific discussion and strong data support.
-        """
-    )
+        """)
 
     # Combine the instruction, section text, and income statement
     prompt = combine_prompt(instruction, df_string)
@@ -407,17 +403,15 @@ def analyze_cash_flow(
 
     df_string = "Cash flow statement:\n" + cash_flow
 
-    instruction = dedent(
-        """
+    instruction = dedent("""
         Dive into a comprehensive evaluation of the company's cash flow for the latest fiscal year, focusing on cash inflows
         and outflows across operating, investing, and financing activities. Examine the operational cash flow to assess the
         core business profitability, scrutinize investing activities for insights into capital expenditures and investments,
         and review financing activities to understand debt, equity movements, and dividend policies. Compare these cash movements
         to prior periods to discern trends, sustainability, and liquidity risks. Conclude with an informed analysis of the company's
         cash management effectiveness, liquidity position, and potential for future growth or financial challenges in a single paragraph.
         Less than 130 words.
-        """
-    )
+        """)
 
     prompt = combine_prompt(instruction, df_string)
     return prompt
@@ -457,12 +451,10 @@ def get_share_performance(
 
     df_string = "Past 60 days Stock prices:\n" + json.dumps(prices)
 
-    instruction = dedent(
-        """
+    instruction = dedent("""
         Dive into a comprehensive evaluation of the company's stock price for the latest 60 days.
         Less than 130 words.
-        """
-    )
+        """)
 
     prompt = combine_prompt(instruction, df_string)
     return prompt

MultimodalQnA/docker_compose/amd/cpu/epyc/config/milvus.yaml

@@ -425,7 +425,7 @@ dataCoord:
     balanceSilentDuration: 300 # The duration after which the channel manager start background channel balancing
     balanceInterval: 360 # The interval with which the channel manager check dml channel balance status
     checkInterval: 1 # The interval in seconds with which the channel manager advances channel states
-    notifyChannelOperationTimeout: 5 # Timeout notifing channel operations (in seconds).
+    notifyChannelOperationTimeout: 5 # Timeout notifying channel operations (in seconds).
   segment:
     maxSize: 1024 # Maximum size of a segment in MB
     diskSegmentMaxSize: 2048 # Maximum size of a segment in MB for collection which has Disk index

MultimodalQnA/docker_compose/intel/cpu/xeon/config/milvus.yaml

@@ -423,7 +423,7 @@ dataCoord:
     balanceSilentDuration: 300 # The duration after which the channel manager start background channel balancing
     balanceInterval: 360 # The interval with which the channel manager check dml channel balance status
     checkInterval: 1 # The interval in seconds with which the channel manager advances channel states
-    notifyChannelOperationTimeout: 5 # Timeout notifing channel operations (in seconds).
+    notifyChannelOperationTimeout: 5 # Timeout notifying channel operations (in seconds).
   segment:
     maxSize: 1024 # Maximum size of a segment in MB
     diskSegmentMaxSize: 2048 # Maximum size of a segment in MB for collection which has Disk index

MultimodalQnA/docker_compose/intel/hpu/gaudi/config/milvus.yaml

@@ -423,7 +423,7 @@ dataCoord:
     balanceSilentDuration: 300 # The duration after which the channel manager start background channel balancing
     balanceInterval: 360 # The interval with which the channel manager check dml channel balance status
     checkInterval: 1 # The interval in seconds with which the channel manager advances channel states
-    notifyChannelOperationTimeout: 5 # Timeout notifing channel operations (in seconds).
+    notifyChannelOperationTimeout: 5 # Timeout notifying channel operations (in seconds).
   segment:
     maxSize: 1024 # Maximum size of a segment in MB
     diskSegmentMaxSize: 2048 # Maximum size of a segment in MB for collection which has Disk index

MultimodalQnA/ui/gradio/utils.py

@@ -105,7 +105,7 @@ def flush(self):
 def maintain_aspect_ratio_resize(image, width=None, height=None, inter=cv2.INTER_AREA):
     # Grab the image size and initialize dimensions
     dim = None
-    (h, w) = image.shape[:2]
+    h, w = image.shape[:2]
 
     # Return original image if no need to resize
     if width is None and height is None:

one_click_deploy/core/deployer.py

@@ -313,7 +313,7 @@ def _interactive_setup_for_deploy(self):
 
         # If this is an offline deployment, the mode is already set and should not be prompted.
         if getattr(self.args, "is_offline_deployment", False):
-            log_message("INFO", f"Offline Deployment Mode: {self.args.deploy_mode} (pre-selected)")
+            log_message("INFO", f"Offline Deployment Mode: {self.args.deploy_mode} (preselected)")
         else:
             self.args.deploy_mode = click.prompt(
                 "Deployment Mode", type=click.Choice(["docker", "k8s"]), default="docker"