clean: run linting and formating on repo

gradio_app.py

@@ -4,12 +4,12 @@ from deepengineer.common_path import DATA_DIR
 
 with gr.Blocks() as demo:
     gr.Markdown("# Agent Interface with Real‑Time Tool Logging")
-    user_input  = gr.Textbox(label="User Message")
-    
+    user_input = gr.Textbox(label="User Message")
+
     log_output = gr.Textbox(label="Tool Invocation Log", interactive=False)
-    
+
     agent_output = gr.Markdown(
-        label="Agent Response", 
+        label="Agent Response",
     )
 
     send = gr.Button("Send")

src/deepengineer/backend/gradio_tools.py

@@ -15,14 +15,14 @@ def parse_markdown_images(markdown_text: str, image_dir: Path) -> str:
     if not markdown_text or not image_dir:
         return markdown_text
 
-    image_pattern = r'!\[([^\]]*)\]\(([^)]+)\)'
+    image_pattern = r"!\[([^\]]*)\]\(([^)]+)\)"
 
     def replace_image_path(match):
         alt_text = match.group(1)
         image_name = match.group(2)
         # Always use image_dir/image_name
-        new_path = "gradio_api/file="+str(Path(image_dir) / image_name)
-        return f'![{alt_text}]({new_path})'
+        new_path = "gradio_api/file=" + str(Path(image_dir) / image_name)
+        return f"![{alt_text}]({new_path})"
 
     return re.sub(image_pattern, replace_image_path, markdown_text)
 
@@ -37,7 +37,7 @@ def run_agent_stream(user_input: str):
     Yields tuples: (agent_output, log_output)
     """
     log_queue = queue.Queue()
-    
+
     # empty queue before each run
     while not log_queue.empty():
         print("Emptying log queue")
@@ -47,7 +47,9 @@ def run_agent_stream(user_input: str):
     done = threading.Event()
 
     def _worker():
-        answer_container["text"], answer_container["image_dir"] = main_search(user_input, log_queue)
+        answer_container["text"], answer_container["image_dir"] = main_search(
+            user_input, log_queue
+        )
         done.set()
 
     threading.Thread(target=_worker, daemon=True).start()
@@ -70,13 +72,13 @@ def run_agent_stream(user_input: str):
     # Process the final answer to include images
     final_answer = answer_container["text"]
     image_dir = answer_container["image_dir"]
-    
+
     if final_answer and image_dir:
         final_answer = parse_markdown_images(final_answer, image_dir)
-        
+
     final_answer = final_answer.replace("```python", "")
     final_answer = final_answer.replace("```markdown", "")
     final_answer = final_answer.replace("```", "")
 
     # final yield: agent_output filled with processed markdown, log_output frozen
-    yield (final_answer, log_buffer.rstrip())
+    yield (final_answer, log_buffer.rstrip())

src/deepengineer/deepsearch/main_agent.py

@@ -25,9 +25,11 @@ def create_output_image_path(random_name_images: int | None = None):
     return output_image_path
 
 
-
 def create_main_search_agent(
-    model_id="deepseek/deepseek-reasoner", database: DataBase | None = None, log_queue: queue.Queue | None = None, output_image_path: Path | None = None,
+    model_id="deepseek/deepseek-reasoner",
+    database: DataBase | None = None,
+    log_queue: queue.Queue | None = None,
+    output_image_path: Path | None = None,
 ):
     """
     Simple agent that can search the web and answer the question. This is much faster and better for simple questions that do not require deep research.
@@ -36,20 +38,26 @@ def create_main_search_agent(
     model = LiteLLMModel(model_id=model_id)
     if database is None:
         database = DataBase()
-        
+
     output_image_path = output_image_path or DATA_DIR / "images"
     output_image_path.mkdir(parents=True, exist_ok=True)
 
     # Web search and crawling tools
     WEB_SEARCH_TOOLS = [
-        SearchTool(log_queue=log_queue,),
-        ArxivSearchTool(log_queue=log_queue,),
-        ScientificSearchTool(log_queue=log_queue,),
+        SearchTool(
+            log_queue=log_queue,
+        ),
+        ArxivSearchTool(
+            log_queue=log_queue,
+        ),
+        ScientificSearchTool(
+            log_queue=log_queue,
+        ),
         GetTableOfContentsTool(log_queue=log_queue, database=database),
         GetMarkdownTool(log_queue=log_queue, database=database),
         GetPagesContentTool(log_queue=log_queue, database=database),
         FindInMarkdownTool(log_queue=log_queue, database=database),
-        SaveMatplotlibFigTool(log_queue=log_queue,output_dir=output_image_path),
+        SaveMatplotlibFigTool(log_queue=log_queue, output_dir=output_image_path),
     ]
 
     search_agent = CodeAgent(
@@ -100,9 +108,13 @@ Failure or 'I cannot answer' or 'None found' will not be tolerated, success will
 Run verification steps if that's needed, you must make sure you find the correct answer! Here is the task:
 {task}
 """
-    agent = create_main_search_agent(model_id="mistral/mistral-medium-latest", log_queue=log_queue, output_image_path=output_image_path)
+    agent = create_main_search_agent(
+        model_id="mistral/mistral-medium-latest",
+        log_queue=log_queue,
+        output_image_path=output_image_path,
+    )
     answer = agent.run(MAIN_PROMPT.format(task=task))
-    
+
     return answer, output_image_path
 
 

src/deepengineer/deepsearch/main_deep_search.py

@@ -1,4 +1,5 @@
 """work in progress"""
+
 from pathlib import Path
 import random
 from smolagents import CodeAgent, LiteLLMModel
@@ -7,11 +8,13 @@ from deepengineer.deepsearch.scawl_web_agent import create_web_search_agent
 from deepengineer.deepsearch.draw_agent import SaveMatplotlibFigTool
 from deepengineer.common_path import DATA_DIR
 
+
 def _create_output_image_path(image_folder_suffix: int | None = None):
     output_image_path = Path(DATA_DIR) / f"images_{image_folder_suffix}"
     output_image_path.mkdir(parents=True, exist_ok=True)
     return output_image_path
 
+
 def create_main_deep_search_agent(
     main_model_id="deepseek/deepseek-reasoner",
     web_search_model_id="deepseek/deepseek-reasoner",
@@ -25,10 +28,11 @@ def create_main_deep_search_agent(
     web_search_agent = create_web_search_agent(
         model_id=web_search_model_id, database=database
     )
-    
-    
+
     image_folder_suffix = random.randint(1000000, 9999999)
-    output_image_path = _create_output_image_path(image_folder_suffix=image_folder_suffix)
+    output_image_path = _create_output_image_path(
+        image_folder_suffix=image_folder_suffix
+    )
 
     manager_agent = CodeAgent(
         model=main_model,
@@ -83,4 +87,4 @@ Run verification steps if that's needed, you must make sure you find the correct
 
     answer = agent.run(MAIN_PROMPT.format(task=task))
 
-    print(answer)
+    print(answer)

src/deepengineer/deepsearch/scawl_web_agent.py

@@ -20,6 +20,7 @@ from deepengineer.webcrawler.pdf_utils import (
 from deepengineer.logging_tools import LoggingTool
 import queue
 
+
 class ToolNames(Enum):
     # Search tools
     SEARCH_TOOL = "web_search_tool"
@@ -54,7 +55,7 @@ class SearchTool(LoggingTool):
         },
     }
     output_type = "object"
-    
+
     def __init__(self, log_queue: queue.Queue | None = None):
         super().__init__(log_queue=log_queue)
 
@@ -80,7 +81,7 @@ class ArxivSearchTool(LoggingTool):
         }
     }
     output_type = "object"
-    
+
     def __init__(self, log_queue: queue.Queue | None = None):
         super().__init__(log_queue=log_queue)
 
@@ -102,7 +103,7 @@ class PubmedSearchTool(LoggingTool):
         }
     }
     output_type = "object"
-    
+
     def __init__(self, log_queue: queue.Queue | None = None):
         super().__init__(log_queue=log_queue)
 
@@ -225,7 +226,9 @@ class FindInMarkdownTool(LoggingTool):
 
 
 def create_web_search_agent(
-    model_id="deepseek/deepseek-reasoner", database: DataBase | None = None, log_queue: queue.Queue | None = None
+    model_id="deepseek/deepseek-reasoner",
+    database: DataBase | None = None,
+    log_queue: queue.Queue | None = None,
 ):
     """Create a web search agent with search, crawling, and PDF analysis capabilities."""
 

src/deepengineer/logging_tools.py

@@ -2,14 +2,16 @@ import queue
 from typing import Any
 from smolagents import Tool
 
+
 class LoggingTool(Tool):
     """
     Base class for tools that can push logs to a queue.
     """
+
     def __init__(self, log_queue: queue.Queue | None = None):
         super().__init__()
         self.log_queue = log_queue
 
     def push_log(self, msg: str):
         if self.log_queue:
-            self.log_queue.put(msg)
+            self.log_queue.put(msg)

tests/deepsearch/test_main_agent.py

@@ -15,6 +15,6 @@ def test_main_agent():
     main_search(
         task="""
     Search a paper called "High Energy Physics Opportunities Using Reactor Antineutrinos" on arXiv, download it and extract the table of contents
-    """, log_queue=log_queue
+    """,
+        log_queue=log_queue,
     )
-

tests/deepsearch/test_web_agent.py

@@ -15,4 +15,3 @@ def test_run_web_search_agent():
         )
         is not None
     )
-