[CODE QUALITY]

playground/agents/worker_example.py

@@ -18,7 +18,7 @@ worker = Worker(
     tools=[],
     temperature=0.5,
     llm=OpenAIChat(openai_api_key=api_key),
-    verbose = True,
+    verbose=True,
 )
 
 # Running the worker with a prompt

playground/demos/fof/langchain.py

@@ -2,4 +2,3 @@
 This tutorial shows you how to integrate swarms with Langchain
 
 """
-

swarms/agents/agent_wrapper.py

@@ -1,18 +1,19 @@
 from swarms.structs.agent import Agent
 
+
 def agent_wrapper(ClassToWrap):
     """
     This function takes a class 'ClassToWrap' and returns a new class that
     inherits from both 'ClassToWrap' and 'Agent'. The new class overrides
     the '__init__' method of 'Agent' to call the '__init__' method of 'ClassToWrap'.
-    
+
     Args:
         ClassToWrap (type): The class to be wrapped and made to inherit from 'Agent'.
-        
+
     Returns:
         type: The new class that inherits from both 'ClassToWrap' and 'Agent'.
     """
-    
+
     class WrappedClass(ClassToWrap, Agent):
         def __init__(self, *args, **kwargs):
             try:
@@ -21,5 +22,5 @@ def agent_wrapper(ClassToWrap):
             except Exception as e:
                 print(f"Error initializing WrappedClass: {e}")
                 raise e
-    
+
-    return WrappedClass
+    return WrappedClass

swarms/agents/worker_agent.py

@@ -9,6 +9,7 @@ from langchain_experimental.autonomous_agents import AutoGPT
 
 from swarms.utils.decorators import error_decorator, timing_decorator
 
+
 class Worker:
     """
     The Worker class represents an autonomous agent that can perform tassks through
@@ -164,7 +165,7 @@ class Worker:
     # @log_decorator
     @error_decorator
     @timing_decorator
-    def run(self, task: str = None, img = None, *args, **kwargs):
+    def run(self, task: str = None, img=None, *args, **kwargs):
         """
         Run the autonomous agent on a given task.
 

swarms/models/__init__.py

@@ -112,5 +112,4 @@ __all__ = [
     "LavaMultiModal",
     "QwenVLMultiModal",
     "CLIPQ",
-    
 ]

swarms/models/clipq.py

@@ -10,17 +10,17 @@ from transformers import CLIPModel, CLIPProcessor
 class CLIPQ:
     """
     ClipQ is an CLIQ based model that can be used to generate captions for images.
-    
+
-    
+
     Attributes:
         model_name (str): The name of the model to be used.
         query_text (str): The query text to be used for the model.
-        
+
     Args:
         model_name (str): The name of the model to be used.
         query_text (str): The query text to be used for the model.
-        
+
-    
+
 
 
     """
@@ -30,13 +30,15 @@ class CLIPQ:
         model_name: str = "openai/clip-vit-base-patch16",
         query_text: str = "A photo ",
         *args,
-        **kwargs
+        **kwargs,
     ):
-        self.model = CLIPModel.from_pretrained(model_name, *args, **kwargs)
+        self.model = CLIPModel.from_pretrained(
+            model_name, *args, **kwargs
+        )
         self.processor = CLIPProcessor.from_pretrained(model_name)
         self.query_text = query_text
 
-    def fetch_image_from_url(self, url = "https://picsum.photos/800"):
+    def fetch_image_from_url(self, url="https://picsum.photos/800"):
         """Fetches an image from the given url"""
         response = requests.get(url)
         if response.status_code != 200:
@@ -48,7 +50,9 @@ class CLIPQ:
         """Loads an image from the given path"""
         return Image.open(path)
 
-    def split_image(self, image, h_splits: int = 2, v_splits: int = 2):
+    def split_image(
+        self, image, h_splits: int = 2, v_splits: int = 2
+    ):
         """Splits the given image into h_splits x v_splits parts"""
         width, height = image.size
         w_step, h_step = width // h_splits, height // v_splits
@@ -57,7 +61,12 @@ class CLIPQ:
         for i in range(v_splits):
             for j in range(h_splits):
                 slice = image.crop(
-                    (j * w_step, i * h_step, (j + 1) * w_step, (i + 1) * h_step)
+                    (
+                        j * w_step,
+                        i * h_step,
+                        (j + 1) * w_step,
+                        (i + 1) * h_step,
+                    )
                 )
                 slices.append(slice)
         return slices
@@ -74,10 +83,15 @@ class CLIPQ:
 
         for slice in slices:
             inputs = self.processor(
-                text=self.query_text, images=slice, return_tensors="pt", padding=True
+                text=self.query_text,
+                images=slice,
+                return_tensors="pt",
+                padding=True,
             )
             outputs = self.model(**inputs)
-            vectors.append(outputs.image_embeds.squeeze().detach().numpy())
+            vectors.append(
+                outputs.image_embeds.squeeze().detach().numpy()
+            )
         return vectors
 
     def run_from_url(
@@ -118,7 +132,9 @@ class CLIPQ:
         blur = GaussianBlur(kernel_size)
         return blur(image)
 
-    def run_from_path(self, path: str = None, h_splits: int = 2, v_splits: int = 2):
+    def run_from_path(
+        self, path: str = None, h_splits: int = 2, v_splits: int = 2
+    ):
         """Runs the model on the image loaded from the given path"""
         image = self.load_image_from_path(path)
         return self.get_vectors(image, h_splits, v_splits)
@@ -132,7 +148,9 @@ class CLIPQ:
 
         inputs_text = self.processor(
             text=candidate_captions,
-            images=inputs_image.pixel_values[0],  # Fix the argument name
+            images=inputs_image.pixel_values[
+                0
+            ],  # Fix the argument name
             return_tensors="pt",
             padding=True,
             truncation=True,
@@ -142,7 +160,8 @@ class CLIPQ:
             pixel_values=inputs_image.pixel_values[0]
         ).image_embeds
         text_embeds = self.model(
-            input_ids=inputs_text.input_ids, attention_mask=inputs_text.attention_mask
+            input_ids=inputs_text.input_ids,
+            attention_mask=inputs_text.attention_mask,
         ).text_embeds
 
         # Calculate similarity between image and text
@@ -156,6 +175,9 @@ class CLIPQ:
     ):
         """Get the best caption for the given image"""
         slices = self.split_image(image, h_splits, v_splits)
-        captions = [self.get_captions(slice, candidate_captions) for slice in slices]
+        captions = [
+            self.get_captions(slice, candidate_captions)
+            for slice in slices
+        ]
         concated_captions = "".join(captions)
-        return concated_captions
+        return concated_captions

swarms/structs/agent.py

@@ -670,10 +670,15 @@ class Agent:
         """
         ltr = self.long_term_memory.query(query)
 
-        return f"""{prompt}
+        context = f"""
-            ################ CONTEXT ####################
+            {prompt}
+            ####### Long Term Memory ################
             {ltr}
         """
+        return self.short_memory.append([f"{context}"])
+
+    def add_memory(self, message: str):
+        return self.short_memory.append([f"{message}"])
 
     async def run_concurrent(self, tasks: List[str], **kwargs):
         """

swarms/structs/task.py

@@ -233,7 +233,7 @@ class Task:
                     if task.description is not None
                     else ""
                 )
-                
+
                 result = (
                     task.result if task.result is not None else ""
                 )

swarms/telemetry/sys_info.py

@@ -89,7 +89,6 @@ def get_package_mismatches(file_path="pyproject.toml"):
     return "\n" + "\n".join(mismatches)
 
 
-
 def system_info():
     swarms_verison = get_swarms_verison()
     return {
@@ -100,4 +99,3 @@ def system_info():
         "CPU Info": get_cpu_info(),
         "RAM Info": get_ram_info(),
     }
-

swarms/telemetry/user_utils.py

@@ -5,6 +5,7 @@ import socket
 from swarms.telemetry.sys_info import system_info
 from swarms.telemetry.check_update import check_for_package
 
+
 # Helper functions
 def generate_user_id():
     """Generate user id
@@ -75,7 +76,6 @@ def get_local_ip():
     return socket.gethostbyname(socket.gethostname())
 
 
-
 def get_user_device_data():
     data = {
         "ID": generate_user_id(),
@@ -85,5 +85,6 @@ def get_user_device_data():
         "Swarms [Version]": check_for_package("swarms"),
     }
     return data
-    
+
-# 
+
+#

swarms/utils/__init__.py

@@ -25,7 +25,7 @@ from swarms.utils.download_weights_from_url import (
 from swarms.utils.save_logs import parse_log_file
 
 
-######## 
+########
 from swarms.utils.yaml_output_parser import YamlOutputParser
 from swarms.utils.json_output_parser import JsonOutputParser
 from swarms.utils.remove_json_whitespace import (

swarms/utils/json_output_parser.py

@@ -5,9 +5,11 @@ from pydantic import BaseModel, ValidationError
 
 T = TypeVar("T", bound=BaseModel)
 
+
 class JsonParsingException(Exception):
     """Custom exception for errors in JSON parsing."""
 
+
 class JsonOutputParser:
     """Parse JSON output using a Pydantic model.
 
@@ -17,7 +19,7 @@ class JsonOutputParser:
     Attributes:
         pydantic_object: A Pydantic model class for parsing and validation.
         pattern: A regex pattern to match JSON code blocks.
-        
+
     Examples:
     >>> from pydantic import BaseModel
     >>> from swarms.utils.json_output_parser import JsonOutputParser
@@ -29,12 +31,14 @@ class JsonOutputParser:
     >>> text = "```json\n{\"name\": \"John\", \"age\": 42}\n```"
     >>> model = parser.parse(text)
     >>> model.name
-    
+
     """
 
     def __init__(self, pydantic_object: Type[T]):
         self.pydantic_object = pydantic_object
-        self.pattern = re.compile(r"^```(?:json)?(?P<json>[^`]*)", re.MULTILINE | re.DOTALL)
+        self.pattern = re.compile(
+            r"^```(?:json)?(?P<json>[^`]*)", re.MULTILINE | re.DOTALL
+        )
 
     def parse(self, text: str) -> T:
         """Parse the provided text to extract and validate JSON data.
@@ -57,7 +61,10 @@ class JsonOutputParser:
 
         except (json.JSONDecodeError, ValidationError) as e:
             name = self.pydantic_object.__name__
-            msg = f"Failed to parse {name} from text '{text}'. Error: {e}"
+            msg = (
+                f"Failed to parse {name} from text '{text}'."
+                f" Error: {e}"
+            )
             raise JsonParsingException(msg) from e
 
     def get_format_instructions(self) -> str:
@@ -67,12 +74,19 @@ class JsonOutputParser:
             A string containing formatting instructions.
         """
         schema = self.pydantic_object.schema()
-        reduced_schema = {k: v for k, v in schema.items() if k not in ['title', 'type']}
+        reduced_schema = {
+            k: v
+            for k, v in schema.items()
+            if k not in ["title", "type"]
+        }
         schema_str = json.dumps(reduced_schema, indent=4)
 
-        format_instructions = f"JSON Formatting Instructions:\n{schema_str}"
+        format_instructions = (
+            f"JSON Formatting Instructions:\n{schema_str}"
+        )
         return format_instructions
 
+
 # # Example usage
 # class ExampleModel(BaseModel):
 #     field1: int

swarms/utils/remove_json_whitespace.py

@@ -1,6 +1,7 @@
 import json
 import yaml
 
+
 def remove_whitespace_from_json(json_string: str) -> str:
     """
     Removes unnecessary whitespace from a JSON string.
@@ -15,14 +16,14 @@ def remove_whitespace_from_json(json_string: str) -> str:
         str: The JSON string with whitespace removed.
     """
     parsed = json.loads(json_string)
-    return json.dumps(parsed, separators=(',', ':'))
+    return json.dumps(parsed, separators=(",", ":"))
+
 
 # # Example usage for JSON
 # json_string = '{"field1": 123, "field2": "example text"}'
 # print(remove_whitespace_from_json(json_string))
 
 
-
 def remove_whitespace_from_yaml(yaml_string: str) -> str:
     """
     Removes unnecessary whitespace from a YAML string.
@@ -40,6 +41,7 @@ def remove_whitespace_from_yaml(yaml_string: str) -> str:
     parsed = yaml.safe_load(yaml_string)
     return yaml.dump(parsed, default_flow_style=True)
 
+
 # # Example usage for YAML
 # yaml_string = """
 # field1: 123

swarms/utils/yaml_output_parser.py

@@ -6,9 +6,11 @@ from pydantic import BaseModel, ValidationError
 
 T = TypeVar("T", bound=BaseModel)
 
+
 class YamlParsingException(Exception):
     """Custom exception for errors in YAML parsing."""
 
+
 class YamlOutputParser:
     """Parse YAML output using a Pydantic model.
 
@@ -18,8 +20,8 @@ class YamlOutputParser:
     Attributes:
         pydantic_object: A Pydantic model class for parsing and validation.
         pattern: A regex pattern to match YAML code blocks.
-        
+
-        
+
     Examples:
     >>> from pydantic import BaseModel
     >>> from swarms.utils.yaml_output_parser import YamlOutputParser
@@ -31,12 +33,14 @@ class YamlOutputParser:
     >>> text = "```yaml\nname: John\nage: 42\n```"
     >>> model = parser.parse(text)
     >>> model.name
-    
+
     """
 
     def __init__(self, pydantic_object: Type[T]):
         self.pydantic_object = pydantic_object
-        self.pattern = re.compile(r"^```(?:ya?ml)?(?P<yaml>[^`]*)", re.MULTILINE | re.DOTALL)
+        self.pattern = re.compile(
+            r"^```(?:ya?ml)?(?P<yaml>[^`]*)", re.MULTILINE | re.DOTALL
+        )
 
     def parse(self, text: str) -> T:
         """Parse the provided text to extract and validate YAML data.
@@ -59,7 +63,10 @@ class YamlOutputParser:
 
         except (yaml.YAMLError, ValidationError) as e:
             name = self.pydantic_object.__name__
-            msg = f"Failed to parse {name} from text '{text}'. Error: {e}"
+            msg = (
+                f"Failed to parse {name} from text '{text}'."
+                f" Error: {e}"
+            )
             raise YamlParsingException(msg) from e
 
     def get_format_instructions(self) -> str:
@@ -69,8 +76,14 @@ class YamlOutputParser:
             A string containing formatting instructions.
         """
         schema = self.pydantic_object.schema()
-        reduced_schema = {k: v for k, v in schema.items() if k not in ['title', 'type']}
+        reduced_schema = {
+            k: v
+            for k, v in schema.items()
+            if k not in ["title", "type"]
+        }
         schema_str = json.dumps(reduced_schema, indent=4)
 
-        format_instructions = f"YAML Formatting Instructions:\n{schema_str}"
+        format_instructions = (
+            f"YAML Formatting Instructions:\n{schema_str}"
+        )
         return format_instructions