code quality fixes: line length = 80

playground/agents/mm_agent_example.py

@@ -9,6 +9,9 @@
 img = node.run_img("/image1", "What is this image about?")
 
 chat = node.chat(
-    "What is your name? Generate a picture of yourself. What is this image about?",
+    (
+        "What is your name? Generate a picture of yourself. What is this image"
+        " about?"
+    ),
     streaming=True,
 )

playground/agents/revgpt_agent.py

@@ -10,13 +10,19 @@
     "plugin_ids": [os.getenv("REVGPT_PLUGIN_IDS")],
     "disable_history": os.getenv("REVGPT_DISABLE_HISTORY") == "True",
     "PUID": os.getenv("REVGPT_PUID"),
-    "unverified_plugin_domains": [os.getenv("REVGPT_UNVERIFIED_PLUGIN_DOMAINS")],
+    "unverified_plugin_domains": [
+        os.getenv("REVGPT_UNVERIFIED_PLUGIN_DOMAINS")
+    ],
 }
 
 llm = RevChatGPTModel(access_token=os.getenv("ACCESS_TOKEN"), **config)
 
 worker = Worker(ai_name="Optimus Prime", llm=llm)
 
-task = "What were the winning boston marathon times for the past 5 years (ending in 2022)? Generate a table of the year, name, country of origin, and times."
+task = (
+    "What were the winning boston marathon times for the past 5 years (ending"
+    " in 2022)? Generate a table of the year, name, country of origin, and"
+    " times."
+)
 response = worker.run(task)
 print(response)

playground/demos/accountant_team/accountant_team.py

@@ -103,7 +103,8 @@ def run(self):
 
         # Provide decision making support to the accountant
         decision_making_support_agent_output = decision_making_support_agent.run(
-            f"{self.decision_making_support_agent_instructions}: {summary_agent_output}"
+            f"{self.decision_making_support_agent_instructions}:"
+            f" {summary_agent_output}"
         )
 
         return decision_making_support_agent_output
@@ -113,5 +114,7 @@ def run(self):
     pdf_path="tesla.pdf",
     fraud_detection_instructions="Detect fraud in the document",
     summary_agent_instructions="Generate an actionable summary of the document",
-    decision_making_support_agent_instructions="Provide decision making support to the business owner:",
+    decision_making_support_agent_instructions=(
+        "Provide decision making support to the business owner:"
+    ),
 )

playground/demos/ai_research_team/main.py

@@ -48,6 +48,7 @@
 
 paper = pdf_to_text(PDF_PATH)
 algorithmic_psuedocode_agent = paper_summarizer_agent.run(
-    f"Focus on creating the algorithmic pseudocode for the novel method in this paper: {paper}"
+    "Focus on creating the algorithmic pseudocode for the novel method in this"
+    f" paper: {paper}"
 )
 pytorch_code = paper_implementor_agent.run(algorithmic_psuedocode_agent)

playground/demos/autotemp/autotemp.py

@@ -9,11 +9,18 @@ class AutoTemp:
     """
 
     def __init__(
-        self, api_key, default_temp=0.0, alt_temps=None, auto_select=True, max_workers=6
+        self,
+        api_key,
+        default_temp=0.0,
+        alt_temps=None,
+        auto_select=True,
+        max_workers=6,
     ):
         self.api_key = api_key
         self.default_temp = default_temp
-        self.alt_temps = alt_temps if alt_temps else [0.4, 0.6, 0.8, 1.0, 1.2, 1.4]
+        self.alt_temps = (
+            alt_temps if alt_temps else [0.4, 0.6, 0.8, 1.0, 1.2, 1.4]
+        )
         self.auto_select = auto_select
         self.max_workers = max_workers
         self.llm = OpenAIChat(
@@ -62,12 +69,15 @@ def run(self, prompt, temperature_string):
         if not scores:
             return "No valid outputs generated.", None
 
-        sorted_scores = sorted(scores.items(), key=lambda item: item[1], reverse=True)
+        sorted_scores = sorted(
+            scores.items(), key=lambda item: item[1], reverse=True
+        )
         best_temp, best_score = sorted_scores[0]
         best_output = outputs[best_temp]
 
         return (
-            f"Best AutoTemp Output (Temp {best_temp} | Score: {best_score}):\n{best_output}"
+            f"Best AutoTemp Output (Temp {best_temp} | Score:"
+            f" {best_score}):\n{best_output}"
             if self.auto_select
             else "\n".join(
                 f"Temp {temp} | Score: {score}:\n{outputs[temp]}"

playground/demos/blog_gen/blog_gen.py

@@ -7,7 +7,10 @@
 
 class BlogGen:
     def __init__(
-        self, api_key, blog_topic, temperature_range: str = "0.4,0.6,0.8,1.0,1.2"
+        self,
+        api_key,
+        blog_topic,
+        temperature_range: str = "0.4,0.6,0.8,1.0,1.2",
     ):  # Add blog_topic as an argument
         self.openai_chat = OpenAIChat(openai_api_key=api_key, temperature=0.8)
         self.auto_temp = AutoTemp(api_key)
@@ -40,7 +43,10 @@ def run_workflow(self):
             topic_output = topic_result.generations[0][0].text
             print(
                 colored(
-                    f"\nTopic Selection Task Output:\n----------------------------\n{topic_output}\n",
+                    (
+                        "\nTopic Selection Task"
+                        f" Output:\n----------------------------\n{topic_output}\n"
+                    ),
                     "white",
                 )
             )
@@ -58,7 +64,10 @@ def run_workflow(self):
             initial_draft_output = auto_temp_output  # Assuming AutoTemp.run returns the best output directly
             print(
                 colored(
-                    f"\nInitial Draft Output:\n----------------------------\n{initial_draft_output}\n",
+                    (
+                        "\nInitial Draft"
+                        f" Output:\n----------------------------\n{initial_draft_output}\n"
+                    ),
                     "white",
                 )
             )
@@ -71,7 +80,10 @@ def run_workflow(self):
             review_output = review_result.generations[0][0].text
             print(
                 colored(
-                    f"\nReview Output:\n----------------------------\n{review_output}\n",
+                    (
+                        "\nReview"
+                        f" Output:\n----------------------------\n{review_output}\n"
+                    ),
                     "white",
                 )
             )
@@ -80,22 +92,28 @@ def run_workflow(self):
             distribution_prompt = self.DISTRIBUTION_AGENT_SYSTEM_PROMPT.replace(
                 "{{ARTICLE_TOPIC}}", chosen_topic
             )
-            distribution_result = self.openai_chat.generate([distribution_prompt])
+            distribution_result = self.openai_chat.generate(
+                [distribution_prompt]
+            )
             distribution_output = distribution_result.generations[0][0].text
             print(
                 colored(
-                    f"\nDistribution Output:\n----------------------------\n{distribution_output}\n",
+                    (
+                        "\nDistribution"
+                        f" Output:\n----------------------------\n{distribution_output}\n"
+                    ),
                     "white",
                 )
             )
 
             # Final compilation of the blog
-            final_blog_content = (
-                f"{initial_draft_output}\n\n{review_output}\n\n{distribution_output}"
-            )
+            final_blog_content = f"{initial_draft_output}\n\n{review_output}\n\n{distribution_output}"
             print(
                 colored(
-                    f"\nFinal Blog Content:\n----------------------------\n{final_blog_content}\n",
+                    (
+                        "\nFinal Blog"
+                        f" Content:\n----------------------------\n{final_blog_content}\n"
+                    ),
                     "green",
                 )
             )

playground/demos/multi_modal_autonomous_agents/multi_modal_auto_agent.py

@@ -4,7 +4,10 @@
 # Multi Modality Auto Agent
 llm = Idefics(max_length=2000)
 
-task = "User: What is in this image? https://upload.wikimedia.org/wikipedia/commons/8/86/Id%C3%A9fix.JPG"
+task = (
+    "User: What is in this image?"
+    " https://upload.wikimedia.org/wikipedia/commons/8/86/Id%C3%A9fix.JPG"
+)
 
 ## Initialize the workflow
 flow = Flow(

playground/demos/nutrition/nutrition.py

@@ -10,9 +10,16 @@
 openai_api_key = os.getenv("OPENAI_API_KEY")
 
 # Define prompts for various tasks
-MEAL_PLAN_PROMPT = "Based on the following user preferences: dietary restrictions as vegetarian, preferred cuisines as Italian and Indian, a total caloric intake of around 2000 calories per day, and an exclusion of legumes, create a detailed weekly meal plan. Include a variety of meals for breakfast, lunch, dinner, and optional snacks."
+MEAL_PLAN_PROMPT = (
+    "Based on the following user preferences: dietary restrictions as"
+    " vegetarian, preferred cuisines as Italian and Indian, a total caloric"
+    " intake of around 2000 calories per day, and an exclusion of legumes,"
+    " create a detailed weekly meal plan. Include a variety of meals for"
+    " breakfast, lunch, dinner, and optional snacks."
+)
 IMAGE_ANALYSIS_PROMPT = (
-    "Identify the items in this fridge, including their quantities and condition."
+    "Identify the items in this fridge, including their quantities and"
+    " condition."
 )
 
 
@@ -45,15 +52,19 @@ def create_vision_agent(image_path):
                     {"type": "text", "text": IMAGE_ANALYSIS_PROMPT},
                     {
                         "type": "image_url",
-                        "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"},
+                        "image_url": {
+                            "url": f"data:image/jpeg;base64,{base64_image}"
+                        },
                     },
                 ],
             }
         ],
         "max_tokens": 300,
     }
     response = requests.post(
-        "https://api.openai.com/v1/chat/completions", headers=headers, json=payload
+        "https://api.openai.com/v1/chat/completions",
+        headers=headers,
+        json=payload,
     )
     return response.json()
 
@@ -65,10 +76,11 @@ def generate_integrated_shopping_list(
     # Prepare the prompt for the LLM
     fridge_contents = image_analysis["choices"][0]["message"]["content"]
     prompt = (
-        f"Based on this meal plan: {meal_plan_output}, "
-        f"and the following items in the fridge: {fridge_contents}, "
-        f"considering dietary preferences as vegetarian with a preference for Italian and Indian cuisines, "
-        f"generate a comprehensive shopping list that includes only the items needed."
+        f"Based on this meal plan: {meal_plan_output}, and the following items"
+        f" in the fridge: {fridge_contents}, considering dietary preferences as"
+        " vegetarian with a preference for Italian and Indian cuisines,"
+        " generate a comprehensive shopping list that includes only the items"
+        " needed."
     )
 
     # Send the prompt to the LLM and return the response
@@ -94,7 +106,9 @@ def generate_integrated_shopping_list(
 }
 
 # Generate Meal Plan
-meal_plan_output = meal_plan_agent.run(f"Generate a meal plan: {user_preferences}")
+meal_plan_output = meal_plan_agent.run(
+    f"Generate a meal plan: {user_preferences}"
+)
 
 # Vision Agent - Analyze an Image
 image_analysis_output = create_vision_agent("full_fridge.jpg")

playground/demos/positive_med/positive_med.py

@@ -39,9 +39,9 @@ def get_review_prompt(article):
 
 
 def social_media_prompt(article: str, goal: str = "Clicks and engagement"):
-    prompt = SOCIAL_MEDIA_SYSTEM_PROMPT_AGENT.replace("{{ARTICLE}}", article).replace(
-        "{{GOAL}}", goal
-    )
+    prompt = SOCIAL_MEDIA_SYSTEM_PROMPT_AGENT.replace(
+        "{{ARTICLE}}", article
+    ).replace("{{GOAL}}", goal)
     return prompt
 
 
@@ -50,7 +50,8 @@ def social_media_prompt(article: str, goal: str = "Clicks and engagement"):
     "Generate 10 topics on gaining mental clarity using ancient practices"
 )
 topics = llm(
-    f"Your System Instructions: {TOPIC_GENERATOR}, Your current task: {topic_selection_task}"
+    f"Your System Instructions: {TOPIC_GENERATOR}, Your current task:"
+    f" {topic_selection_task}"
 )
 
 dashboard = print(
@@ -109,7 +110,9 @@ def social_media_prompt(article: str, goal: str = "Clicks and engagement"):
 
 
 # Agent that publishes on social media
-distribution_agent = llm(social_media_prompt(draft_blog, goal="Clicks and engagement"))
+distribution_agent = llm(
+    social_media_prompt(draft_blog, goal="Clicks and engagement")
+)
 distribution_agent_out = print(
     colored(
         f"""

playground/models/bioclip.py

@@ -1,6 +1,8 @@
 from swarms.models.bioclip import BioClip
 
-clip = BioClip("hf-hub:microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224")
+clip = BioClip(
+    "hf-hub:microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224"
+)
 
 labels = [
     "adenocarcinoma histopathology",

playground/models/idefics.py

@@ -2,11 +2,17 @@
 
 model = idefics()
 
-user_input = "User: What is in this image? https://upload.wikimedia.org/wikipedia/commons/8/86/Id%C3%A9fix.JPG"
+user_input = (
+    "User: What is in this image?"
+    " https://upload.wikimedia.org/wikipedia/commons/8/86/Id%C3%A9fix.JPG"
+)
 response = model.chat(user_input)
 print(response)
 
-user_input = "User: And who is that? https://static.wikia.nocookie.net/asterix/images/2/25/R22b.gif/revision/latest?cb=20110815073052"
+user_input = (
+    "User: And who is that?"
+    " https://static.wikia.nocookie.net/asterix/images/2/25/R22b.gif/revision/latest?cb=20110815073052"
+)
 response = model.chat(user_input)
 print(response)
 

playground/models/llama_function_caller.py

@@ -28,7 +28,9 @@ def get_weather(location: str, format: str) -> str:
 )
 
 # Call the function
-result = llama_caller.call_function("get_weather", location="Paris", format="Celsius")
+result = llama_caller.call_function(
+    "get_weather", location="Paris", format="Celsius"
+)
 print(result)
 
 # Stream a user prompt

playground/models/vilt.py

@@ -3,5 +3,6 @@
 model = Vilt()
 
 output = model(
-    "What is this image", "http://images.cocodataset.org/val2017/000000039769.jpg"
+    "What is this image",
+    "http://images.cocodataset.org/val2017/000000039769.jpg",
 )

playground/structs/flow_tools.py

@@ -30,7 +30,9 @@ async def async_load_playwright(url: str) -> str:
 
             text = soup.get_text()
             lines = (line.strip() for line in text.splitlines())
-            chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
+            chunks = (
+                phrase.strip() for line in lines for phrase in line.split("  ")
+            )
             results = "\n".join(chunk for chunk in chunks if chunk)
         except Exception as e:
             results = f"Error: {e}"
@@ -58,5 +60,6 @@ def browse_web_page(url: str) -> str:
 )
 
 out = flow.run(
-    "Generate a 10,000 word blog on mental clarity and the benefits of meditation."
+    "Generate a 10,000 word blog on mental clarity and the benefits of"
+    " meditation."
 )