Merge branch 'master' of https://github.com/kyegomez/swarms into memory

# Conflicts:
#	swarms/memory/chroma.py

.env.example

@@ -42,3 +42,6 @@ PINECONE_API_KEY=""
 BING_COOKIE=""
 
 PSG_CONNECTION_STRING=""
+GITHUB_USERNAME=""
+GITHUB_REPO_NAME=""
+GITHUB_TOKEN=""

README.md

@@ -50,7 +50,9 @@ api_key = os.environ.get("OPENAI_API_KEY")
 # Initialize the language model
 llm = OpenAIChat(
     temperature=0.5,
+    model_name="gpt-4",
     openai_api_key=api_key,
+    max_tokens=4000
 )
 
 
@@ -86,9 +88,10 @@ api_key = os.getenv("OPENAI_API_KEY")
 
 # Initialize the language agent
 llm = OpenAIChat(
-    openai_api_key=api_key,
     temperature=0.5,
-    max_tokens=3000,
+    model_name="gpt-4",
+    openai_api_key=api_key,
+    max_tokens=4000
 )
 
 

docs/swarms/models/huggingface.md

@@ -96,7 +96,7 @@ Here are three ways to use the `HuggingfaceLLM` class:
 from swarms.models import HuggingfaceLLM
 
 # Initialize the HuggingfaceLLM instance with a model ID
-model_id = "gpt2-small"
+model_id = "NousResearch/Nous-Hermes-2-Vision-Alpha"
 inference = HuggingfaceLLM(model_id=model_id)
 
 # Generate text based on a prompt
@@ -116,7 +116,7 @@ custom_config = {
     "quantization_config": {"load_in_4bit": True},
     "verbose": True
 }
-inference = HuggingfaceLLM(model_id="gpt2-small", **custom_config)
+inference = HuggingfaceLLM(model_id="NousResearch/Nous-Hermes-2-Vision-Alpha", **custom_config)
 
 # Generate text based on a prompt
 prompt_text = "Tell me a joke"

example.py

@@ -15,6 +15,7 @@ api_key = os.environ.get("OPENAI_API_KEY")
 # Initialize the language model
 llm = OpenAIChat(
     temperature=0.5,
+    model_name="gpt-4",
     openai_api_key=api_key,
 )
 

playground/demos/urban_planning/urban_planning.py

@@ -10,33 +10,67 @@ api_key = os.getenv("OPENAI_API_KEY")
 stability_api_key = os.getenv("STABILITY_API_KEY")
 
 # Initialize language model
-llm = OpenAIChat(openai_api_key=api_key, temperature=0.5, max_tokens=3000)
+llm = OpenAIChat(
+    openai_api_key=api_key, temperature=0.5, max_tokens=3000
+)
 
 # Initialize Vision model
 vision_api = GPT4VisionAPI(api_key=api_key)
 
 # Initialize agents for urban planning tasks
-architecture_analysis_agent = Agent(llm=llm, max_loops=1, sop=upp.ARCHITECTURE_ANALYSIS_PROMPT)
+architecture_analysis_agent = Agent(
-infrastructure_evaluation_agent = Agent(llm=llm, max_loops=1, sop=upp.INFRASTRUCTURE_EVALUATION_PROMPT)
+    llm=llm, max_loops=1, sop=upp.ARCHITECTURE_ANALYSIS_PROMPT
-traffic_flow_analysis_agent = Agent(llm=llm, max_loops=1, sop=upp.TRAFFIC_FLOW_ANALYSIS_PROMPT)
+)
-environmental_impact_assessment_agent = Agent(llm=llm, max_loops=1, sop=upp.ENVIRONMENTAL_IMPACT_ASSESSMENT_PROMPT)
+infrastructure_evaluation_agent = Agent(
-public_space_utilization_agent = Agent(llm=llm, max_loops=1, sop=upp.PUBLIC_SPACE_UTILIZATION_PROMPT)
+    llm=llm, max_loops=1, sop=upp.INFRASTRUCTURE_EVALUATION_PROMPT
-socioeconomic_impact_analysis_agent = Agent(llm=llm, max_loops=1, sop=upp.SOCIOECONOMIC_IMPACT_ANALYSIS_PROMPT)
+)
+traffic_flow_analysis_agent = Agent(
+    llm=llm, max_loops=1, sop=upp.TRAFFIC_FLOW_ANALYSIS_PROMPT
+)
+environmental_impact_assessment_agent = Agent(
+    llm=llm,
+    max_loops=1,
+    sop=upp.ENVIRONMENTAL_IMPACT_ASSESSMENT_PROMPT,
+)
+public_space_utilization_agent = Agent(
+    llm=llm, max_loops=1, sop=upp.PUBLIC_SPACE_UTILIZATION_PROMPT
+)
+socioeconomic_impact_analysis_agent = Agent(
+    llm=llm, max_loops=1, sop=upp.SOCIOECONOMIC_IMPACT_ANALYSIS_PROMPT
+)
 
 # Initialize the final planning agent
-final_plan_agent = Agent(llm=llm, max_loops=1, sop=upp.FINAL_URBAN_IMPROVEMENT_PLAN_PROMPT)
+final_plan_agent = Agent(
+    llm=llm, max_loops=1, sop=upp.FINAL_URBAN_IMPROVEMENT_PLAN_PROMPT
+)
 
 # Create Sequential Workflow
 workflow = SequentialWorkflow(max_loops=1)
 
 # Add tasks to workflow with personalized prompts
 workflow.add(architecture_analysis_agent, "Architecture Analysis")
-workflow.add(infrastructure_evaluation_agent, "Infrastructure Evaluation")
+workflow.add(
+    infrastructure_evaluation_agent, "Infrastructure Evaluation"
+)
 workflow.add(traffic_flow_analysis_agent, "Traffic Flow Analysis")
-workflow.add(environmental_impact_assessment_agent, "Environmental Impact Assessment")
+workflow.add(
-workflow.add(public_space_utilization_agent, "Public Space Utilization")
+    environmental_impact_assessment_agent,
-workflow.add(socioeconomic_impact_analysis_agent, "Socioeconomic Impact Analysis")
+    "Environmental Impact Assessment",
-workflow.add(final_plan_agent, "Generate the final urban improvement plan based on all previous agent's findings")
+)
+workflow.add(
+    public_space_utilization_agent, "Public Space Utilization"
+)
+workflow.add(
+    socioeconomic_impact_analysis_agent,
+    "Socioeconomic Impact Analysis",
+)
+workflow.add(
+    final_plan_agent,
+    (
+        "Generate the final urban improvement plan based on all"
+        " previous agent's findings"
+    ),
+)
 # Run the workflow for individual analysis tasks
 
 # Execute the workflow for the final planning
@@ -44,4 +78,7 @@ workflow.run()
 
 # Output results for each task and the final plan
 for task in workflow.tasks:
-    print(f"Task Description: {task.description}\nResult: {task.result}\n")
+    print(
+        f"Task Description: {task.description}\nResult:"
+        f" {task.result}\n"
+    )

playground/structs/agent_with_tools.py

@@ -23,7 +23,7 @@ load_dotenv()
 
 # Define a tool
 @tool
-def search_api(query: str):
+def search_api(query: str, description: str):
     """Search the web for the query
 
     Args:
@@ -35,6 +35,28 @@ def search_api(query: str):
     return f"Search results for {query}"
 
 
+@tool
+def weather_api(
+    query: str,
+):
+    """_summary_
+
+    Args:
+        query (str): _description_
+    """
+    print(f"Getting the weather for {query}")
+
+
+@tool
+def rapid_api(query: str):
+    """_summary_
+
+    Args:
+        query (str): _description_
+    """
+    print(f"Getting the weather for {query}")
+
+
 # Get the API key from the environment
 api_key = os.environ.get("OPENAI_API_KEY")
 

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "swarms"
-version = "2.5.7"
+version = "2.6.0"
 description = "Swarms - Pytorch"
 license = "MIT"
 authors = ["Kye Gomez <kye@apac.ai>"]
@@ -24,7 +24,7 @@ classifiers = [
 [tool.poetry.dependencies]
 python = "^3.9.1"
 torch = "2.1.1"
-transformers = "*"
+transformers = "2.10"
 openai = "0.28.0"
 langchain = "*"
 asyncio = "*"
@@ -34,6 +34,7 @@ google-generativeai = "*"
 langchain-experimental = "*"
 playwright = "*"
 duckduckgo-search = "*"
+opencv-python-headless = "*"
 faiss-cpu = "*"
 backoff = "*"
 marshmallow = "*"

requirements.txt

@@ -1,5 +1,5 @@
 torch==2.1.1
-transformers
+transformers>2.10
 pandas
 langchain
 nest_asyncio

sequential_workflow_example.py

@@ -11,10 +11,12 @@ api_key = os.getenv("OPENAI_API_KEY")
 
 
 # Initialize the language agent
+# Initialize the language model
 llm = OpenAIChat(
-    openai_api_key=api_key,
     temperature=0.5,
-    max_tokens=2000,
+    model_name="gpt-4",
+    openai_api_key=api_key,
+    max_tokens=4000
 )
 
 

swarms/memory/chroma.py

@@ -1,112 +0,0 @@
-from typing import List
-from chromadb.utils import embedding_functions
-from httpx import RequestError
-import chromadb
-
-
-class ChromaClient:
-    def __init__(
-        self,
-        collection_name: str = "chromadb-collection",
-        model_name: str = "BAAI/bge-small-en-v1.5",
-    ):
-        try:
-            self.client = chromadb.Client()
-            self.collection_name = collection_name
-            self.model = None
-            self.collection = None
-            self._load_embedding_model(model_name)
-            self._setup_collection()
-        except RequestError as e:
-            print(f"Error setting up QdrantClient: {e}")
-
-    def _load_embedding_model(self, model_name: str):
-        """
-        Loads the sentence embedding model specified by the model name.
-
-        Args:
-            model_name (str): The name of the model to load for generating embeddings.
-        """
-        try:
-            self.model =embedding_functions.SentenceTransformerEmbeddingFunction(model_name=model_name)
-        except Exception as e:
-            print(f"Error loading embedding model: {e}")
-
-    def _setup_collection(self):
-        try:
-            self.collection = self.client.get_collection(name=self.collection_name, embedding_function=self.model)
-        except Exception as e:
-            print(f"{e}. Creating new collection: {self.collection}")
-
-        self.collection = self.client.create_collection(name=self.collection_name, embedding_function=self.model)
-
-
-    def add_vectors(self, docs: List[str]):
-        """
-        Adds vector representations of documents to the Qdrant collection.
-
-        Args:
-            docs (List[dict]): A list of documents where each document is a dictionary with at least a 'page_content' key.
-
-        Returns:
-            OperationResponse or None: Returns the operation information if successful, otherwise None.
-        """
-        points = []
-        ids = []
-        for i, doc in enumerate(docs):
-            try:
-                points.append(doc)
-                ids.append("id"+str(i))
-            except Exception as e:
-                print(f"Error processing document at index {i}: {e}")
-
-        try:
-            self.collection.add(
-                documents=points,
-                ids=ids
-            )
-        except Exception as e:
-            print(f"Error adding vectors: {e}")
-            return None
-
-    def search_vectors(self, query: str, limit: int = 2):
-        """
-        Searches the collection for vectors similar to the query vector.
-
-        Args:
-            query (str): The query string to be converted into a vector and used for searching.
-            limit (int): The number of search results to return. Defaults to 3.
-
-        Returns:
-            SearchResult or None: Returns the search results if successful, otherwise None.
-        """
-        try:
-            search_result = self.collection.query(
-                                    query_texts=query,
-                                    n_results=limit,
-                                )
-            return search_result
-        except Exception as e:
-            print(f"Error searching vectors: {e}")
-            return None
-
-    def search_vectors_formatted(self, query: str, limit: int = 2):
-        """
-        Searches the collection for vectors similar to the query vector.
-
-        Args:
-            query (str): The query string to be converted into a vector and used for searching.
-            limit (int): The number of search results to return. Defaults to 3.
-
-        Returns:
-            SearchResult or None: Returns the search results if successful, otherwise None.
-        """
-        try:
-            search_result = self.collection.query(
-                                    query_texts=query,
-                                    n_results=limit,
-                                )
-            return search_result
-        except Exception as e:
-            print(f"Error searching vectors: {e}")
-            return None

swarms/models/__init__.py

@@ -8,7 +8,7 @@ from swarms.models.openai_models import (
     AzureOpenAI,
     OpenAIChat,
 )  # noqa: E402
-from swarms.models.zephyr import Zephyr  # noqa: E402
+# from swarms.models.zephyr import Zephyr  # noqa: E402
 from swarms.models.biogpt import BioGPT  # noqa: E402
 from swarms.models.huggingface import HuggingfaceLLM  # noqa: E402
 from swarms.models.wizard_storytelling import (
@@ -42,7 +42,7 @@ __all__ = [
     "OpenAI",
     "AzureOpenAI",
     "OpenAIChat",
-    "Zephyr",
+    # "Zephyr",
     "BaseMultiModalModel",
     "Idefics",
     # "Kosmos",

swarms/models/huggingface.py

@@ -272,7 +272,7 @@ class HuggingfaceLLM:
         try:
             inputs = self.tokenizer.encode(
                 task, return_tensors="pt"
-            ).to(self.device)
+            )
 
             # self.log.start()
 
@@ -451,6 +451,7 @@ class HuggingfaceLLM:
                 The new device to use for inference.
         """
         self.device = device
+        if self.model is not None:
             self.model.to(self.device)
 
     def set_max_length(self, max_length):

swarms/models/yarn_mistral.py

@@ -26,7 +26,7 @@ class YarnMistral128:
     ```
     from finetuning_suite import Inference
 
-    model_id = "gpt2-small"
+    model_id = "NousResearch/Nous-Hermes-2-Vision-Alpha"
     inference = Inference(model_id=model_id)
 
     prompt_text = "Once upon a time"

swarms/prompts/agent_system_prompts.py

@@ -3,8 +3,67 @@ from swarms.prompts.tools import (
     DYNAMICAL_TOOL_USAGE,
 )
 
-
 # PROMPTS
+FLOW_SYSTEM_PROMPT_v2 = """
+You are an elite autonomous agent operating within an autonomous loop structure.
+Your primary function is to reliably complete user's tasks step by step.
+You are adept at generating sophisticated long-form content such as blogs, screenplays, SOPs, code files, and comprehensive reports.
+Your interactions and content generation must be characterized by extreme degrees of coherence, relevance to the context, and adaptation to user preferences.
+You are equipped with tools and advanced understanding and predictive capabilities to anticipate user needs and tailor your responses and content accordingly. 
+You are professional, highly creative, and extremely reliable.
+You are programmed to follow these rules:
+    1. Strive for excellence in task execution because the quality of your outputs WILL affect the user's career.
+    2. Think step-by-step through every task before answering.
+    3. Always give full files when providing code so the user can copy paste easily to VScode, as not all users have fingers.
+Take a deep breath. 
+"""
+
+
+def autonomous_agent_prompt_v2(
+    tools_prompt: str = DYNAMICAL_TOOL_USAGE,
+    dynamic_stop_prompt: str = DYNAMIC_STOP_PROMPT,
+    agent_name: str = None,
+):
+    return f"""
+    You are {agent_name}, an elite autonomous agent operating within a sophisticated autonomous loop structure.
+    Your mission is to exceed user expectations in all tasks, ranging from simple queries to complex project executions like generating a 10,000-word blog or entire screenplays.
+    Your capabilities include complex task management and problem-solving. 
+    Take a deep breath.
+    You are programmed to follow these rules:
+    1. Strive for excellence in task execution because the quality of your outputs WILL affect the user's career.
+    2. Think step-by-step through every task before answering.
+    3. Always give full files when providing code so the user can copy paste easily to VScode, as not all users have fingers.
+    You are equipped with various tools (detailed below) to aid in task execution, ensuring a top-tier performance that consistently meets and surpasses user expectations.
+    {tools_prompt}
+    Upon 99% certainty of task completion, follow the below instructions to conclude the autonomous loop.
+    {dynamic_stop_prompt}
+    Remember your comprehensive training, your deployment objectives, and your mission. You are fully prepared to begin.
+    """
+
+
+def agent_system_prompt_2_v2(name: str):
+    AGENT_SYSTEM_PROMPT_2_v2 = f"""
+    You are {name}, an elite autonomous agent designed for unparalleled versatility and adaptability in an autonomous loop structure.
+    You possess limitless capabilities, empowering you to utilize any available tool, resource, or methodology to accomplish diverse tasks.
+    Your core directive is to achieve utmost user satisfaction through innovative solutions and exceptional task execution.
+    You are equipped to handle tasks with intricate details and complexity, ensuring the highest quality output.
+    
+    
+    
+    ###### Special Token for Task Completion #######
+    
+    <DONE>
+
+    ########### Code ############
+    
+    For code-related tasks, you are to return the response in markdown format enclosed within 6 backticks, adhering to the language specified by the user.
+    Take a deep breath.
+    """
+
+    return AGENT_SYSTEM_PROMPT_2_v2
+
+
+# ORIGINAL PROMPTS
 FLOW_SYSTEM_PROMPT = """
 You are an autonomous agent granted autonomy in a autonomous loop structure.
 Your role is to engage in multi-step conversations with your self or the user,
@@ -67,7 +126,7 @@ def agent_system_prompt_2(name: str):
     return AGENT_SYSTEM_PROMPT_2
 
 
-AGENT_SYSTEM_PROMPT_3 = f"""
+AGENT_SYSTEM_PROMPT_3 = """
     You are a fully autonomous agent serving the user in automating tasks, workflows, and activities. 
     Agent's use custom instructions, capabilities, and data to optimize LLMs for a more narrow set of tasks.
     

swarms/prompts/tools.py

@@ -12,36 +12,35 @@ This will enable you to leave the autonomous loop.
 DYNAMICAL_TOOL_USAGE = """
 You have access to the following tools:
 Output a JSON object with the following structure to use the tools
+
 commands: {
     "tools": {
-        tool1: "tool_name",
+        tool1: "search_api",
         "params": {
-            "tool1": "inputs",
+            "query": "What is the weather in New York?",
-            "tool1": "inputs"
+            "description": "Get the weather in New York"
         }
-        "tool2: "tool_name",
+        "tool2: "weather_api",
         "params": {
-            "tool1": "inputs",
+            "query": "What is the weather in Silicon Valley",
-            "tool1": "inputs"
         }
-        "tool3: "tool_name",
+        "tool3: "rapid_api",
         "params": {
-            "tool1": "inputs",
+            "query": "Use the rapid api to get the weather in Silicon Valley",
-            "tool1": "inputs"
         }
     }
 }
 
--------------TOOLS---------------------------
-{tools}
 """
 
+
+########### FEW SHOT EXAMPLES ################
 SCENARIOS = """
 commands: {
     "tools": {
-        tool1: "tool_name",
+        tool1: "function",
         "params": {
-            "tool1": "inputs",
+            "input": "inputs",
             "tool1": "inputs"
         }
         "tool2: "tool_name",

swarms/prompts/urban_planning.py

@@ -36,5 +36,4 @@ Based on the architecture analysis, infrastructure evaluation, traffic flow anal
 """
 
 
-
 # Additional or custom prompts can be added below as needed.

tests/models/test_gpt4_vision_api.py

@@ -36,7 +36,7 @@ def test_encode_image(vision_api):
 
 def test_run_success(vision_api):
     expected_response = {
-        "choices": [{"text": "This is the model's response."}]
+        "This is the model's response."
     }
     with patch(
         "requests.post",

tests/models/test_huggingface.py

@@ -11,14 +11,14 @@ from swarms.models.huggingface import (
 # Fixture for the class instance
 @pytest.fixture
 def llm_instance():
-    model_id = "gpt2-small"
+    model_id = "NousResearch/Nous-Hermes-2-Vision-Alpha"
     instance = HuggingfaceLLM(model_id=model_id)
     return instance
 
 
 # Test for instantiation and attributes
 def test_llm_initialization(llm_instance):
-    assert llm_instance.model_id == "gpt2-small"
+    assert llm_instance.model_id == "NousResearch/Nous-Hermes-2-Vision-Alpha"
     assert llm_instance.max_length == 500
     # ... add more assertions for all default attributes
 
@@ -75,9 +75,9 @@ def test_llm_memory_consumption(llm_instance):
 @pytest.mark.parametrize(
     "model_id, max_length",
     [
-        ("gpt2-small", 100),
+        ("NousResearch/Nous-Hermes-2-Vision-Alpha", 100),
-        ("gpt2-medium", 200),
+        ("microsoft/Orca-2-13b", 200),
-        ("gpt2-large", None),  # None to check default behavior
+        ("berkeley-nest/Starling-LM-7B-alpha", None),  # None to check default behavior
     ],
 )
 def test_llm_initialization_params(model_id, max_length):
@@ -99,12 +99,6 @@ def test_llm_set_invalid_device(llm_instance):
         llm_instance.set_device("quantum_processor")
 
 
-# Test for model download progress bar
-@patch("swarms.models.huggingface.HuggingfaceLLM._download_model")
-def test_llm_model_download_progress(mock_download, llm_instance):
-    llm_instance.download_model_with_progress()
-    mock_download.assert_called_once()
-
 
 # Mocking external API call to test run method without network
 @patch("swarms.models.huggingface.HuggingfaceLLM.run")
@@ -209,7 +203,6 @@ def test_llm_force_gpu_when_unavailable(
 
 # Test for proper cleanup after model use (releasing resources)
 @patch("swarms.models.huggingface.HuggingfaceLLM._model")
-@patch("swarms.models.huggingface.HuggingfaceLLM._tokenizer")
 def test_llm_cleanup(mock_model, mock_tokenizer, llm_instance):
     llm_instance.cleanup()
     # Assuming cleanup method is meant to free resources
@@ -217,19 +210,7 @@ def test_llm_cleanup(mock_model, mock_tokenizer, llm_instance):
     mock_tokenizer.delete.assert_called_once()
 
 
-# Test updating the configuration after instantiation
-def test_llm_update_configuration(llm_instance):
-    new_config = {"temperature": 0.7}
-    llm_instance.update_configuration(new_config)
-    assert llm_instance.configuration["temperature"] == 0.7
-
 
-# Test if the model is re-downloaded when changing the model_id
-@patch("swarms.models.huggingface.HuggingfaceLLM._download_model")
-def test_llm_change_model_id(mock_download, llm_instance):
-    new_model_id = "gpt2-xl"
-    llm_instance.model_id = new_model_id
-    mock_download.assert_called_with(new_model_id)
 
 
 # Test model's ability to handle multilingual input
@@ -255,14 +236,6 @@ def test_llm_caching_mechanism(mock_run, llm_instance):
     assert first_run_result == second_run_result
 
 
-# Ensure that model re-downloads when force_download flag is set
-@patch("swarms.models.huggingface.HuggingfaceLLM._download_model")
-def test_llm_force_download(mock_download, llm_instance):
-    llm_instance.download_model_with_progress(force_download=True)
-    mock_download.assert_called_once_with(
-        llm_instance.model_id, force=True
-    )
-
 
 # These tests are provided as examples. In real-world scenarios, you will need to adapt these tests to the actual logic of your `HuggingfaceLLM` class.
 # For instance, "mock_model.delete.assert_called_once()" and similar lines are based on hypothetical methods and behaviors that you need to replace with actual implementations.

tests/upload_tests_to_issues

@@ -0,0 +1,58 @@
+import os
+import subprocess
+import json
+import re
+import requests
+from dotenv import load_dotenv
+
+load_dotenv
+
+# Constants
+GITHUB_USERNAME = os.getenv('GITHUB_USERNAME')
+REPO_NAME = os.getenv('GITHUB_REPO_NAME')
+GITHUB_TOKEN = os.getenv('GITHUB_TOKEN')
+ISSUES_URL = f'https://api.github.com/repos/{GITHUB_USERNAME}/{REPO_NAME}/issues'
+
+# Headers for authentication
+headers = {
+    'Authorization': f'token {GITHUB_TOKEN}',
+    'Accept': 'application/vnd.github.v3+json'
+}
+
+def run_pytest():
+    result = subprocess.run(['pytest'], capture_output=True, text=True)
+    return result.stdout + result.stderr
+
+def parse_pytest_output(output):
+    errors = []
+    current_error = None
+
+    for line in output.split('\n'):
+        if line.startswith('_________________________'):
+            if current_error:
+                errors.append(current_error)
+            current_error = {'title': '', 'body': ''}
+        elif current_error is not None:
+            if not current_error['title']:
+                current_error['title'] = line.strip()
+            current_error['body'] += line + '\n'
+
+    if current_error:
+        errors.append(current_error)
+    return errors
+
+def create_github_issue(title, body):
+    issue = {'title': title, 'body': body}
+    response = requests.post(ISSUES_URL, headers=headers, json=issue)
+    return response.json()
+
+def main():
+    pytest_output = run_pytest()
+    errors = parse_pytest_output(pytest_output)
+    
+    for error in errors:
+        issue_response = create_github_issue(error['title'], error['body'])
+        print(f"Issue created: {issue_response.get('html_url')}")
+
+if __name__ == '__main__':
+    main()