clean up of pdf to text and acount swarm

playground/demos/accountant_team/accountant_team.py

@@ -1,13 +1,27 @@
-from swarms.models.nougat import Nougat
-from swarms.structs import Flow
-from swarms.models import OpenAIChat, Anthropic
+import os
 from typing import List
 
+from dotenv import load_dotenv
+
+from swarms.models import Anthropic, OpenAIChat
+from swarms.structs import Flow
+from swarms.utils.pdf_to_text import pdf_to_text
+
+
+# Environment variables
+load_dotenv()
+anthropic_api_key = os.getenv("ANTHROPIC_API_KEY")
+openai_api_key = os.getenv("OPENAI_API_KEY")
+
 
 # Base llms
-llm1 = OpenAIChat()
-llm2 = Anthropic()
-nougat = Nougat()
+llm1 = OpenAIChat(
+    openai_api_key=openai_api_key,
+)
+
+llm2 = Anthropic(
+    anthropic_api_key=anthropic_api_key,
+)
 
 
 # Prompts for each agent
@@ -67,23 +81,27 @@ class AccountantSwarms:
 
     def __init__(
         self,
-        financial_document_img: str,
-        financial_document_list_img: List[str] = None,
+        pdf_path: str,
+        list_pdfs: List[str] = None,
         fraud_detection_instructions: str = None,
         summary_agent_instructions: str = None,
         decision_making_support_agent_instructions: str = None,
     ):
         super().__init__()
-        self.financial_document_img = financial_document_img
+        self.pdf_path = pdf_path
+        self.list_pdfs = list_pdfs
         self.fraud_detection_instructions = fraud_detection_instructions
         self.summary_agent_instructions = summary_agent_instructions
+        self.decision_making_support_agent_instructions = (
+            decision_making_support_agent_instructions
+        )
 
     def run(self):
-        # Extract text from the image
-        analyzed_doc = self.nougat(self.financial_document_img)
+        # Transform the pdf to text
+        pdf_text = pdf_to_text(self.pdf_path)
 
         # Detect fraud in the document
-        fraud_detection_agent_output = self.fraud_detection_agent(analyzed_doc)
+        fraud_detection_agent_output = self.fraud_detection_agent(pdf_text)
 
         # Generate an actionable summary of the document
         summary_agent_output = self.summary_agent(fraud_detection_agent_output)

playground/worker/ultranode_example.py

@@ -1,15 +0,0 @@
-from swarms import WorkerUltraUltraNode
-
-# Define an objective
-objective = """
-Please make a web GUI for using HTTP API server.
-The name of it is Swarms.
-You can check the server code at ./main.py.
-The server is served on localhost:8000.
-Users should be able to write text input as 'query' and url array as 'files', and check the response.
-Users input form should be delivered in JSON format.
-I want it to have neumorphism-style. Serve it on port 4500.
-"""
-
-node = WorkerUltraUltraNode(objective)
-result = node.execute()

playground/worker/worker.py

@@ -1,17 +0,0 @@
-from langchain.models import OpenAIChat
-from swarms import Worker
-
-llm = OpenAIChat(model_name="gpt-4", openai_api_key="api-key", temperature=0.5)
-
-node = Worker(
-    llm=llm,
-    ai_name="Optimus Prime",
-    ai_role="Worker in a swarm",
-    external_tools=None,
-    human_in_the_loop=False,
-    temperature=0.5,
-)
-
-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 = node.run(task)
-print(response)

playground/worker/worker_auto.py

@@ -1,15 +0,0 @@
-from swarms import worker_node
-
-# Your OpenAI API key
-api_key = "sksdsds"
-
-# Initialize a WorkerNode with your API key
-node = worker_node(api_key)
-
-# Define an objective
-objective = "Please make a web GUI for using HTTP API server..."
-
-# Run the task
-task = node.run(objective)
-
-print(task)

playground/worker/worker_ultra.py

@@ -1,25 +0,0 @@
-import os
-from swarms.swarms.swarms import WorkerUltra
-
-api_key = os.getenv("OPENAI_API_KEY")
-
-# Define an objective
-objective = """
-Please make a web GUI for using HTTP API server.
-The name of it is Swarms.
-You can check the server code at ./main.py.
-The server is served on localhost:8000.
-Users should be able to write text input as 'query' and url array as 'files', and check the response.
-Users input form should be delivered in JSON format.
-I want it to have neumorphism-style. Serve it on port 4500.
-
-"""
-
-# Create an instance of WorkerUltra
-worker = WorkerUltra(objective, api_key)
-
-# Execute the task
-result = worker.execute()
-
-# Print the result
-print(result)

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "swarms"
-version = "2.3.6"
+version = "2.3.7"
 description = "Swarms - Pytorch"
 license = "MIT"
 authors = ["Kye Gomez <kye@apac.ai>"]
@@ -39,6 +39,7 @@ backoff = "*"
 marshmallow = "*"
 datasets = "*"
 diffusers = "*"
+PyPDF2 = "*"
 accelerate = "*"
 sentencepiece = "*"
 wget = "*"

requirements.txt

@@ -27,6 +27,7 @@ huggingface-hub
 google-generativeai
 sentencepiece
 duckduckgo-search
+PyPDF2
 agent-protocol
 accelerate
 chromadb

swarms/structs/flow.py

@@ -151,10 +151,11 @@ class Flow:
         interactive: bool = False,
         dashboard: bool = False,
         agent_name: str = " Autonomous Agent XYZ1B",
+        agent_description: str = None,
         system_prompt: str = FLOW_SYSTEM_PROMPT,
         # tools: List[Any] = None,
         dynamic_temperature: bool = False,
-        SOP: str = None,
+        sop: str = None,
         saved_state_path: Optional[str] = "flow_state.json",
         autosave: bool = False,
         context_length: int = 8192,
@@ -180,13 +181,14 @@ class Flow:
         self.user_name = user_name
         self.context_length = context_length
         # SOPS to inject into the system prompt
-        self.SOP = SOP
+        self.sop = sop
         # The max_loops will be set dynamically if the dynamic_loop
         if self.dynamic_loops:
             self.max_loops = "auto"
         # self.tools = tools or []
         self.system_prompt = system_prompt
         self.agent_name = agent_name
+        self.agent_description = agent_description
         self.saved_state_path = saved_state_path
         self.autosave = autosave
         self.response_filters = []
@@ -402,6 +404,7 @@ class Flow:
         5. Repeat until stopping condition is met or max_loops is reached
 
         """
+        try:
             # dynamic_prompt = self.construct_dynamic_prompt()
             # combined_prompt = f"{dynamic_prompt}\n{task}"
 
@@ -473,6 +476,9 @@ class Flow:
                 return response, history
 
             return response
+        except Exception as error:
+            print(f"Error running flow: {error}")
+            raise
 
     async def arun(self, task: str, **kwargs):
         """
@@ -572,10 +578,24 @@ class Flow:
         Returns:
             str: The agent history prompt
         """
+        if self.sop:
+            system_prompt = system_prompt or self.system_prompt
+            agent_history_prompt = f"""
+                SYSTEM_PROMPT: {system_prompt}
+
+                Follow this standard operating procedure (SOP) to complete tasks:
+                {self.sop}
+                
+                -----------------
+                History of conversations between yourself and your user {self.user_name}: {history}
+            """
+            return agent_history_prompt
+        else:
             system_prompt = system_prompt or self.system_prompt
             agent_history_prompt = f"""
                 SYSTEM_PROMPT: {system_prompt}
 
+
                 History: {history}
             """
             return agent_history_prompt

swarms/utils/__init__.py

@@ -2,10 +2,12 @@ from swarms.utils.display_markdown import display_markdown_message
 from swarms.utils.futures import execute_futures_dict
 from swarms.utils.code_interpreter import SubprocessCodeInterpreter
 from swarms.utils.parse_code import extract_code_in_backticks_in_string
+from swarms.utils.pdf_to_text import pdf_to_text
 
 __all__ = [
     "display_markdown_message",
     "execute_futures_dict",
     "SubprocessCodeInterpreter",
     "extract_code_in_backticks_in_string",
+    "pdf_to_text",
 ]

swarms/utils/pdf_to_text.py

@@ -0,0 +1,44 @@
+import sys
+import os
+
+try:
+    import PyPDF2
+except ImportError:
+    print("PyPDF2 not installed. Please install it using: pip install PyPDF2")
+    sys.exit(1)
+
+
+
+def pdf_to_text(pdf_path):
+    """
+    Converts a PDF file to a string of text.
+
+    Args:
+        pdf_path (str): The path to the PDF file to be converted.
+
+    Returns:
+        str: The text extracted from the PDF.
+
+    Raises:
+        FileNotFoundError: If the PDF file is not found at the specified path.
+        Exception: If there is an error in reading the PDF file.
+    """
+    try:
+        # Open the PDF file
+        with open(pdf_path, 'rb') as file:
+            pdf_reader = PyPDF2.PdfReader(file)
+            text = ""
+
+            # Iterate through each page and extract text
+            for page in pdf_reader.pages:
+                text += page.extract_text() + "\n"
+
+            return text
+    except FileNotFoundError:
+        raise FileNotFoundError(f"The file at {pdf_path} was not found.")
+    except Exception as e:
+        raise Exception(f"An error occurred while reading the PDF file: {e}")
+
+# Example usage
+# text = pdf_to_text("test.pdf")
+# print(text)