feat: Inital tool flow integraton

flow.py

@@ -1,5 +1,6 @@
 from swarms.models import OpenAIChat
 from swarms.structs import Flow
+from swarms.tools.interpreter_tool import compile
 
 api_key = ""
 
@@ -11,21 +12,21 @@ llm = OpenAIChat(
 )
 
 # Initialize the flow
-flow = Flow(llm=llm, max_loops=5, dashboard=True,)
+flow = Flow(llm=llm, max_loops=5, dashboard=True, tools=[compile])
 
-flow = Flow(
+# flow = Flow(
-    llm=llm,
+#     llm=llm,
-    max_loops=5,
+#     max_loops=5,
-    dashboard=True,
+#     dashboard=True,
-    # stopping_condition=None,  # You can define a stopping condition as needed.
+#     # stopping_condition=None,  # You can define a stopping condition as needed.
-    # loop_interval=1,
+#     # loop_interval=1,
-    # retry_attempts=3,
+#     # retry_attempts=3,
-    # retry_interval=1,
+#     # retry_interval=1,
-    # interactive=False,  # Set to 'True' for interactive mode.
+#     # interactive=False,  # Set to 'True' for interactive mode.
-    # dynamic_temperature=False,  # Set to 'True' for dynamic temperature handling.
+#     # dynamic_temperature=False,  # Set to 'True' for dynamic temperature handling.
-)
+# )
 
 
-out = flow.run("Generate a 10,000 word blog on health and wellness.")
+out = flow.run("Use your open interpreter tool to print hello world to the terminal")
 
-print(out)
+print(out)

swarms/structs/flow.py

@@ -8,7 +8,6 @@ TODO:
 
 import json
 import logging
-import re
 import time
 from typing import Any, Callable, Dict, List, Optional, Tuple, Generator
 from termcolor import colored
@@ -16,8 +15,6 @@ import inspect
 import random
 from swarms.tools.tool import BaseTool
 
-from swarms.models.openai_models import OpenAIChat
-
 
 # Constants
 FLOW_SYSTEM_PROMPT = """
@@ -97,8 +94,8 @@ class Flow:
 
     def __init__(
         self,
-        llm: Any,
         # template: str,
+        llm: Any,
         max_loops: int = 5,
         stopping_condition: Optional[Callable[[str], bool]] = None,
         loop_interval: int = 1,
@@ -111,11 +108,6 @@ class Flow:
         **kwargs: Any,
     ):
         # self.template = template
-        self.processors = {
-            'text': self.process_text,
-            'image': self.process_image,
-            'audio': self.process_audio,
-        }
         self.llm = llm
         self.max_loops = max_loops
         self.stopping_condition = stopping_condition
@@ -129,45 +121,22 @@ class Flow:
         self.interactive = interactive
         self.dashboard = dashboard
         self.dynamic_temperature = dynamic_temperature
-        self.tools = tools
+        self.tools = tools or []
-
+    
-    def __call__(self, task, **kwargs):
+    def run(self, task: str, **kwargs):
-        """Invoke the flow by providing a template and its variables."""
+        for i in range(self.max_loops):
-        subtasks = self.break_down_task(task)
+            for tool in self.tools:
-        responses = []
+                tool_prompt = f"\n\nTool: {tool.__name__}\n{tool.__doc__}"
-        for subtask in subtasks:
+                reponse = self.llm(
-            mode = self.determine_mode(subtask)
+                        f"""
-            processor = self.processors.get(mode)
+                    {FLOW_SYSTEM_PROMPT}
-            if processor:
+                    {tool_prompt}
-                refined_prompt = self.text_model(f"Define the task '{subtask}' as it relates to the original task '{task}'.")
-                response = processor(refined_prompt, task)
-                responses.append(response)
-            else:
-                    raise ValueError(f'Invalid mode: {mode}')
-            return responses
-
-    def break_down_task(self, task):
-        # Break down the task into subtasks
-        subtasks = re.split(r' with | and ', task)
-        return subtasks
-
-    def determine_mode(self, subtask):
-        result = self.classifier(subtask, candidate_labels=['text', 'image', 'audio', 'video'])
-        return result['labels'][0]
 
-    def process_image(self, image_description):
+                    History: {reponse}
-        response = self.image_model(image_description)
-        return response
 
-    def process_audio(self, audio_description):
+                    """, **kwargs
-        response = self.audio_model(audio_description)
+                )
-        return response
 
-    def process_video(self, video_description):
-        response = self.video_model(video_description)
-        return response
-        
-        return "Video generated from description: " + video_description
     def provide_feedback(self, feedback: str) -> None:
         """Allow users to provide feedback on the responses."""
         self.feedback.append(feedback)
@@ -179,6 +148,11 @@ class Flow:
             return self.stopping_condition(response)
         return False
 
+    def __call__(self, prompt, **kwargs) -> str:
+        """Invoke the flow by providing a template and its variables."""
+        response = self.llm(prompt, **kwargs)
+        return response
+
     def dynamic_temperature(self):
         """
         1. Check the self.llm object for the temperature

swarms/tools/interpreter_tool.py

@@ -1,7 +1,9 @@
 import os
+from swarms.tools import tool
 import interpreter
 
 
+@tool
 def compile(task: str):
     """
     Open Interpreter lets LLMs run code (Python, Javascript, Shell, and more) locally. You can chat with Open Interpreter through a ChatGPT-like interface in your terminal by running $ interpreter after installing.