diff --git a/simple_example.py b/simple_example.py
deleted file mode 100644
index 1044958a..00000000
--- a/simple_example.py
+++ /dev/null
@@ -1,9 +0,0 @@
-from swarms import Agent
-
-Agent(
- agent_name="Stock-Analysis-Agent",
- model_name="gpt-4o-mini",
- max_loops=1,
- interactive=False,
- streaming_on=True,
-).run("What are 5 hft algorithms")
diff --git a/swarms/structs/spreadsheet_swarm.py b/swarms/structs/spreadsheet_swarm.py
index 9bbb36a7..8215bf2a 100644
--- a/swarms/structs/spreadsheet_swarm.py
+++ b/swarms/structs/spreadsheet_swarm.py
@@ -1,6 +1,6 @@
import asyncio
import csv
-import datetime
+from datetime import datetime
import os
import uuid
from typing import List, Union
@@ -16,14 +16,8 @@ from swarms.utils.loguru_logger import initialize_logger
logger = initialize_logger(log_folder="spreadsheet_swarm")
-time = datetime.datetime.now().isoformat()
-uuid_hex = uuid.uuid4().hex
-
-# --------------- NEW CHANGE START ---------------
-# Format time variable to be compatible across operating systems
-formatted_time = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
-# --------------- NEW CHANGE END ---------------
-
+# Replace timestamp-based time with a UUID for file naming
+run_id = uuid.uuid4().hex # Unique identifier for each run
class AgentOutput(BaseModel):
agent_name: str
@@ -34,13 +28,13 @@ class AgentOutput(BaseModel):
class SwarmRunMetadata(BaseModel):
run_id: str = Field(
- default_factory=lambda: f"spreadsheet_swarm_run_{uuid_hex}"
+ default_factory=lambda: f"spreadsheet_swarm_run_{run_id}"
)
name: str
description: str
agents: List[str]
start_time: str = Field(
- default_factory=lambda: time,
+ default_factory=lambda: str(datetime.now().timestamp()), # Numeric timestamp
description="The start time of the swarm run.",
)
end_time: str
@@ -71,7 +65,7 @@ class SpreadSheetSwarm(BaseSwarm):
def __init__(
self,
name: str = "Spreadsheet-Swarm",
- description: str = "A swarm that that processes tasks concurrently using multiple agents and saves the metadata to a CSV file.",
+ description: str = "A swarm that processes tasks concurrently using multiple agents and saves the metadata to a CSV file.",
agents: Union[Agent, List[Agent]] = [],
autosave_on: bool = True,
save_file_path: str = None,
@@ -94,17 +88,18 @@ class SpreadSheetSwarm(BaseSwarm):
self.max_loops = max_loops
self.workspace_dir = workspace_dir
- # --------------- NEW CHANGE START ---------------
- # The save_file_path now uses the formatted_time and uuid_hex
- self.save_file_path = f"spreadsheet_swarm_{formatted_time}_run_id_{uuid_hex}.csv"
- # --------------- NEW CHANGE END ---------------
+ # Create a timestamp without colons or periods
+ timestamp = datetime.now().isoformat().replace(":", "_").replace(".", "_")
+
+ # Use this timestamp in the CSV filename
+ self.save_file_path = f"spreadsheet_swarm_{timestamp}_run_id_{run_id}.csv"
self.metadata = SwarmRunMetadata(
- run_id=f"spreadsheet_swarm_run_{time}",
+ run_id=f"spreadsheet_swarm_run_{run_id}",
name=name,
description=description,
agents=[agent.name for agent in agents],
- start_time=time,
+ start_time=str(datetime.now().timestamp()), # Numeric timestamp
end_time="",
tasks_completed=0,
outputs=[],
@@ -147,12 +142,18 @@ class SpreadSheetSwarm(BaseSwarm):
"""
logger.info(f"Running the swarm with task: {task}")
- self.metadata.start_time = time
+ self.metadata.start_time = str(datetime.now().timestamp()) # Numeric timestamp
- # Run the asyncio event loop
- asyncio.run(self._run_tasks(task, *args, **kwargs))
+ # Check if we're already in an event loop
+ if asyncio.get_event_loop().is_running():
+ # If so, create and run tasks directly using `create_task` without `asyncio.run`
+ task_future = asyncio.create_task(self._run_tasks(task, *args, **kwargs))
+ asyncio.get_event_loop().run_until_complete(task_future)
+ else:
+ # If no event loop is running, run using `asyncio.run`
+ asyncio.run(self._run_tasks(task, *args, **kwargs))
- self.metadata.end_time = time
+ self.metadata.end_time = str(datetime.now().timestamp()) # Numeric timestamp
# Synchronously save metadata
logger.info("Saving metadata to CSV and JSON...")
@@ -227,7 +228,7 @@ class SpreadSheetSwarm(BaseSwarm):
agent_name=agent_name,
task=task,
result=result,
- timestamp=time,
+ timestamp=str(datetime.now().timestamp()), # Numeric timestamp
)
)
@@ -263,38 +264,19 @@ class SpreadSheetSwarm(BaseSwarm):
"""
Save the swarm metadata to a CSV file.
"""
- logger.info(
- f"Saving swarm metadata to: {self.save_file_path}"
- )
+ logger.info(f"Saving swarm metadata to: {self.save_file_path}")
run_id = uuid.uuid4()
# Check if file exists before opening it
file_exists = os.path.exists(self.save_file_path)
- async with aiofiles.open(
- self.save_file_path, mode="a"
- ) as file:
- writer = csv.writer(file)
-
+ async with aiofiles.open(self.save_file_path, mode="a") as file:
# Write header if file doesn't exist
if not file_exists:
- await writer.writerow(
- [
- "Run ID",
- "Agent Name",
- "Task",
- "Result",
- "Timestamp",
- ]
- )
+ header = "Run ID,Agent Name,Task,Result,Timestamp\n"
+ await file.write(header)
+ # Write each output as a new row
for output in self.metadata.outputs:
- await writer.writerow(
- [
- str(run_id),
- output.agent_name,
- output.task,
- output.result,
- output.timestamp,
- ]
- )
+ row = f"{run_id},{output.agent_name},{output.task},{output.result},{output.timestamp}\n"
+ await file.write(row)
\ No newline at end of file
diff --git a/swarms/structs/ui/ui.py b/swarms/structs/ui/ui.py
index f91008d1..81de9bfb 100644
--- a/swarms/structs/ui/ui.py
+++ b/swarms/structs/ui/ui.py
@@ -8,9 +8,11 @@ import gradio as gr
from swarms.structs.agent import Agent
from swarms.structs.swarm_router import SwarmRouter
from swarms.utils.loguru_logger import initialize_logger
-from swarm_models import OpenAIChat
-import re # Import the regular expression module
-
+import re
+import csv # Import the csv module for csv parsing
+from swarms.utils.litellm_wrapper import LiteLLM
+from litellm import models_by_provider
+from dotenv import set_key, find_dotenv
# Initialize logger
logger = initialize_logger(log_folder="swarm_ui")
@@ -20,19 +22,7 @@ load_dotenv()
# Get the OpenAI API key from the environment variable
api_key = os.getenv("GROQ_API_KEY")
-# changed to swarms_models
-# adding functionality to view other models of swarms models
-
-# Model initialization
-model = OpenAIChat(
- openai_api_base="https://api.groq.com/openai/v1",
- openai_api_key=api_key,
- model_name="llama-3.1-70b-versatile",
- temperature=0.1,
-)
-
# Define the path to agent_prompts.json
-
# locates the json file and then fetches the promopts
PROMPT_JSON_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "agent_prompts.json")
@@ -94,7 +84,8 @@ def load_prompts_from_json() -> Dict[str, str]:
AGENT_PROMPTS = load_prompts_from_json()
-def initialize_agents(dynamic_temp: float, agent_keys: List[str]) -> List[Agent]:
+def initialize_agents(dynamic_temp: float, agent_keys: List[str], model_name: str,
+ provider: str, api_key: str, temperature:float, max_tokens:int) -> List[Agent]:
agents = []
seen_names = set()
for agent_key in agent_keys:
@@ -112,10 +103,18 @@ def initialize_agents(dynamic_temp: float, agent_keys: List[str]) -> List[Agent]
counter += 1
seen_names.add(agent_name)
+ llm = LiteLLM(
+ model_name=model_name,
+ system_prompt=agent_prompt,
+ temperature=temperature,
+ max_tokens = max_tokens,
+ )
+
+
agent = Agent(
agent_name=f"Agent-{agent_name}",
system_prompt=agent_prompt,
- llm=model,
+ llm=llm,
max_loops=1,
autosave=True,
verbose=True,
@@ -132,11 +131,12 @@ def initialize_agents(dynamic_temp: float, agent_keys: List[str]) -> List[Agent]
return agents
async def execute_task(task: str, max_loops: int, dynamic_temp: float,
- swarm_type: str, agent_keys: List[str], flow: str = None) -> Tuple[Any, 'SwarmRouter', str]:
+ swarm_type: str, agent_keys: List[str], flow: str = None, model_name:str = "gpt-4o",
+ provider:str = "openai", api_key:str=None, temperature:float=0.5, max_tokens:int=4000) -> Tuple[Any, 'SwarmRouter', str]:
try:
# Initialize agents
try:
- agents = initialize_agents(dynamic_temp, agent_keys)
+ agents = initialize_agents(dynamic_temp, agent_keys, model_name, provider, api_key, temperature, max_tokens)
except Exception as e:
return None, None, str(e)
@@ -160,27 +160,12 @@ async def execute_task(task: str, max_loops: int, dynamic_temp: float,
if swarm_type == "MixtureOfAgents":
if len(agents) < 2:
return None, None, "MixtureOfAgents requires at least 2 agents"
+
+ if swarm_type == "SequentialWorkflow":
+ if len(agents) < 2:
+ return None, None, "SequentialWorkflow requires at least 2 agents"
- if swarm_type == "SpreadSheetSwarm":
- # spread sheet swarm needs specific setup
- output_dir = "swarm_outputs"
- os.makedirs(output_dir, exist_ok=True)
-
- # Create a sanitized filename using only safe characters
- timestamp = time.strftime("%Y%m%d_%H%M%S")
- output_file = f"swarm_output_{timestamp}.csv"
- output_path = os.path.join(output_dir, output_file)
- # Validate the output path
- try:
- # Ensure the path is valid and writable
- with open(output_path, 'w') as f:
- pass
- os.remove(output_path) # Clean up the test file
- except OSError as e:
- return None, None, str(e)
-
- router_kwargs["output_path"] = output_path
# Create and execute SwarmRouter
try:
timeout = 450 if swarm_type != "SpreadSheetSwarm" else 900 # SpreadSheetSwarm will have different timeout.
@@ -198,13 +183,6 @@ async def execute_task(task: str, max_loops: int, dynamic_temp: float,
timeout=timeout
)
- if swarm_type == "SpreadSheetSwarm":
- # Verify the output file was created
- if not os.path.exists(output_path):
- return None, None, "Output file was not created"
-
- return output_path, router, ""
-
return result, router, ""
except asyncio.TimeoutError:
@@ -248,11 +226,11 @@ def parse_agent_rearrange_output(data: Optional[str]) -> str:
overall_time = parsed_data["time"]
- output = f"**Workflow Execution Details**\n\n"
- output += f"* **Swarm ID:** `{swarm_id}`\n"
- output += f"* **Swarm Name:** `{swarm_name}`\n"
- output += f"* **Agent Flow:** `{agent_flow}`\n\n---\n"
- output += f"**Agent Task Execution**\n\n"
+ output = f"Workflow Execution Details\n\n"
+ output += f"Swarm ID: `{swarm_id}`\n"
+ output += f"Swarm Name: `{swarm_name}`\n"
+ output += f"Agent Flow: `{agent_flow}`\n\n---\n"
+ output += f"Agent Task Execution\n\n"
if "outputs" not in parsed_data:
return "Error: 'outputs' key is missing"
@@ -263,7 +241,7 @@ def parse_agent_rearrange_output(data: Optional[str]) -> str:
if not isinstance(parsed_data["outputs"], list):
return "Error: 'outputs' data is not a list."
- for i, agent_output in enumerate(parsed_data["outputs"], start=1):
+ for i, agent_output in enumerate(parsed_data["outputs"], start=3):
if not isinstance(agent_output, dict):
return f"Error: Agent output at index {i} is not a dictionary"
@@ -280,11 +258,11 @@ def parse_agent_rearrange_output(data: Optional[str]) -> str:
return f"Error: 'steps' data is not a list at index {i}"
agent_name = agent_output["agent_name"]
- output += f"**Run {i} (Agent: `{agent_name}`)**\n\n"
- output += "<details>\n<summary>Show/Hide Agent Steps</summary>\n\n"
+ output += f"Run {(3-i)} (Agent: `{agent_name}`)**\n\n"
+ # output += "<details>\n<summary>Show/Hide Agent Steps</summary>\n\n"
# Iterate over steps
- for j, step in enumerate(agent_output["steps"], start=1):
+ for j, step in enumerate(agent_output["steps"], start=3):
if not isinstance(step, dict):
return f"Error: step at index {j} is not a dictionary at {i} agent output."
@@ -296,15 +274,16 @@ def parse_agent_rearrange_output(data: Optional[str]) -> str:
if "content" not in step:
return f"Error: 'content' key missing at step {j} at {i} agent output."
-
- role = step["role"]
- content = step["content"]
- output += f" **Step {j}:** ({role})\n"
- output += f" > {content}\n\n"
+
+ if step["role"].strip() != "System:": # Filter out system prompts
+ # role = step["role"]
+ content = step["content"]
+ output += f"Step {(3-j)}: \n"
+ output += f"Response :\n {content}\n\n"
- output += "</details>\n\n---\n"
+ # output += "</details>\n\n---\n"
- output += f"**Overall Completion Time:** `{overall_time}`"
+ output += f"Overall Completion Time: `{overall_time}`"
return output
except json.JSONDecodeError as e :
return f"Error during parsing: json.JSONDecodeError {e}"
@@ -312,6 +291,263 @@ def parse_agent_rearrange_output(data: Optional[str]) -> str:
except Exception as e:
return f"Error during parsing: {str(e)}"
+def parse_mixture_of_agents_output(data: Optional[str]) -> str:
+ """Parses the MixtureOfAgents output string and formats it for display."""
+ if data is None:
+ return "Error: No data provided for parsing."
+
+ print(f"Raw data received for parsing:\n{data}") # Debug: Print raw data
+
+ try:
+ parsed_data = json.loads(data)
+
+ if "InputConfig" not in parsed_data or not isinstance(parsed_data["InputConfig"], dict):
+ return "Error: 'InputConfig' data is missing or not a dictionary."
+
+ if "name" not in parsed_data["InputConfig"]:
+ return "Error: 'name' key is missing in 'InputConfig'."
+ if "description" not in parsed_data["InputConfig"]:
+ return "Error: 'description' key is missing in 'InputConfig'."
+
+ if "agents" not in parsed_data["InputConfig"] or not isinstance(parsed_data["InputConfig"]["agents"], list) :
+ return "Error: 'agents' key is missing in 'InputConfig' or not a list."
+
+
+ name = parsed_data["InputConfig"]["name"]
+ description = parsed_data["InputConfig"]["description"]
+
+ output = f"Mixture of Agents Workflow Details\n\n"
+ output += f"Name: `{name}`\n"
+ output += f"Description: `{description}`\n\n---\n"
+ output += f"Agent Task Execution\n\n"
+
+ for agent in parsed_data["InputConfig"]["agents"]:
+ if not isinstance(agent, dict):
+ return "Error: agent is not a dict in InputConfig agents"
+ if "agent_name" not in agent:
+ return "Error: 'agent_name' key is missing in agents."
+
+ if "system_prompt" not in agent:
+ return f"Error: 'system_prompt' key is missing in agents."
+
+ agent_name = agent["agent_name"]
+ # system_prompt = agent["system_prompt"]
+ output += f"Agent: `{agent_name}`\n"
+ # output += f"* **System Prompt:** `{system_prompt}`\n\n"
+
+
+ if "normal_agent_outputs" not in parsed_data or not isinstance(parsed_data["normal_agent_outputs"], list) :
+ return "Error: 'normal_agent_outputs' key is missing or not a list."
+
+ for i, agent_output in enumerate(parsed_data["normal_agent_outputs"], start=3):
+ if not isinstance(agent_output, dict):
+ return f"Error: agent output at index {i} is not a dictionary."
+ if "agent_name" not in agent_output:
+ return f"Error: 'agent_name' key is missing at index {i}"
+ if "steps" not in agent_output:
+ return f"Error: 'steps' key is missing at index {i}"
+
+ if agent_output["steps"] is None:
+ return f"Error: 'steps' is None at index {i}"
+ if not isinstance(agent_output["steps"], list):
+ return f"Error: 'steps' data is not a list at index {i}."
+
+ agent_name = agent_output["agent_name"]
+ output += f"Run {(3-i)} (Agent: `{agent_name}`)\n\n"
+ # output += "<details>\n<summary>Show/Hide Agent Steps</summary>\n\n"
+ for j, step in enumerate(agent_output["steps"], start=3):
+ if not isinstance(step, dict):
+ return f"Error: step at index {j} is not a dictionary at {i} agent output."
+
+ if step is None:
+ return f"Error: step at index {j} is None at {i} agent output."
+
+ if "role" not in step:
+ return f"Error: 'role' key missing at step {j} at {i} agent output."
+
+ if "content" not in step:
+ return f"Error: 'content' key missing at step {j} at {i} agent output."
+
+ if step["role"].strip() != "System:": # Filter out system prompts
+ # role = step["role"]
+ content = step["content"]
+ output += f"Step {(3-j)}: \n"
+ output += f"Response:\n {content}\n\n"
+
+ # output += "</details>\n\n---\n"
+
+ if "aggregator_agent_summary" in parsed_data:
+ output += f"\nAggregated Summary :\n{parsed_data['aggregator_agent_summary']}\n{'=' * 50}\n"
+
+ return output
+
+ except json.JSONDecodeError as e:
+ return f"Error during parsing json.JSONDecodeError : {e}"
+
+ except Exception as e:
+ return f"Error during parsing: {str(e)}"
+
+def parse_sequential_workflow_output(data: Optional[str]) -> str:
+ """Parses the SequentialWorkflow output string and formats it for display."""
+ if data is None:
+ return "Error: No data provided for parsing."
+
+ print(f"Raw data received for parsing:\n{data}") # Debug: Print raw data
+
+ try:
+ parsed_data = json.loads(data)
+
+ if "input" not in parsed_data or not isinstance(parsed_data.get("input"), dict):
+ return "Error: 'input' data is missing or not a dictionary."
+
+ if "swarm_id" not in parsed_data["input"] :
+ return "Error: 'swarm_id' key is missing in the 'input'."
+
+ if "name" not in parsed_data["input"]:
+ return "Error: 'name' key is missing in the 'input'."
+
+ if "flow" not in parsed_data["input"]:
+ return "Error: 'flow' key is missing in the 'input'."
+
+ if "time" not in parsed_data :
+ return "Error: 'time' key is missing."
+
+ swarm_id = parsed_data["input"]["swarm_id"]
+ swarm_name = parsed_data["input"]["name"]
+ agent_flow = parsed_data["input"]["flow"]
+ overall_time = parsed_data["time"]
+
+
+ output = f"Workflow Execution Details\n\n"
+ output += f"Swarm ID: `{swarm_id}`\n"
+ output += f"Swarm Name: `{swarm_name}`\n"
+ output += f"Agent Flow: `{agent_flow}`\n\n---\n"
+ output += f"Agent Task Execution\n\n"
+
+ if "outputs" not in parsed_data:
+ return "Error: 'outputs' key is missing"
+
+ if parsed_data["outputs"] is None:
+ return "Error: 'outputs' data is None"
+
+ if not isinstance(parsed_data["outputs"], list):
+ return "Error: 'outputs' data is not a list."
+
+ for i, agent_output in enumerate(parsed_data["outputs"], start=3):
+ if not isinstance(agent_output, dict):
+ return f"Error: Agent output at index {i} is not a dictionary"
+
+ if "agent_name" not in agent_output:
+ return f"Error: 'agent_name' key is missing at index {i}"
+
+ if "steps" not in agent_output:
+ return f"Error: 'steps' key is missing at index {i}"
+
+ if agent_output["steps"] is None:
+ return f"Error: 'steps' data is None at index {i}"
+
+ if not isinstance(agent_output["steps"], list):
+ return f"Error: 'steps' data is not a list at index {i}"
+
+ agent_name = agent_output["agent_name"]
+ output += f"Run {(3-i)} (Agent: `{agent_name}`)\n\n"
+ # output += "<details>\n<summary>Show/Hide Agent Steps</summary>\n\n"
+
+ # Iterate over steps
+ for j, step in enumerate(agent_output["steps"], start=3):
+ if not isinstance(step, dict):
+ return f"Error: step at index {j} is not a dictionary at {i} agent output."
+
+ if step is None:
+ return f"Error: step at index {j} is None at {i} agent output"
+
+ if "role" not in step:
+ return f"Error: 'role' key missing at step {j} at {i} agent output."
+
+ if "content" not in step:
+ return f"Error: 'content' key missing at step {j} at {i} agent output."
+
+ if step["role"].strip() != "System:": # Filter out system prompts
+ # role = step["role"]
+ content = step["content"]
+ output += f"Step {(3-j)}:\n"
+ output += f"Response : {content}\n\n"
+
+ # output += "</details>\n\n---\n"
+
+ output += f"Overall Completion Time: `{overall_time}`"
+ return output
+
+ except json.JSONDecodeError as e :
+ return f"Error during parsing json.JSONDecodeError : {e}"
+
+ except Exception as e:
+ return f"Error during parsing: {str(e)}"
+
+
+def parse_spreadsheet_swarm_output(file_path: str) -> str:
+ """Parses the SpreadSheetSwarm output CSV file and formats it for display."""
+ if not file_path:
+ return "Error: No file path provided for parsing."
+
+ print(f"Parsing spreadsheet output from: {file_path}")
+
+ try:
+ with open(file_path, 'r', encoding='utf-8') as file:
+ csv_reader = csv.reader(file)
+ header = next(csv_reader, None) # Read the header row
+ if not header:
+ return "Error: CSV file is empty or has no header"
+
+ output = "### Spreadsheet Swarm Output ###\n\n"
+ output += "| " + " | ".join(header) + " |\n" # Adding header
+ output += "| " + " | ".join(["---"] * len(header)) + " |\n" # Adding header seperator
+
+ for row in csv_reader:
+ output += "| " + " | ".join(row) + " |\n" # Adding row
+
+ output += "\n"
+ return output
+
+ except FileNotFoundError:
+ return "Error: CSV file not found."
+ except Exception as e:
+ return f"Error during parsing CSV file: {str(e)}"
+def parse_json_output(data:str) -> str:
+ """Parses a JSON string and formats it for display."""
+ if not data:
+ return "Error: No data provided for parsing."
+
+ print(f"Parsing json output from: {data}")
+ try:
+ parsed_data = json.loads(data)
+
+ output = "### Swarm Metadata ###\n\n"
+
+ for key,value in parsed_data.items():
+ if key == "outputs":
+ output += f"**{key}**:\n"
+ if isinstance(value, list):
+ for item in value:
+ output += f" - Agent Name : {item.get('agent_name', 'N/A')}\n"
+ output += f" Task : {item.get('task', 'N/A')}\n"
+ output += f" Result : {item.get('result', 'N/A')}\n"
+ output += f" Timestamp : {item.get('timestamp', 'N/A')}\n\n"
+
+ else :
+ output += f" {value}\n"
+
+ else :
+ output += f"**{key}**: {value}\n"
+
+ return output
+
+ except json.JSONDecodeError as e:
+ return f"Error: Invalid JSON format - {e}"
+
+ except Exception as e:
+ return f"Error during JSON parsing: {str(e)}"
+
class UI:
def __init__(self, theme):
self.theme = theme
@@ -345,7 +581,7 @@ class UI:
label=label,
interactive=True,
)
- self.components[f'slider_{label}']
+ self.components[f'slider_{label}'] = slider
return slider
def create_dropdown(self, label, choices, value=None, multiselect=False):
@@ -414,9 +650,9 @@ class UI:
@staticmethod
def create_ui_theme(primary_color="red"):
- return gr.themes.Soft(
+ return gr.themes.Ocean(
primary_hue=primary_color,
- secondary_hue="gray",
+ secondary_hue=primary_color,
neutral_hue="gray",
).set(
body_background_fill="#20252c",
@@ -465,6 +701,25 @@ def create_app():
theme = UI.create_ui_theme(primary_color="red")
ui = UI(theme=theme)
+ # Available providers and models
+ providers = [
+ "openai",
+ "anthropic",
+ "cohere",
+ "gemini",
+ "mistral",
+ "groq",
+ "nvidia_nim",
+ "huggingface",
+ "perplexity"
+ ]
+
+ filtered_models = {}
+
+ for provider in providers:
+ filtered_models[provider] = models_by_provider.get(provider, [])
+
+
with ui.blocks:
with gr.Row():
with gr.Column(scale=4): # Left column (80% width)
@@ -494,7 +749,7 @@ def create_app():
with gr.Column(scale=1):
# Get available swarm types
swarm_types = [
- "SequentialWorkflow", "ConcurrentWorkflow", "AgentRearrange",
+ "SequentialWorkflow", "ConcurrentWorkflow","AgentRearrange",
"MixtureOfAgents", "SpreadSheetSwarm", "auto"
]
agent_selector = gr.Dropdown(
@@ -509,7 +764,7 @@ def create_app():
with gr.Column(visible=False) as flow_config:
flow_text = gr.Textbox(
label="Agent Flow Configuration",
- placeholder="Enter agent flow (e.g., Agent1 -> Agent2 -> Agent3)",
+ placeholder="Enter agent flow !",
lines=2
)
gr.Markdown(
@@ -522,7 +777,48 @@ def create_app():
)
with gr.Column(scale=2, min_width=200):
- with gr.Row():
+ with gr.Row():
+ # Provider selection dropdown
+ provider_dropdown = gr.Dropdown(
+ label="Select Provider",
+ choices=providers,
+ value=providers[0] if providers else None,
+ interactive=True,
+ )
+ # with gr.Row():
+ # # Model selection dropdown (initially empty)
+ model_dropdown = gr.Dropdown(
+ label="Select Model",
+ choices=[],
+ interactive=True,
+ )
+ with gr.Row():
+ # API key input
+ api_key_input = gr.Textbox(
+ label="API Key",
+ placeholder="Enter your API key",
+ type="password",
+ )
+ with gr.Row():
+ # temp slider
+ temperature_slider = gr.Slider(
+ label="Temperature",
+ minimum=0,
+ maximum=1,
+ value=0.1,
+ step=0.01
+ )
+ # with gr.Row():
+ # max tokens slider
+ max_tokens_slider = gr.Slider(
+ label="Max Tokens",
+ minimum=100,
+ maximum=10000,
+ value=4000,
+ step=100,
+ )
+
+ with gr.Row():
max_loops_slider = gr.Slider(
label="Max Loops",
minimum=1,
@@ -530,21 +826,25 @@ def create_app():
value=1,
step=1
)
-
- with gr.Row():
+ with gr.Row():
dynamic_slider = gr.Slider(
- label="Dynamic Temp",
+ label="Dyn. Temp",
minimum=0,
maximum=1,
value=0.1,
step=0.01
)
- with gr.Row():
+ with gr.Row():
loading_status = gr.Textbox(
label="Status",
value="Ready",
interactive=False
)
+ #Hidden textbox to store API Key
+ env_api_key_textbox = gr.Textbox(
+ value="",
+ visible=False
+ )
with gr.Row():
run_button = gr.Button("Run Task", variant="primary")
@@ -587,29 +887,71 @@ def create_app():
gr.update(maximum=max_loops), # For max_loops_slider
f"Selected {swarm_type}" # For loading_status
)
+ def update_model_dropdown(provider):
+ """Update model dropdown based on selected provider."""
+ models = filtered_models.get(provider, [])
+ return gr.update(choices=models, value=models[0] if models else None)
- async def run_task_wrapper(task, max_loops, dynamic_temp, swarm_type, agent_prompt_selector, flow_text):
+ async def run_task_wrapper(task, max_loops, dynamic_temp, swarm_type, agent_prompt_selector, flow_text,
+ provider, model_name, api_key, temperature, max_tokens):
"""Execute the task and update the UI with progress."""
try:
if not task:
- yield "Please provide a task description.", "Error: Missing task"
+ yield "Please provide a task description.", "Error: Missing task", ""
return
if not agent_prompt_selector or len(agent_prompt_selector) == 0:
- yield "Please select at least one agent.", "Error: No agents selected"
+ yield "Please select at least one agent.", "Error: No agents selected", ""
return
+
+ if not provider:
+ yield "Please select a provider.", "Error: No provider selected", ""
+ return
+
+ if not model_name:
+ yield "Please select a model.", "Error: No model selected", ""
+ return
+
+ if not api_key:
+ yield "Please enter an API Key", "Error: API Key is required", ""
+ return
+
# Update status
- yield "Processing...", "Running task..."
+ yield "Processing...", "Running task...", ""
# Prepare flow for AgentRearrange
flow = None
if swarm_type == "AgentRearrange":
if not flow_text:
- yield "Please provide the agent flow configuration.", "Error: Flow not configured"
+ yield "Please provide the agent flow configuration.", "Error: Flow not configured", ""
return
flow = flow_text
+ # Save API key to .env
+ env_path = find_dotenv()
+ if provider == "openai":
+ set_key(env_path, "OPENAI_API_KEY", api_key)
+ elif provider == "anthropic":
+ set_key(env_path, "ANTHROPIC_API_KEY", api_key)
+ elif provider == "cohere":
+ set_key(env_path, "COHERE_API_KEY", api_key)
+ elif provider == "gemini":
+ set_key(env_path, "GEMINI_API_KEY", api_key)
+ elif provider == "mistral":
+ set_key(env_path, "MISTRAL_API_KEY", api_key)
+ elif provider == "groq":
+ set_key(env_path, "GROQ_API_KEY", api_key)
+ elif provider == "nvidia_nim":
+ set_key(env_path, "NVIDIA_NIM_API_KEY", api_key)
+ elif provider == "huggingface":
+ set_key(env_path, "HUGGINGFACE_API_KEY", api_key)
+ elif provider == "perplexity":
+ set_key(env_path, "PERPLEXITY_API_KEY", api_key)
+ else:
+ yield f"Error: {provider} this provider is not present", f"Error: {provider} not supported", ""
+ return
+
# Execute task
result, router, error = await execute_task(
task=task,
@@ -617,22 +959,40 @@ def create_app():
dynamic_temp=dynamic_temp,
swarm_type=swarm_type,
agent_keys=agent_prompt_selector,
- flow=flow
+ flow=flow,
+ model_name=model_name,
+ provider=provider,
+ api_key=api_key,
+ temperature=temperature,
+ max_tokens = max_tokens,
)
if error:
- yield f"Error: {error}", "Error occurred"
+ yield f"Error: {error}", f"Error: {error}", ""
return
# Format output based on swarm type
output_lines = []
if swarm_type == "SpreadSheetSwarm":
- output_lines.append(f"### Spreadsheet Output ###\n{result}\n{'=' * 50}\n")
+ # Check if the result is a file path or JSON
+ if os.path.exists(result):
+ parsed_output = parse_spreadsheet_swarm_output(result)
+ output_lines.append(parsed_output)
+ else:
+ parsed_output = parse_json_output(result)
+ output_lines.append(parsed_output)
+
elif swarm_type == "AgentRearrange":
parsed_output = parse_agent_rearrange_output(result)
output_lines.append(parsed_output)
+ elif swarm_type == "MixtureOfAgents":
+ parsed_output = parse_mixture_of_agents_output(result)
+ output_lines.append(parsed_output)
+ elif swarm_type == "SequentialWorkflow":
+ parsed_output = parse_sequential_workflow_output(result)
+ output_lines.append(parsed_output)
elif isinstance(result, dict): # checking if result is dict or string.
-
+
if swarm_type == "MixtureOfAgents":
# Add individual agent outputs
for key, value in result.items():
@@ -647,10 +1007,10 @@ def create_app():
elif isinstance(result, str):
output_lines.append(str(result))
- yield "\n".join(output_lines), "Completed"
-
+ yield "\n".join(output_lines), "Completed", api_key
+
except Exception as e:
- yield f"Error: {str(e)}", "Error occurred"
+ yield f"Error: {str(e)}", f"Error: {str(e)}", ""
# Connect the update functions
agent_selector.change(
@@ -658,6 +1018,11 @@ def create_app():
inputs=[agent_selector],
outputs=[flow_config, max_loops_slider, loading_status]
)
+ provider_dropdown.change(
+ fn=update_model_dropdown,
+ inputs=[provider_dropdown],
+ outputs=[model_dropdown]
+ )
# Create event trigger
# Create event trigger for run button
@@ -669,19 +1034,24 @@ def create_app():
dynamic_slider,
agent_selector,
agent_prompt_selector,
- flow_text
+ flow_text,
+ provider_dropdown,
+ model_dropdown,
+ api_key_input,
+ temperature_slider,
+ max_tokens_slider
],
- outputs=[agent_output_display, loading_status]
+ outputs=[agent_output_display, loading_status, env_api_key_textbox]
)
# Connect cancel button to interrupt processing
def cancel_task():
- return "Task cancelled.", "Cancelled"
+ return "Task cancelled.", "Cancelled", ""
cancel_button.click(
fn=cancel_task,
inputs=None,
- outputs=[agent_output_display, loading_status],
+ outputs=[agent_output_display, loading_status, env_api_key_textbox],
cancels=run_event
)