[TESTS]

playground/demos/plant_biologist_swarm/using_concurrent_workflow.py

@@ -0,0 +1,111 @@
+import os
+
+from dotenv import load_dotenv
+from playground.demos.plant_biologist_swarm.prompts import (
+    diagnoser_agent,
+    disease_detector_agent,
+    growth_predictor_agent,
+    harvester_agent,
+    treatment_recommender_agent,
+)
+
+from swarms import Agent, GPT4VisionAPI, ConcurrentWorkflow
+
+
+# Load the OpenAI API key from the .env file
+load_dotenv()
+
+# Initialize the OpenAI API key
+api_key = os.environ.get("OPENAI_API_KEY")
+
+
+# llm = llm,
+llm = GPT4VisionAPI(
+    max_tokens=4000,
+)
+
+# Initialize Diagnoser Agent
+diagnoser_agent = Agent(
+    agent_name="Diagnoser Agent",
+    system_prompt=diagnoser_agent(),
+    llm=llm,
+    max_loops=1,
+    dashboard=False,
+    streaming_on=True,
+    verbose=True,
+    # saved_state_path="diagnoser.json",
+    multi_modal=True,
+    autosave=True,
+)
+
+# Initialize Harvester Agent
+harvester_agent = Agent(
+    agent_name="Harvester Agent",
+    system_prompt=harvester_agent(),
+    llm=llm,
+    max_loops=1,
+    dashboard=False,
+    streaming_on=True,
+    verbose=True,
+    # saved_state_path="harvester.json",
+    multi_modal=True,
+    autosave=True,
+)
+
+# Initialize Growth Predictor Agent
+growth_predictor_agent = Agent(
+    agent_name="Growth Predictor Agent",
+    system_prompt=growth_predictor_agent(),
+    llm=llm,
+    max_loops=1,
+    dashboard=False,
+    streaming_on=True,
+    verbose=True,
+    # saved_state_path="growth_predictor.json",
+    multi_modal=True,
+    autosave=True,
+)
+
+# Initialize Treatment Recommender Agent
+treatment_recommender_agent = Agent(
+    agent_name="Treatment Recommender Agent",
+    system_prompt=treatment_recommender_agent(),
+    llm=llm,
+    max_loops=1,
+    dashboard=False,
+    streaming_on=True,
+    verbose=True,
+    # saved_state_path="treatment_recommender.json",
+    multi_modal=True,
+    autosave=True,
+)
+
+# Initialize Disease Detector Agent
+disease_detector_agent = Agent(
+    agent_name="Disease Detector Agent",
+    system_prompt=disease_detector_agent(),
+    llm=llm,
+    max_loops=1,
+    dashboard=False,
+    streaming_on=True,
+    verbose=True,
+    # saved_state_path="disease_detector.json",
+    multi_modal=True,
+    autosave=True,
+)
+agents = [
+    diagnoser_agent,
+    disease_detector_agent,
+    treatment_recommender_agent,
+    growth_predictor_agent,
+    harvester_agent,
+]
+
+
+# Create the Concurrent workflow
+workflow = ConcurrentWorkflow(
+    agents=agents,
+    max_loops=1,
+)
+
+workflow.run("Diagnose the plant disease.")

swarms/structs/concurrent_workflow.py

@@ -89,35 +89,70 @@ class ConcurrentWorkflow(BaseWorkflow):
         """
         loop = 0
         while loop < self.max_loops:
-            with concurrent.futures.ThreadPoolExecutor(
-                max_workers=self.max_workers
-            ) as executor:
-                futures = {
-                    executor.submit(task.execute): task
-                    for task in self.task_pool
-                }
-                results = []
-
-                for future in concurrent.futures.as_completed(futures):
-                    task = futures[future]
-                    try:
-                        result = future.result()
-                        if self.print_results:
-                            logger.info(f"Task {task}: {result}")
-                        if self.return_results:
-                            results.append(result)
-                    except Exception as e:
-                        logger.error(
-                            f"Task {task} generated an exception: {e}"
-                        )
-
-            loop += 1
-            if self.stopping_condition and self.stopping_condition(
-                results
-            ):
+
+            if self.tasks is not None:
+                with concurrent.futures.ThreadPoolExecutor(
+                    max_workers=self.max_workers
+                ) as executor:
+                    futures = {
+                        executor.submit(task.execute): task
+                        for task in self.task_pool
+                    }
+                    results = []
+
+                    for future in concurrent.futures.as_completed(futures):
+                        task = futures[future]
+                        try:
+                            result = future.result()
+                            if self.print_results:
+                                logger.info(f"Task {task}: {result}")
+                            if self.return_results:
+                                results.append(result)
+                        except Exception as e:
+                            logger.error(
+                                f"Task {task} generated an exception: {e}"
+                            )
+
+                loop += 1
+                if self.stopping_condition and self.stopping_condition(
+                    results
+                ):
+                    break
+
+            elif self.agents is not None:
+                with concurrent.futures.ThreadPoolExecutor(
+                    max_workers=self.max_workers
+                ) as executor:
+                    futures = {
+                        executor.submit(agent.run): agent
+                        for agent in self.agents
+                    }
+                    results = []
+
+                    for future in concurrent.futures.as_completed(futures):
+                        agent = futures[future]
+                        try:
+                            result = future.result()
+                            if self.print_results:
+                                logger.info(f"Agent {agent}: {result}")
+                            if self.return_results:
+                                results.append(result)
+                        except Exception as e:
+                            logger.error(
+                                f"Agent {agent} generated an exception: {e}"
+                            )
+
+                loop += 1
+                if self.stopping_condition and self.stopping_condition(
+                    results
+                ):
+                    break
+
+            else:
+                logger.warning("No tasks or agents found in the workflow.")
                 break
 
-        return results if self.return_results else None
+            return results if self.return_results else None
 
     def list_tasks(self):
         """Prints a list of the tasks in the workflow."""