swarm at 2024-02-10 21:44:58

example.py

@@ -1,32 +1,58 @@
 import os
-
 from dotenv import load_dotenv
-
-# Import the OpenAIChat model and the Agent struct
-from swarms.models import OpenAIChat
+from swarms.models import Gemini
 from swarms.structs import Agent
 
-# Load the environment variables
+# Load environment variables
 load_dotenv()
 
 # Get the API key from the environment
-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=1000,
+api_key = os.environ.get("GOOGLE_API_KEY")
+if not api_key:
+    raise ValueError("Gemini API key not found. Please set it in your .env file")
+
+# Initialize the first agent (Gemini Pro Vision) for image analysis
+agent_analysis = Agent(
+    llm=Gemini(
+        temperature=0.8,
+        model_name="gemini-pro-vision",
+        gemini_api_key=api_key
+    ),
+    max_loops=1,
+    autosave=True,
+    dashboard=True,
 )
 
-## Initialize the workflow
-agent = Agent(
-    llm=llm,
+# Initialize the second agent (Gemini Pro) for text-based tasks
+agent_instruction = Agent(
+    llm=Gemini(
+        temperature=0.8,
+        model_name="gemini-pro",
+        gemini_api_key=api_key
+    ),
     max_loops=1,
     autosave=True,
     dashboard=True,
 )
 
-# Run the workflow on a task
-agent.run("Generate a 10,000 word blog on health and wellness.")
+# Task for the first agent: Image Analysis
+task_analysis = """
+Analyze this logo and summarize its content, then give a structured list of suggested improvements.
+"""
+img = 'swarmslogobanner.png'  # Replace with your image file name
+
+# Run the first agent on the task
+analysis_results = agent_analysis.run(task=task_analysis, img=img)
+
+# Task for the second agent: Instructions for a better logo
+task_instruction = f"""
+Based on the analysis and suggestions for the logo:
+{analysis_results}
+
+Generate detailed instructions for creating an improved version of this logo.
+"""
+
+# Run the second agent on the task
+instruction_results = agent_instruction.run(task=task_instruction)
+
+.