Spaces:
Running
Running
File size: 3,969 Bytes
a23082c 114747f a23082c b8f6b7f a23082c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 |
import os
import logging
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.google_genai import GoogleGenAI
# Setup logging
logger = logging.getLogger(__name__)
# Helper function to load prompt from file
def load_prompt_from_file(filename="../prompts/image_analyzer_prompt.txt") -> str:
"""Loads the system prompt from a text file."""
try:
# Assuming the prompt file is in the same directory as the agent script
script_dir = os.path.dirname(__file__)
prompt_path = os.path.join(script_dir, filename)
with open(prompt_path, "r") as f:
prompt = f.read()
logger.info(f"Successfully loaded system prompt from {prompt_path}")
return prompt
except FileNotFoundError:
logger.error(f"Prompt file {filename} not found at {prompt_path}. Using fallback prompt.")
# Fallback minimal prompt
return "You are an image analyzer. Describe the image factually."
except Exception as e:
logger.error(f"Error loading prompt file {filename}: {e}", exc_info=True)
return "You are an image analyzer. Describe the image factually."
def initialize_image_analyzer_agent() -> FunctionAgent:
"""
Create an agent that orchestrates image analysis.
Uses Gemini Pro multimodal capabilities directly without explicit tools.
Configuration and prompt are loaded from environment/file.
"""
logger.info("Initializing ImageAnalyzerAgent...")
# Configuration from environment variables
llm_model_name = os.getenv("IMAGE_ANALYZER_LLM_MODEL", "gemini-2.5-pro-preview-03-25")
gemini_api_key = os.getenv("GEMINI_API_KEY")
if not gemini_api_key:
logger.error("GEMINI_API_KEY not found in environment variables.")
raise ValueError("GEMINI_API_KEY must be set")
try:
llm = GoogleGenAI(
api_key=gemini_api_key,
model=llm_model_name,
)
logger.info(f"Using LLM model: {llm_model_name}")
# Load system prompt from file
system_prompt = load_prompt_from_file()
# Note: This agent is a FunctionAgent but doesn't explicitly define tools.
# It relies on the LLM (Gemini 1.5 Pro) to understand the system prompt
# and perform the analysis when an image is passed in the ChatMessage blocks.
agent = FunctionAgent(
name="image_analyzer_agent",
description=(
"ImageAnalyzerAgent inspects image files using its multimodal capabilities, "
"interpreting the visual content according to a detailed factual analysis prompt."
),
llm=llm,
system_prompt=system_prompt,
# No explicit tools needed if relying on direct multimodal LLM call
# tools=[],
can_handoff_to=["planner_agent", "research_agent", "reasoning_agent", "figure_interpretation_agent"],
)
logger.info("ImageAnalyzerAgent initialized successfully.")
return agent
except Exception as e:
logger.error(f"Error during ImageAnalyzerAgent initialization: {e}", exc_info=True)
raise
# Example usage (for testing if run directly)
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger.info("Running image_analyzer_agent.py directly for testing...")
# Ensure API key is set for testing
if not os.getenv("GEMINI_API_KEY"):
print("Error: GEMINI_API_KEY environment variable not set. Cannot run test.")
else:
try:
test_agent = initialize_image_analyzer_agent()
print("Image Analyzer Agent initialized successfully for testing.")
# To test further, you would need to construct a ChatMessage with an ImageBlock
# and run agent.chat(message)
except Exception as e:
print(f"Error during testing: {e}")
|