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Integrate Gemini 2.5 Flash and LIME
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Linked challenge: Multi-Agent Ad Policy Auditor
Format
Code-aware
Lines
17
Sections
5
Linked challenge
Multi-Agent Ad Policy Auditor
Prompt source
Original prompt text with formatting preserved for inspection.
17 lines
5 sections
No variables
1 code block
Implement the `UserTopicAnalyzer` agent to call Gemini 2.5 Flash for extracting topics from simulated user data. Subsequently, integrate LIME to explain the `AdStrategist`'s targeting decisions and the `PrivacyAuditor`'s flagging decisions. This might involve creating a wrapper function or a custom tool for the agents to invoke LIME.
```python
# Example of how UserTopicAnalyzer might use Gemini
def analyze_topics(user_data: str) -> str:
response = autogen.Completion.create(context=user_data, llm_config=llm_config)
return response.get("choices")[0].get("message").get("content")
# Integrate this function into UserTopicAnalyzer's capabilities or tools
# Consider a custom tool for LIME:
# from autogen.agentchat.contrib.llm_utils import register_for_llm
# from lime.lime_text import LimeTextExplainer
# @register_for_llm(name="explain_decision", description="Generates a LIME explanation for a text-based decision.")
# def explain_decision(text_input: str, model_prediction_function, class_names: list) -> str:
# explainer = LimeTextExplainer(class_names=class_names)
# explanation = explainer.explain_instance(text_input, model_prediction_function, num_features=6)
# return explanation.as_plain_text()
# # ... then make this tool available to the relevant agents
```Adaptation plan
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Keep stable
Hold the task contract and output shape stable so generated implementations remain comparable.
Tune next
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Verify after
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.