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Updated LLM model references in curriculum_gateway.py, gateway.py, and guardrails.py to use 'models/gemini-2.5-pro' for improved output quality. Added a comprehensive evaluation framework, experimentation guide, batch and API test scripts, realistic test data, and supporting files to enable systematic testing and prompt improvement for SEAL project endpoints.

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Updated LLM model references in curriculum_gateway.py, gateway.py, and guardrails.py to use 'models/gemini-2.5-pro' for improved output quality. Added a comprehensive evaluation framework, experimentation guide, batch and API test scripts, realistic test data, and supporting files to enable systematic testing and prompt improvement for SEAL project endpoints.
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Pull request overview

This pull request updates the SEAL project to use the Gemini 2.5 Pro model and adds a comprehensive evaluation framework for testing and improving prompt outputs. The changes include model reference updates across three core files and the addition of extensive testing, evaluation, and data generation tools.

Key Changes:

  • Updated LLM model from 'gemini-1.5-flash-002' to 'models/gemini-2.5-pro' in gateway files and guardrails
  • Added comprehensive evaluation framework with automated and human evaluation capabilities
  • Introduced realistic test data generation based on educational research patterns
  • Created extensive documentation and experimentation guides

Reviewed changes

Copilot reviewed 17 out of 18 changed files in this pull request and generated 18 comments.

Show a summary per file
File Description
app/llm/gateway.py Updated Gemini model version to 2.5 Pro
app/llm/curriculum_gateway.py Updated Gemini model version to 2.5 Pro
app/safety/guardrails.py Updated Gemini model version to 2.5 Pro
test/evaluation_framework.py Added automated evaluation framework with quality scoring
test/realistic_data_generator.py Added realistic test data generation with educational patterns
test/human_evaluation_framework.py Added HTML-based human evaluation interface
test/run_comprehensive_evaluation.py Added comprehensive evaluation runner integrating all components
test/quick_start_evaluation.py Added quick start tools and test case generation
test/manual_prompt_runner.py Added manual prompt testing utility
test/batch_prompt_runner.py Added batch processing for test cases
test/api_test_script.py Added API endpoint testing script
test/quick_test_cases.json Added sample test cases for EMT and curriculum
test/results/batch_results.csv Added batch test results with sample responses
test/data/realistic_profiles.json Added realistic class profile data
test/data/curriculum_test_cases.json Added curriculum intervention test cases
test/EXPERIMENTATION_GUIDE.md Added comprehensive experimentation guide
SEAL_EVALUATION_FRAMEWORK_SUMMARY.md Added framework summary documentation

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print("\n" + "=" * 50)

# Load test cases
test_cases = create_simple_test_cases()
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The function create_simple_test_cases() is called but never defined in this file. This will cause a NameError at runtime when main() is executed.

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"num_students": 19,
"emt_scores": {
"EMT1": [
100,
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The line numbers in the realistic_profiles.json file appear incorrect. Line 1088 shows a value of 100 which should be written as 100.0 for consistency with the other float values in the EMT scores arrays throughout the file.

Suggested change
100,
100.0,

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67.1,
94.1,
67.6,
100,
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The line shows a value of 100 which should be written as 100.0 for consistency with the other float values in the EMT scores arrays throughout the file.

Suggested change
100,
100.0,

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def create_test_cases(self) -> List[TestCase]:
"""Create comprehensive test cases for both prompt types"""
test_cases = []
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Variable test_cases is not used.

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test_cases = []

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test_cases = evaluator.generate_realistic_test_data(args.num_classes)

# Step 2: Run automated evaluation
automated_results = evaluator.run_automated_evaluation(test_cases)
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Variable automated_results is not used.

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automated_results = evaluator.run_automated_evaluation(test_cases)
evaluator.run_automated_evaluation(test_cases)

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Simplified script to get Mayur started with prompt testing
"""

import os
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Import of 'os' is not used.

Suggested change
import os

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import random
import json
from typing import Dict, List, Any, Tuple
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Import of 'Tuple' is not used.

Suggested change
from typing import Dict, List, Any, Tuple
from typing import Dict, List, Any

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from typing import Dict, List, Any, Tuple
from dataclasses import dataclass
from pathlib import Path
import numpy as np
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Import of 'np' is not used.

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import numpy as np

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Integrates automated testing, realistic data generation, and human evaluation
"""

import os
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Import of 'os' is not used.

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import os

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parser.add_argument("--out", default=None, help="Output CSV path (optional)")
args = parser.parse_args()

cases = json.load(open(args.input, "r", encoding="utf-8"))
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File is opened but is not closed.

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cases = json.load(open(args.input, "r", encoding="utf-8"))
with open(args.input, "r", encoding="utf-8") as f:
cases = json.load(f)

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