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e2fc5fd 15ef1b8 e2fc5fd 15ef1b8 e2fc5fd 15ef1b8 | 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 | """Tests for src/llm_interface.py — unit-testable functions only (no API calls)."""
import sys, os
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'src'))
import base64
import pytest
from unittest.mock import patch, MagicMock
from PIL import Image
from llm_interface import encode_image_to_base64, construct_initial_prompt
def _make_test_image(mode="RGB"):
return Image.new(mode, (10, 10), color="white")
def test_encode_returns_valid_base64():
img = _make_test_image()
result = encode_image_to_base64(img)
# Should decode without error
decoded = base64.b64decode(result)
assert len(decoded) > 0
def test_encode_produces_png_not_jpeg():
img = _make_test_image()
result = encode_image_to_base64(img)
raw = base64.b64decode(result)
# PNG magic bytes: \x89PNG
assert raw[:4] == b'\x89PNG', f"Expected PNG header, got {raw[:4]!r}"
def test_encode_preserves_rgba_mode():
"""RGBA images (transparency) should NOT be force-converted to RGB."""
img = _make_test_image(mode="RGBA")
result = encode_image_to_base64(img)
raw = base64.b64decode(result)
assert raw[:4] == b'\x89PNG'
# --- Claude extended thinking: request payload ---
def test_claude_model_gets_thinking_param():
"""anthropic/* models should have thinking enabled and temperature=1."""
import requests
captured = {}
def fake_post(url, headers, json, timeout):
captured["json"] = json
raise RuntimeError("stop after capture")
with patch("llm_interface.requests.post", side_effect=fake_post):
try:
from llm_interface import get_openrouter_prediction
get_openrouter_prediction(
model_identifier="anthropic/claude-sonnet-4.6",
api_key="test",
image=_make_test_image(),
exam_name="NEET", exam_year="2025",
question_type="MCQ_SINGLE_CORRECT",
max_tokens=10000,
)
except RuntimeError:
pass
assert captured["json"]["thinking"] == {"type": "enabled", "budget_tokens": 9000}
assert captured["json"]["temperature"] == 1
def test_non_claude_model_no_thinking_param():
"""Non-anthropic models should NOT have a thinking param."""
captured = {}
def fake_post(url, headers, json, timeout):
captured["json"] = json
raise RuntimeError("stop after capture")
with patch("llm_interface.requests.post", side_effect=fake_post):
try:
from llm_interface import get_openrouter_prediction
get_openrouter_prediction(
model_identifier="openai/gpt-5.5",
api_key="test",
image=_make_test_image(),
exam_name="NEET", exam_year="2025",
question_type="MCQ_SINGLE_CORRECT",
max_tokens=10000,
temperature=0.0,
)
except RuntimeError:
pass
assert "thinking" not in captured["json"]
assert captured["json"]["temperature"] == 0.0
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