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chore: import upstream snapshot with attribution
2026-07-13 13:17:32 +08:00

96 lines
3.4 KiB
Python

import pytest
from pydantic import BaseModel
import gradio as gr
from gradio.state_holder import SessionState
class TestModel(BaseModel):
name: str
class TestState:
def test_as_component(self):
state = gr.State(value=5)
assert state.preprocess(10) == 10
assert state.preprocess("abc") == "abc"
assert state.stateful
def test_initial_value_deepcopy(self):
with pytest.raises(TypeError):
gr.State(value=gr) # modules are not deepcopyable
def test_initial_value_pydantic(self):
state = gr.State(value=TestModel(name="Freddy"))
assert isinstance(state.value, TestModel)
@pytest.mark.asyncio
async def test_callable_default_is_called_before_load_event(self):
with gr.Blocks() as demo:
state = gr.State(value=lambda: {"count": 42})
num = gr.Number()
btn = gr.Button()
btn.click(lambda s: s["count"], state, num)
session = SessionState(demo)
result = await demo.process_api(0, [None], state=session)
assert result["data"][0] == 42
@pytest.mark.asyncio
async def test_in_interface(self):
def test(x, y=" def"):
return (x + y, x + y)
io = gr.Interface(test, ["text", "state"], ["text", "state"])
result = await io.call_function(0, ["abc"])
assert result["prediction"][0] == "abc def"
result = await io.call_function(0, ["abc", result["prediction"][0]])
assert result["prediction"][0] == "abcabc def"
@pytest.mark.asyncio
async def test_in_blocks(self):
with gr.Blocks() as demo:
score = gr.State()
btn = gr.Button()
btn.click(lambda x: x + 1, score, score)
result = await demo.call_function(0, [0])
assert result["prediction"] == 1
result = await demo.call_function(0, [result["prediction"]])
assert result["prediction"] == 2
@pytest.mark.asyncio
async def test_update_with_gr_update(self):
"""Test that gr.update(value=...) works with gr.State components."""
with gr.Blocks() as demo:
state = gr.State(value=False)
btn = gr.Button()
# First function sets state to True using gr.update
btn.click(lambda: gr.update(value=True), None, state)
# Second function returns the current state value
btn2 = gr.Button()
btn2.click(lambda x: x, state, state)
# Call first function to set state to True
result = await demo.call_function(0, [])
# The prediction is the raw return value (gr.update dict)
assert result["prediction"] == {"__type__": "update", "value": True}
# Call second function to verify state was actually updated
result = await demo.call_function(1, [True]) # Pass True as current state
assert result["prediction"] is True
@pytest.mark.asyncio
async def test_update_with_gr_update_complex_value(self):
"""Test that gr.update(value=...) works with complex values."""
with gr.Blocks() as demo:
state = gr.State(value={"count": 0})
btn = gr.Button()
btn.click(
lambda x: gr.update(value={"count": x["count"] + 1}), state, state
)
result = await demo.call_function(0, [{"count": 5}])
# The prediction is the raw return value (gr.update dict)
assert result["prediction"] == {"__type__": "update", "value": {"count": 6}}