⚡️ Speed up function chatbot_preprocess by 11%

[codeflash-ai]

📄 11% (0.11x) speedup for chatbot_preprocess in gradio/external_utils.py

⏱️ Runtime : 29.1 microseconds → 26.2 microseconds (best of 123 runs)

📝 Explanation and details

The optimized code achieves a 10% speedup through two key optimizations:

1. Condition Logic Reversal: Changed if not state: to if state:, eliminating the negation operation. This reduces computational overhead on every function call, as Python no longer needs to evaluate the boolean NOT operation.

2. Dictionary Access Caching: Extracted state["conversation"] into a local variable convo, reducing redundant dictionary lookups from 2 to 1. In the original code, state["conversation"] was accessed twice - once for "generated_responses" and once for "past_user_inputs". The optimization caches this intermediate dictionary reference.

Performance Impact Analysis:

• The line profiler shows the optimized version reduces total execution time from 84.1μs to 69.9μs
• Dictionary access caching is particularly effective since dictionary lookups in Python involve hash computations
• The condition reversal provides consistent micro-optimizations across all code paths

Test Case Performance:

• Best gains (13-21% faster): Cases with valid conversation states benefit most from the cached dictionary access
• Modest gains (3-10% faster): Simple cases with falsy states still benefit from the eliminated negation
• Edge cases: Error scenarios show 7-20% improvements, indicating the optimizations help even in exception paths

This optimization is especially valuable for chatbot preprocessing functions that are likely called frequently in conversational AI workflows, where even small per-call improvements compound significantly over many interactions.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 41 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

import pytest  # used for our unit tests
from gradio.external_utils import chatbot_preprocess

# unit tests

# ------------------------------
# 1. Basic Test Cases
# ------------------------------

def test_empty_state_returns_empty_lists():
    # Basic: Empty state (None)
    text = "Hello!"
    state = None
    codeflash_output = chatbot_preprocess(text, state); result = codeflash_output # 607ns -> 565ns (7.43% faster)

def test_empty_dict_state_returns_empty_lists():
    # Basic: Empty dict as state
    text = "How are you?"
    state = {}
    codeflash_output = chatbot_preprocess(text, state); result = codeflash_output # 590ns -> 546ns (8.06% faster)

def test_state_with_conversation_returns_expected_lists():
    # Basic: Normal state with conversation
    text = "What's up?"
    state = {
        "conversation": {
            "generated_responses": ["Hi!", "How can I help?"],
            "past_user_inputs": ["Hello", "I need help"]
        }
    }
    codeflash_output = chatbot_preprocess(text, state); result = codeflash_output # 693ns -> 609ns (13.8% faster)

def test_state_with_empty_lists():
    # Basic: State with conversation but empty lists
    text = "Test"
    state = {
        "conversation": {
            "generated_responses": [],
            "past_user_inputs": []
        }
    }
    codeflash_output = chatbot_preprocess(text, state); result = codeflash_output # 731ns -> 619ns (18.1% faster)

# ------------------------------
# 2. Edge Test Cases
# ------------------------------

def test_state_is_falsey_value():
    # Edge: state is a falsey value (other than None or {})
    text = "Edge"
    for falsey in [False, 0, "", [], ()]:
        codeflash_output = chatbot_preprocess(text, falsey); result = codeflash_output # 1.52μs -> 1.50μs (0.999% faster)

def test_state_missing_conversation_key():
    # Edge: state present but missing 'conversation' key
    text = "Missing key"
    state = {"other_key": 123}
    with pytest.raises(KeyError):
        chatbot_preprocess(text, state) # 1.21μs -> 999ns (20.7% faster)

def test_state_conversation_missing_generated_responses():
    # Edge: conversation missing 'generated_responses'
    text = "Missing generated_responses"
    state = {"conversation": {"past_user_inputs": ["hi"]}}
    with pytest.raises(KeyError):
        chatbot_preprocess(text, state) # 1.22μs -> 1.07μs (13.8% faster)

def test_state_conversation_missing_past_user_inputs():
    # Edge: conversation missing 'past_user_inputs'
    text = "Missing past_user_inputs"
    state = {"conversation": {"generated_responses": ["hello"]}}
    with pytest.raises(KeyError):
        chatbot_preprocess(text, state) # 1.28μs -> 1.10μs (16.6% faster)

def test_state_conversation_wrong_types():
    # Edge: conversation keys are not lists
    text = "Wrong types"
    state = {
        "conversation": {
            "generated_responses": "not a list",
            "past_user_inputs": 123
        }
    }
    codeflash_output = chatbot_preprocess(text, state); result = codeflash_output # 730ns -> 652ns (12.0% faster)

def test_text_is_empty_string():
    # Edge: empty text
    text = ""
    state = None
    codeflash_output = chatbot_preprocess(text, state); result = codeflash_output # 565ns -> 540ns (4.63% faster)

def test_text_is_none():
    # Edge: text is None
    text = None
    state = None
    codeflash_output = chatbot_preprocess(text, state); result = codeflash_output # 535ns -> 445ns (20.2% faster)

def test_state_extra_keys_ignored():
    # Edge: state has extra keys, should not affect output
    text = "Extra"
    state = {
        "conversation": {
            "generated_responses": ["A"],
            "past_user_inputs": ["B"]
        },
        "extra": 42
    }
    codeflash_output = chatbot_preprocess(text, state); result = codeflash_output # 713ns -> 612ns (16.5% faster)

def test_state_conversation_extra_keys_ignored():
    # Edge: conversation has extra keys
    text = "Extra conv"
    state = {
        "conversation": {
            "generated_responses": ["A"],
            "past_user_inputs": ["B"],
            "foo": "bar"
        }
    }
    codeflash_output = chatbot_preprocess(text, state); result = codeflash_output # 718ns -> 625ns (14.9% faster)

def test_state_conversation_lists_are_empty_strings():
    # Edge: generated_responses and past_user_inputs are lists of empty strings
    text = "Edge"
    state = {
        "conversation": {
            "generated_responses": [""],
            "past_user_inputs": [""]
        }
    }
    codeflash_output = chatbot_preprocess(text, state); result = codeflash_output # 687ns -> 597ns (15.1% faster)

# ------------------------------
# 3. Large Scale Test Cases
# ------------------------------

def test_large_lists_in_state():
    # Large Scale: generated_responses and past_user_inputs with 1000 elements
    text = "Large test"
    gen_responses = [f"response_{i}" for i in range(1000)]
    user_inputs = [f"user_{i}" for i in range(1000)]
    state = {
        "conversation": {
            "generated_responses": gen_responses,
            "past_user_inputs": user_inputs
        }
    }
    codeflash_output = chatbot_preprocess(text, state); result = codeflash_output # 667ns -> 632ns (5.54% faster)

def test_large_text_input():
    # Large Scale: very large text input
    text = "x" * 10000
    state = None
    codeflash_output = chatbot_preprocess(text, state); result = codeflash_output # 562ns -> 525ns (7.05% faster)

def test_large_state_dict_with_irrelevant_keys():
    # Large Scale: state dict with many irrelevant keys
    text = "Irrelevant keys"
    state = {f"key_{i}": i for i in range(500)}
    state["conversation"] = {
        "generated_responses": ["foo"],
        "past_user_inputs": ["bar"]
    }
    codeflash_output = chatbot_preprocess(text, state); result = codeflash_output # 734ns -> 657ns (11.7% faster)

def test_large_state_with_nested_irrelevant_structures():
    # Large Scale: state dict with deeply nested irrelevant structures
    text = "Deep nest"
    state = {
        "conversation": {
            "generated_responses": ["yes"],
            "past_user_inputs": ["no"]
        },
        "nested": [{"a": [j for j in range(100)]} for i in range(10)]
    }
    codeflash_output = chatbot_preprocess(text, state); result = codeflash_output # 683ns -> 634ns (7.73% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

from __future__ import annotations

# imports
import pytest  # used for our unit tests
from gradio.external_utils import chatbot_preprocess

# unit tests

# -------------------------
# Basic Test Cases
# -------------------------

def test_none_state_returns_empty_lists():
    # Test with state as None
    text = "Hello"
    state = None
    codeflash_output = chatbot_preprocess(text, state); result = codeflash_output # 574ns -> 521ns (10.2% faster)

def test_empty_dict_state_returns_empty_lists():
    # Test with state as empty dict
    text = "Hi"
    state = {}
    codeflash_output = chatbot_preprocess(text, state); result = codeflash_output # 555ns -> 581ns (4.48% slower)

def test_basic_state_with_conversation():
    # Test with a normal state containing conversation
    text = "How are you?"
    state = {
        "conversation": {
            "generated_responses": ["I'm fine, thank you."],
            "past_user_inputs": ["How are you?"]
        }
    }
    codeflash_output = chatbot_preprocess(text, state); result = codeflash_output # 655ns -> 570ns (14.9% faster)

def test_state_with_multiple_responses_and_inputs():
    # Test with multiple responses and inputs
    text = "What's the weather?"
    state = {
        "conversation": {
            "generated_responses": ["It's sunny.", "It's raining."],
            "past_user_inputs": ["What's the weather?", "Will it rain today?"]
        }
    }
    codeflash_output = chatbot_preprocess(text, state); result = codeflash_output # 679ns -> 606ns (12.0% faster)

# -------------------------
# Edge Test Cases
# -------------------------

def test_state_with_empty_conversation_lists():
    # State with empty lists in conversation
    text = "Test"
    state = {
        "conversation": {
            "generated_responses": [],
            "past_user_inputs": []
        }
    }
    codeflash_output = chatbot_preprocess(text, state); result = codeflash_output # 713ns -> 616ns (15.7% faster)

def test_state_with_missing_conversation_key():
    # State missing 'conversation' key
    text = "Edge"
    state = {
        "not_conversation": {
            "generated_responses": ["Hi"],
            "past_user_inputs": ["Hello"]
        }
    }
    # Should raise KeyError
    with pytest.raises(KeyError):
        chatbot_preprocess(text, state) # 1.22μs -> 1.02μs (19.4% faster)

def test_state_with_missing_generated_responses_key():
    # State missing 'generated_responses' key
    text = "Missing"
    state = {
        "conversation": {
            "past_user_inputs": ["Hi"]
        }
    }
    with pytest.raises(KeyError):
        chatbot_preprocess(text, state) # 1.17μs -> 1.09μs (7.62% faster)

def test_state_with_missing_past_user_inputs_key():
    # State missing 'past_user_inputs' key
    text = "Missing"
    state = {
        "conversation": {
            "generated_responses": ["Hello"]
        }
    }
    with pytest.raises(KeyError):
        chatbot_preprocess(text, state) # 1.25μs -> 1.11μs (13.1% faster)

def test_state_with_non_list_generated_responses():
    # generated_responses is not a list
    text = "Non-list"
    state = {
        "conversation": {
            "generated_responses": "not a list",
            "past_user_inputs": ["Hi"]
        }
    }
    # Should still return the value as is (no type enforcement)
    codeflash_output = chatbot_preprocess(text, state); result = codeflash_output # 729ns -> 636ns (14.6% faster)

def test_state_with_non_list_past_user_inputs():
    # past_user_inputs is not a list
    text = "Non-list"
    state = {
        "conversation": {
            "generated_responses": ["Hi"],
            "past_user_inputs": "not a list"
        }
    }
    codeflash_output = chatbot_preprocess(text, state); result = codeflash_output # 679ns -> 563ns (20.6% faster)

def test_state_is_falsey_but_not_none_or_empty():
    # State is a falsey value but not None or empty dict
    text = "Falsey"
    state = 0
    codeflash_output = chatbot_preprocess(text, state); result = codeflash_output # 524ns -> 513ns (2.14% faster)

def test_text_is_none():
    # Text is None
    text = None
    state = None
    codeflash_output = chatbot_preprocess(text, state); result = codeflash_output # 553ns -> 510ns (8.43% faster)

def test_text_is_empty_string():
    # Text is empty string
    text = ""
    state = None
    codeflash_output = chatbot_preprocess(text, state); result = codeflash_output # 563ns -> 545ns (3.30% faster)

def test_state_with_extra_keys_in_conversation():
    # State has extra keys in conversation
    text = "Extra"
    state = {
        "conversation": {
            "generated_responses": ["Hi"],
            "past_user_inputs": ["Hello"],
            "extra": "value"
        }
    }
    codeflash_output = chatbot_preprocess(text, state); result = codeflash_output # 704ns -> 626ns (12.5% faster)

def test_state_with_non_dict_conversation():
    # conversation is not a dict
    text = "Non-dict"
    state = {
        "conversation": "not a dict"
    }
    with pytest.raises(TypeError):
        # Will fail when trying to subscript a string with ["generated_responses"]
        chatbot_preprocess(text, state) # 1.21μs -> 1.20μs (0.666% faster)

# -------------------------
# Large Scale Test Cases
# -------------------------

def test_large_number_of_conversation_items():
    # Test with large lists (1000 items)
    text = "Bulk"
    responses = [f"Response {i}" for i in range(1000)]
    inputs = [f"Input {i}" for i in range(1000)]
    state = {
        "conversation": {
            "generated_responses": responses,
            "past_user_inputs": inputs
        }
    }
    codeflash_output = chatbot_preprocess(text, state); result = codeflash_output # 709ns -> 685ns (3.50% faster)

def test_large_scale_empty_strings():
    # Test with large lists of empty strings
    text = ""
    responses = [""] * 1000
    inputs = [""] * 1000
    state = {
        "conversation": {
            "generated_responses": responses,
            "past_user_inputs": inputs
        }
    }
    codeflash_output = chatbot_preprocess(text, state); result = codeflash_output # 727ns -> 652ns (11.5% faster)

def test_large_scale_none_values():
    # Test with large lists of None values
    text = None
    responses = [None] * 1000
    inputs = [None] * 1000
    state = {
        "conversation": {
            "generated_responses": responses,
            "past_user_inputs": inputs
        }
    }
    codeflash_output = chatbot_preprocess(text, state); result = codeflash_output # 711ns -> 628ns (13.2% faster)

def test_performance_large_scale():
    # Test performance with large lists; should not take excessive time
    import time
    text = "Performance"
    responses = [f"Response {i}" for i in range(1000)]
    inputs = [f"Input {i}" for i in range(1000)]
    state = {
        "conversation": {
            "generated_responses": responses,
            "past_user_inputs": inputs
        }
    }
    start = time.time()
    codeflash_output = chatbot_preprocess(text, state); result = codeflash_output # 740ns -> 636ns (16.4% faster)
    end = time.time()
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

To edit these changes git checkout codeflash/optimize-chatbot_preprocess-mhwsgz0x and push.