⚡️ Speed up function chatbot_postprocess by 11%

[codeflash-ai]

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

⏱️ Runtime : 181 microseconds → 163 microseconds (best of 143 runs)

📝 Explanation and details

The optimized code achieves an 11% speedup through two key changes:

1. Eliminates redundant dictionary lookups: The original code performs multiple nested dictionary accesses (response["conversation"]["past_user_inputs"] and response["conversation"]["generated_responses"]) within the zip function call. The optimized version extracts these values into variables (past_inputs and gen_responses) first, reducing the overhead of repeated dictionary key lookups.

2. Removes strict=False parameter: The original code uses zip(..., strict=False), which adds unnecessary overhead for parameter validation and processing. Since the function's behavior doesn't require strict length checking (it naturally handles mismatched lengths by truncating to the shorter list), removing this parameter eliminates the extra processing cost.

Performance characteristics: The line profiler shows that the optimized version reduces the time spent in the zip operation itself (78.1% vs 70.4% of total time), while the dictionary lookups are now isolated and more efficient. The optimization is most effective for small to medium-sized conversations (17-40% speedup in basic test cases) and maintains consistent benefits across various edge cases including mismatched list lengths, empty conversations, and non-string values.

Workload impact: This function appears to process chatbot conversation histories, likely called frequently in conversational AI applications. The 11% improvement compounds when processing multiple conversations or in real-time chat scenarios where response latency matters.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 35 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

from __future__ import annotations

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

# unit tests

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

def test_basic_single_pair():
    # Test with a single user input and response
    response = {
        "conversation": {
            "past_user_inputs": ["Hello"],
            "generated_responses": ["Hi there!"]
        }
    }
    history, returned_response = chatbot_postprocess(response) # 2.28μs -> 1.72μs (32.9% faster)

def test_basic_multiple_pairs():
    # Test with multiple user inputs and responses
    response = {
        "conversation": {
            "past_user_inputs": ["Hi", "How are you?", "Tell me a joke."],
            "generated_responses": ["Hello!", "I'm fine, thank you.", "Why did the chicken cross the road?"]
        }
    }
    history, returned_response = chatbot_postprocess(response) # 2.23μs -> 1.72μs (29.5% faster)

def test_basic_empty_lists():
    # Test with empty lists for both inputs and responses
    response = {
        "conversation": {
            "past_user_inputs": [],
            "generated_responses": []
        }
    }
    history, returned_response = chatbot_postprocess(response) # 2.06μs -> 1.76μs (17.1% faster)

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

def test_edge_user_inputs_longer():
    # Test where past_user_inputs is longer than generated_responses
    response = {
        "conversation": {
            "past_user_inputs": ["Hi", "How are you?", "Tell me a joke."],
            "generated_responses": ["Hello!", "I'm fine, thank you."]
        }
    }
    history, returned_response = chatbot_postprocess(response) # 2.26μs -> 1.69μs (33.8% faster)

def test_edge_generated_responses_longer():
    # Test where generated_responses is longer than past_user_inputs
    response = {
        "conversation": {
            "past_user_inputs": ["Hi", "How are you?"],
            "generated_responses": ["Hello!", "I'm fine, thank you.", "Extra response"]
        }
    }
    history, returned_response = chatbot_postprocess(response) # 2.23μs -> 1.76μs (27.0% faster)

def test_edge_none_values():
    # Test with None values in the lists
    response = {
        "conversation": {
            "past_user_inputs": ["Hi", None, "Tell me a joke."],
            "generated_responses": ["Hello!", "I'm fine, thank you.", None]
        }
    }
    history, returned_response = chatbot_postprocess(response) # 2.26μs -> 1.75μs (29.0% faster)

def test_edge_empty_strings():
    # Test with empty strings in the lists
    response = {
        "conversation": {
            "past_user_inputs": ["", "User2"],
            "generated_responses": ["", "Bot2"]
        }
    }
    history, returned_response = chatbot_postprocess(response) # 2.21μs -> 1.75μs (26.5% faster)

def test_edge_non_string_values():
    # Test with non-string values in the lists
    response = {
        "conversation": {
            "past_user_inputs": ["Hi", 42, {"text": "object"}],
            "generated_responses": ["Hello!", 3.14, ["list", "response"]]
        }
    }
    history, returned_response = chatbot_postprocess(response) # 2.34μs -> 1.75μs (33.9% faster)

def test_edge_missing_conversation_key():
    # Test with missing 'conversation' key
    response = {}
    with pytest.raises(KeyError):
        chatbot_postprocess(response) # 1.08μs -> 1.01μs (6.80% faster)

def test_edge_missing_past_user_inputs_key():
    # Test with missing 'past_user_inputs' key
    response = {
        "conversation": {
            "generated_responses": ["Hi"]
        }
    }
    with pytest.raises(KeyError):
        chatbot_postprocess(response) # 1.02μs -> 959ns (6.47% faster)

def test_edge_missing_generated_responses_key():
    # Test with missing 'generated_responses' key
    response = {
        "conversation": {
            "past_user_inputs": ["Hello"]
        }
    }
    with pytest.raises(KeyError):
        chatbot_postprocess(response) # 1.07μs -> 961ns (10.9% faster)

def test_edge_non_list_inputs():
    # Test with non-list types for inputs and responses
    response = {
        "conversation": {
            "past_user_inputs": "Not a list",
            "generated_responses": "Also not a list"
        }
    }
    # zip will iterate over strings character by character
    history, returned_response = chatbot_postprocess(response) # 2.83μs -> 2.26μs (25.0% faster)

def test_edge_lists_of_different_types():
    # Test with lists of different types
    response = {
        "conversation": {
            "past_user_inputs": [1, 2, 3],
            "generated_responses": ["a", "b", "c"]
        }
    }
    history, returned_response = chatbot_postprocess(response) # 2.25μs -> 1.67μs (34.8% faster)

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

def test_large_scale_maximum_pairs():
    # Test with maximum allowed pairs (1000)
    n = 1000
    response = {
        "conversation": {
            "past_user_inputs": [f"User{i}" for i in range(n)],
            "generated_responses": [f"Bot{i}" for i in range(n)]
        }
    }
    history, returned_response = chatbot_postprocess(response) # 20.0μs -> 18.2μs (9.97% faster)
    for i in range(n):
        pass

def test_large_scale_inputs_longer():
    # Test with 1000 user inputs and 999 responses
    n = 1000
    response = {
        "conversation": {
            "past_user_inputs": [f"User{i}" for i in range(n)],
            "generated_responses": [f"Bot{i}" for i in range(n-1)]
        }
    }
    history, returned_response = chatbot_postprocess(response) # 18.1μs -> 16.8μs (7.71% faster)
    for i in range(n-1):
        pass

def test_large_scale_responses_longer():
    # Test with 999 user inputs and 1000 responses
    n = 1000
    response = {
        "conversation": {
            "past_user_inputs": [f"User{i}" for i in range(n-1)],
            "generated_responses": [f"Bot{i}" for i in range(n)]
        }
    }
    history, returned_response = chatbot_postprocess(response) # 17.2μs -> 15.3μs (12.3% faster)
    for i in range(n-1):
        pass

def test_large_scale_empty_strings():
    # Test with 1000 empty strings in both lists
    n = 1000
    response = {
        "conversation": {
            "past_user_inputs": [""] * n,
            "generated_responses": [""] * n
        }
    }
    history, returned_response = chatbot_postprocess(response) # 15.8μs -> 15.3μs (3.65% faster)
    for pair in history:
        pass

def test_large_scale_mixed_types():
    # Test with 1000 mixed types in both lists
    n = 1000
    response = {
        "conversation": {
            "past_user_inputs": [i if i % 2 == 0 else str(i) for i in range(n)],
            "generated_responses": [str(i) if i % 2 == 0 else i for i in range(n)]
        }
    }
    history, returned_response = chatbot_postprocess(response) # 15.8μs -> 15.8μs (0.044% slower)
    for i in range(n):
        pass
# 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_postprocess

# unit tests

# 1. BASIC TEST CASES

def test_basic_typical_conversation():
    # Basic: 3 user inputs and 3 bot responses
    response = {
        "conversation": {
            "past_user_inputs": ["Hello", "How are you?", "Tell me a joke."],
            "generated_responses": ["Hi!", "I'm fine, thanks.", "Why did the chicken cross the road?"],
        }
    }
    expected_history = [
        ("Hello", "Hi!"),
        ("How are you?", "I'm fine, thanks."),
        ("Tell me a joke.", "Why did the chicken cross the road?"),
    ]
    chatbot_history, returned_response = chatbot_postprocess(response) # 2.33μs -> 1.77μs (31.6% faster)

def test_basic_empty_conversation():
    # Basic: No conversation yet
    response = {
        "conversation": {
            "past_user_inputs": [],
            "generated_responses": [],
        }
    }
    expected_history = []
    chatbot_history, returned_response = chatbot_postprocess(response) # 2.22μs -> 1.75μs (26.7% faster)

def test_basic_single_exchange():
    # Basic: Only one exchange
    response = {
        "conversation": {
            "past_user_inputs": ["Hi"],
            "generated_responses": ["Hello!"],
        }
    }
    expected_history = [("Hi", "Hello!")]
    chatbot_history, returned_response = chatbot_postprocess(response) # 2.20μs -> 1.75μs (26.0% faster)

# 2. EDGE TEST CASES

def test_edge_more_user_inputs_than_responses():
    # Edge: More user inputs than bot responses
    response = {
        "conversation": {
            "past_user_inputs": ["A", "B", "C"],
            "generated_responses": ["1", "2"],
        }
    }
    # zip(strict=False) truncates to shortest list: only first two pairs
    expected_history = [("A", "1"), ("B", "2")]
    chatbot_history, returned_response = chatbot_postprocess(response) # 2.25μs -> 1.66μs (35.6% faster)

def test_edge_more_responses_than_user_inputs():
    # Edge: More bot responses than user inputs
    response = {
        "conversation": {
            "past_user_inputs": ["A", "B"],
            "generated_responses": ["1", "2", "3"],
        }
    }
    # Only first two pairs included
    expected_history = [("A", "1"), ("B", "2")]
    chatbot_history, returned_response = chatbot_postprocess(response) # 2.22μs -> 1.63μs (36.2% faster)

def test_edge_non_string_inputs():
    # Edge: Inputs and responses are not strings
    response = {
        "conversation": {
            "past_user_inputs": [123, None, True],
            "generated_responses": [456, False, None],
        }
    }
    expected_history = [
        (123, 456),
        (None, False),
        (True, None),
    ]
    chatbot_history, returned_response = chatbot_postprocess(response) # 2.29μs -> 1.72μs (33.1% faster)

def test_edge_empty_strings_and_whitespace():
    # Edge: Empty strings and whitespace
    response = {
        "conversation": {
            "past_user_inputs": ["", "   ", "\n"],
            "generated_responses": ["", "\t", " "],
        }
    }
    expected_history = [
        ("", ""),
        ("   ", "\t"),
        ("\n", " "),
    ]
    chatbot_history, returned_response = chatbot_postprocess(response) # 2.33μs -> 1.70μs (36.6% faster)

def test_edge_missing_keys():
    # Edge: Missing keys should raise KeyError
    response = {
        "conversation": {
            "past_user_inputs": ["Hi"],
            # "generated_responses" missing
        }
    }
    with pytest.raises(KeyError):
        chatbot_postprocess(response) # 1.09μs -> 982ns (10.9% faster)

    response = {
        "conversation": {
            # "past_user_inputs" missing
            "generated_responses": ["Hello!"],
        }
    }
    with pytest.raises(KeyError):
        chatbot_postprocess(response) # 553ns -> 521ns (6.14% faster)

    response = {
        # "conversation" missing
    }
    with pytest.raises(KeyError):
        chatbot_postprocess(response) # 389ns -> 386ns (0.777% faster)

def test_edge_non_list_inputs():
    # Edge: Inputs are not lists
    response = {
        "conversation": {
            "past_user_inputs": "Hi",
            "generated_responses": "Hello!",
        }
    }
    # zip on strings will pair each character
    expected_history = list(zip("Hi", "Hello!", strict=False))
    chatbot_history, returned_response = chatbot_postprocess(response) # 1.14μs -> 1.14μs (0.088% faster)

def test_edge_lists_of_different_types():
    # Edge: Lists contain mixed types
    response = {
        "conversation": {
            "past_user_inputs": ["Hi", 42, None, {"a": 1}],
            "generated_responses": ["Hello!", 3.14, [], (1, 2)],
        }
    }
    expected_history = [
        ("Hi", "Hello!"),
        (42, 3.14),
        (None, []),
        ({"a": 1}, (1, 2)),
    ]
    chatbot_history, returned_response = chatbot_postprocess(response) # 2.20μs -> 1.71μs (28.5% faster)

# 3. LARGE SCALE TEST CASES

def test_large_scale_hundred_exchanges():
    # Large Scale: 100 exchanges
    user_inputs = [f"user_{i}" for i in range(100)]
    bot_responses = [f"bot_{i}" for i in range(100)]
    response = {
        "conversation": {
            "past_user_inputs": user_inputs,
            "generated_responses": bot_responses,
        }
    }
    expected_history = list(zip(user_inputs, bot_responses, strict=False))
    chatbot_history, returned_response = chatbot_postprocess(response) # 2.97μs -> 3.02μs (1.59% slower)

def test_large_scale_maximum_allowed_exchanges():
    # Large Scale: 999 exchanges (just under 1000)
    user_inputs = [f"u{i}" for i in range(999)]
    bot_responses = [f"b{i}" for i in range(999)]
    response = {
        "conversation": {
            "past_user_inputs": user_inputs,
            "generated_responses": bot_responses,
        }
    }
    expected_history = list(zip(user_inputs, bot_responses, strict=False))
    chatbot_history, returned_response = chatbot_postprocess(response) # 19.4μs -> 17.9μs (8.41% faster)

def test_large_scale_unequal_lists():
    # Large Scale: 999 user inputs, 900 bot responses
    user_inputs = [f"u{i}" for i in range(999)]
    bot_responses = [f"b{i}" for i in range(900)]
    response = {
        "conversation": {
            "past_user_inputs": user_inputs,
            "generated_responses": bot_responses,
        }
    }
    expected_history = list(zip(user_inputs, bot_responses, strict=False))
    chatbot_history, returned_response = chatbot_postprocess(response) # 13.2μs -> 13.5μs (2.52% slower)

def test_large_scale_empty_lists():
    # Large Scale: Both lists empty, but large object
    response = {
        "conversation": {
            "past_user_inputs": [],
            "generated_responses": [],
        }
    }
    expected_history = []
    chatbot_history, returned_response = chatbot_postprocess(response) # 2.25μs -> 1.61μs (39.8% faster)

def test_large_scale_non_string_elements():
    # Large Scale: 500 ints, 500 floats
    user_inputs = list(range(500))
    bot_responses = [float(i) for i in range(500)]
    response = {
        "conversation": {
            "past_user_inputs": user_inputs,
            "generated_responses": bot_responses,
        }
    }
    expected_history = list(zip(user_inputs, bot_responses, strict=False))
    chatbot_history, returned_response = chatbot_postprocess(response) # 8.77μs -> 7.91μs (10.9% faster)
# 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_postprocess-mhwsm2qw and push.