[FIX] log final explanation

codeflash/lsp/beta.py

@@ -14,13 +14,10 @@
 from codeflash.discovery.functions_to_optimize import filter_functions, get_functions_within_git_diff
 from codeflash.either import is_successful
 from codeflash.lsp.server import CodeflashLanguageServer, CodeflashLanguageServerProtocol
-from codeflash.result.explanation import Explanation
 
 if TYPE_CHECKING:
     from lsprotocol import types
 
-    from codeflash.models.models import GeneratedTestsList, OptimizationSet
-
 
 @dataclass
 class OptimizableFunctionsParams:
@@ -179,67 +176,6 @@ def provide_api_key(server: CodeflashLanguageServer, params: ProvideApiKeyParams
         return {"status": "error", "message": "something went wrong while saving the api key"}
 
 
-@server.feature("prepareOptimization")
-def prepare_optimization(server: CodeflashLanguageServer, params: FunctionOptimizationParams) -> dict[str, str]:
-    current_function = server.optimizer.current_function_being_optimized
-
-    module_prep_result = server.optimizer.prepare_module_for_optimization(current_function.file_path)
-    validated_original_code, original_module_ast = module_prep_result
-
-    function_optimizer = server.optimizer.create_function_optimizer(
-        current_function,
-        function_to_optimize_source_code=validated_original_code[current_function.file_path].source_code,
-        original_module_ast=original_module_ast,
-        original_module_path=current_function.file_path,
-    )
-
-    server.optimizer.current_function_optimizer = function_optimizer
-    if not function_optimizer:
-        return {"functionName": params.functionName, "status": "error", "message": "No function optimizer found"}
-
-    initialization_result = function_optimizer.can_be_optimized()
-    if not is_successful(initialization_result):
-        return {"functionName": params.functionName, "status": "error", "message": initialization_result.failure()}
-
-    return {"functionName": params.functionName, "status": "success", "message": "Optimization preparation completed"}
-
-
-@server.feature("generateTests")
-def generate_tests(server: CodeflashLanguageServer, params: FunctionOptimizationParams) -> dict[str, str]:
-    function_optimizer = server.optimizer.current_function_optimizer
-    if not function_optimizer:
-        return {"functionName": params.functionName, "status": "error", "message": "No function optimizer found"}
-
-    initialization_result = function_optimizer.can_be_optimized()
-    if not is_successful(initialization_result):
-        return {"functionName": params.functionName, "status": "error", "message": initialization_result.failure()}
-
-    should_run_experiment, code_context, original_helper_code = initialization_result.unwrap()
-
-    test_setup_result = function_optimizer.generate_and_instrument_tests(
-        code_context, should_run_experiment=should_run_experiment
-    )
-    if not is_successful(test_setup_result):
-        return {"functionName": params.functionName, "status": "error", "message": test_setup_result.failure()}
-    generated_tests_list: GeneratedTestsList
-    optimizations_set: OptimizationSet
-    generated_tests_list, _, concolic__test_str, optimizations_set = test_setup_result.unwrap()
-
-    generated_tests: list[str] = [
-        generated_test.generated_original_test_source for generated_test in generated_tests_list.generated_tests
-    ]
-    optimizations_dict = {
-        candidate.optimization_id: {"source_code": candidate.source_code.markdown, "explanation": candidate.explanation}
-        for candidate in optimizations_set.control + optimizations_set.experiment
-    }
-
-    return {
-        "functionName": params.functionName,
-        "status": "success",
-        "message": {"generated_tests": generated_tests, "optimizations": optimizations_dict},
-    }
-
-
 @server.feature("performFunctionOptimization")
 def perform_function_optimization(  # noqa: PLR0911
     server: CodeflashLanguageServer, params: FunctionOptimizationParams
@@ -351,16 +287,14 @@ def perform_function_optimization(  # noqa: PLR0911
 
         server.show_message_log(f"Optimization completed for {params.functionName} with {speedup:.2f}x speedup", "Info")
 
-        explanation = best_optimization.candidate.explanation
-        explanation_str = explanation.explanation_message() if isinstance(explanation, Explanation) else explanation
         return {
             "functionName": params.functionName,
             "status": "success",
             "message": "Optimization completed successfully",
             "extra": f"Speedup: {speedup:.2f}x faster",
             "optimization": optimized_source,
             "patch_file": str(patch_file),
-            "explanation": explanation_str,
+            "explanation": best_optimization.explanation_v2,
         }
     finally:
         cleanup_the_optimizer(server)

codeflash/models/models.py

@@ -91,6 +91,7 @@ def __hash__(self) -> int:
 
 class BestOptimization(BaseModel):
     candidate: OptimizedCandidate
+    explanation_v2: Optional[str] = None
     helper_functions: list[FunctionSource]
     code_context: CodeOptimizationContext
     runtime: int
@@ -343,7 +344,7 @@ class TestsInFile:
     test_type: TestType
 
 
-@dataclass
+@dataclass(frozen=True)
 class OptimizedCandidate:
     source_code: CodeStringsMarkdown
     explanation: str

codeflash/optimization/function_optimizer.py

@@ -1158,7 +1158,6 @@ def find_and_process_best_optimization(
                     original_helper_code,
                     code_context,
                 )
-                self.log_successful_optimization(explanation, generated_tests, exp_type)
         return best_optimization
 
     def process_review(
@@ -1232,10 +1231,9 @@ def process_review(
             file_path=explanation.file_path,
             benchmark_details=explanation.benchmark_details,
         )
+        self.log_successful_optimization(new_explanation, generated_tests, exp_type)
 
-        best_optimization.candidate.explanation = new_explanation
-
-        console.print(Panel(new_explanation_raw_str, title="Best Candidate Explanation", border_style="blue"))
+        best_optimization.explanation_v2 = new_explanation.explanation_message()
 
         data = {
             "original_code": original_code_combined,