feat: display the optimization reviewer result in the CLI

codeflash/api/aiservice.py

@@ -21,6 +21,7 @@
 from codeflash.models.models import (
     AIServiceRefinerRequest,
     CodeStringsMarkdown,
+    OptimizationReviewResult,
     OptimizedCandidate,
     OptimizedCandidateSource,
 )
@@ -652,7 +653,7 @@ def get_optimization_review(
         replay_tests: str,
         concolic_tests: str,  # noqa: ARG002
         calling_fn_details: str,
-    ) -> str:
+    ) -> OptimizationReviewResult:
         """Compute the optimization review of current Pull Request.
 
         Args:
@@ -670,7 +671,7 @@ def get_optimization_review(
 
         Returns:
         -------
-        - 'high', 'medium' or 'low' optimization review
+        OptimizationReviewResult with review ('high', 'medium', 'low', or '') and explanation
 
         """
         diff_str = "\n".join(
@@ -706,18 +707,21 @@ def get_optimization_review(
         except requests.exceptions.RequestException as e:
             logger.exception(f"Error generating optimization refinements: {e}")
             ph("cli-optimize-error-caught", {"error": str(e)})
-            return ""
+            return OptimizationReviewResult(review="", explanation="")
 
         if response.status_code == 200:
-            return cast("str", response.json()["review"])
+            data = response.json()
+            return OptimizationReviewResult(
+                review=cast("str", data["review"]), explanation=cast("str", data.get("review_explanation", ""))
+            )
         try:
             error = cast("str", response.json()["error"])
         except Exception:
             error = response.text
         logger.error(f"Error generating optimization review: {response.status_code} - {error}")
         ph("cli-optimize-error-response", {"response_status_code": response.status_code, "error": error})
         console.rule()
-        return ""
+        return OptimizationReviewResult(review="", explanation="")
 
     def generate_workflow_steps(
         self,

codeflash/models/models.py

@@ -20,7 +20,7 @@
 from enum import Enum, IntEnum
 from pathlib import Path
 from re import Pattern
-from typing import Annotated, Optional, cast
+from typing import Annotated, NamedTuple, Optional, cast
 
 from jedi.api.classes import Name
 from pydantic import AfterValidator, BaseModel, ConfigDict, Field, PrivateAttr, ValidationError
@@ -95,6 +95,13 @@ class AIServiceCodeRepairRequest:
     test_diffs: list[TestDiff]
 
 
+class OptimizationReviewResult(NamedTuple):
+    """Result from the optimization review API."""
+
+    review: str  # "high", "medium", "low", or ""
+    explanation: str
+
+
 # If the method spam is in the class Ham, which is at the top level of the module eggs in the package foo, the fully
 # qualified name of the method is foo.eggs.Ham.spam, its qualified name is Ham.spam, and its name is spam. The full name
 # of the module is foo.eggs.

codeflash/optimization/function_optimizer.py

@@ -85,6 +85,7 @@
     CodeOptimizationContext,
     GeneratedTests,
     GeneratedTestsList,
+    OptimizationReviewResult,
     OptimizationSet,
     OptimizedCandidate,
     OptimizedCandidateResult,
@@ -1840,19 +1841,40 @@ def process_review(
 
         raise_pr = not self.args.no_pr
         staging_review = self.args.staging_review
-        opt_review_response = ""
+        opt_review_result = OptimizationReviewResult(review="", explanation="")
         # this will now run regardless of pr, staging review flags
         try:
-            opt_review_response = self.aiservice_client.get_optimization_review(
+            opt_review_result = self.aiservice_client.get_optimization_review(
                 **data, calling_fn_details=function_references
             )
         except Exception as e:
             logger.debug(f"optimization review response failed, investigate {e}")
-        data["optimization_review"] = opt_review_response
-        self.optimization_review = opt_review_response
+        data["optimization_review"] = opt_review_result.review
+        self.optimization_review = opt_review_result.review
+
+        # Display the reviewer result to the user
+        if opt_review_result.review:
+            review_display = {
+                "high": ("[bold green]High[/bold green]", "green", "Recommended to merge"),
+                "medium": ("[bold yellow]Medium[/bold yellow]", "yellow", "Review recommended before merging"),
+                "low": ("[bold red]Low[/bold red]", "red", "Not recommended to merge"),
+            }
+            display_info = review_display.get(opt_review_result.review.lower(), ("[bold]Unknown[/bold]", "white", ""))
+            explanation_text = opt_review_result.explanation.strip() if opt_review_result.explanation else ""
+            if is_LSP_enabled():
+                md_content = f"### Reviewer Assessment: {opt_review_result.review.capitalize()}\n{display_info[2]}"
+                if explanation_text:
+                    md_content += f"\n\n{explanation_text}"
+                lsp_log(LspMarkdownMessage(markdown=md_content))
+            else:
+                panel_content = f"Reviewer Assessment: {display_info[0]}\n{display_info[2]}"
+                if explanation_text:
+                    panel_content += f"\n\n[dim]{explanation_text}[/dim]"
+                console.print(Panel(panel_content, title="Optimization Review", border_style=display_info[1]))
+
         if raise_pr or staging_review:
             data["root_dir"] = git_root_dir()
-        if raise_pr and not staging_review and opt_review_response != "low":
+        if raise_pr and not staging_review and opt_review_result.review != "low":
             # Ensure root_dir is set for PR creation (needed for async functions that skip opt_review)
             if "root_dir" not in data:
                 data["root_dir"] = git_root_dir()