Update api.py

api.py

@@ -66,9 +66,6 @@ class ProblemDescriptionRequest(BaseModel):
     descriptions: List[str]
     technical_topic: str
 
-class ProblemDescriptionResponse(BaseModel):
-    problem_description: str
-
 # Format KI
 
 class FormattedKeyIssue(BaseModel):
@@ -96,6 +93,9 @@ class ProblemDescriptionItem(BaseModel):
     problematic: str
     score: float
 
+class EvaluateProblemDescriptionsRequest(BaseModel):
+    problem_descriptions: List[str]
+
 # --- Global Variables / State ---
 # Keep the graph instance global for efficiency if desired,
 # but consider potential concurrency issues if graph/LLMs have state.
@@ -697,9 +697,49 @@ async def create_several_probdesc_hardcoded(request: CreateSeveralProbDescReques
             'score': 0.624
         }
     ]
-    
     return hardcoded_response
 
+@app.post("/evaluate_problem_descriptions", response_model=List[ProblemDescriptionItem])
+async def evaluate_problem_descriptions_endpoint(request: EvaluateProblemDescriptionsRequest):
+    """
+    Evaluates a list of problem descriptions by generating problematics and scoring them.
+    """
+    logger.info(f"Received request to evaluate {len(request.problem_descriptions)} problem descriptions.")
+
+    if not request.problem_descriptions:
+        # Although Pydantic might catch empty list if min_items=1 is set, explicit check is good.
+        raise HTTPException(status_code=400, detail="The 'problem_descriptions' list cannot be empty.")
+
+    evaluated_results = generate_problematics_and_scores(request.problem_descriptions)
+
+    # Validate consistency of lengths
+    if len(request.problem_descriptions) != len(evaluated_results):
+        logger.error(
+            f"Mismatch between the number of input problem descriptions ({len(request.problem_descriptions)}) "
+            f"and the number of evaluated results ({len(evaluated_results)})."
+        )
+        raise HTTPException(
+            status_code=500, 
+            detail="Internal error: Mismatch occurred while processing problem descriptions."
+        )
+
+    response_items: List[ProblemDescriptionItem] = []
+    for i in range(len(request.problem_descriptions)):
+        problem_description = request.problem_descriptions[i]
+        problematic = evaluated_results[i].problematic
+        score = round(evaluated_results[i].score, 3)
+
+        response_items.append(
+            ProblemDescriptionItem(
+                problem_description=problem_description,
+                problematic=problematic,
+                score=score
+            )
+        )
+        
+    logger.info(f"Successfully evaluated and prepared {len(response_items)} problem descriptions.")
+    return response_items
+
 
 # --- How to Run ---
 if __name__ == "__main__":