Update api.py

api.py

@@ -25,7 +25,6 @@ except ImportError as e:
 
 
 import google.generativeai as genai
-from google.generativeai.types import GenerationConfig, Content, Part # Corrected import path
 
 # Configure logging for the API
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
@@ -97,13 +96,9 @@ async def lifespan(app: FastAPI):
             if not api_key:
                  raise ValueError("GEMINI_API_KEY not found in environment or settings.")
             genai.configure(api_key=api_key)
-            # Optionally, you could create a specific model instance here if needed frequently
-            # gemini_client = genai.GenerativeModel(...)
             logger.info("Gemini client configured successfully.")
         except Exception as e:
             logger.error(f"Failed to configure Gemini client: {e}", exc_info=True)
-            # Decide if the app should fail to start or just log the error
-            # gemini_client will remain None, endpoints using it will fail
     else:
         logger.warning("Gemini library not imported. Endpoints requiring Gemini will not work.")
 
@@ -276,21 +271,10 @@ async def evaluation(request: SpecificityEvaluationRequest):
     try:
         logger.info("Calling Gemini API to generate problematic...")
         # Use the specified model and configuration
-        model_name = "gemini-1.5-flash-latest" # Changed to a generally available model
+        model_name = "gemini-2.5-flash-preview-04-17"
         model = genai.GenerativeModel(model_name)
 
-        # Prepare contents (ensure correct structure for the library version)
-        contents = [Content(role="user", parts=[Part.from_text(text=prompt)])]
-
-        # Define generation config
-        generate_config = GenerationConfig(response_mime_type="text/plain") # Updated parameter name
-
-        # Make the API call
-        response = await model.generate_content_async(
-            contents=contents,
-            generation_config=generate_config # Pass config object
-        )
-
+        response = model.generate_content(prompt)
         # Extract the result
         problematic_result = response.text.strip()
         logger.info("Successfully generated problematic from Gemini.")