UPDATE: antigravity test

requirements.txt

@@ -1,3 +1,3 @@
 gradio>=6.1.0
-google-generativeai>=0.8.3
+google-genai>=0.2.0
 pyyaml>=6.0.3

resumeRewritingService.py

@@ -1,16 +1,17 @@
-import google.generativeai as genai
+from google import genai
+from google.genai import types
 from utils import readPrompts
 import os
 import re
+import base64
 
 class ResumeEditService:
     def __init__(self):
         prompts = readPrompts(path=os.path.join(os.getcwd(), "prompts.yaml"))
         self.prompt_template = prompts.get("prompt")
         
-        # Configure the Gemini API
-        genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
-        self.model = genai.GenerativeModel('gemini-3-flash-preview')
+        # Configure the Gemini Client
+        self.client = genai.Client(api_key=os.getenv("GOOGLE_API_KEY"))
     
     def generate_optimized_resume(self, resume_base64: str, job_description: str, current_date: str) -> str:
         """
@@ -29,18 +30,20 @@ class ResumeEditService:
         prompt = self.prompt_template.replace("{{CURRENT_DATE}}", current_date)
         prompt = prompt.replace("{{JOB_DESCRIPTION}}", job_description)
         
-        # Prepare the inline data for the file attachment
-        # Assuming PDF for now based on app.py, but could support others.
-        # The app.py reads the file and base64 encodes it without a mime prefix.
-        # We need to construct the part for the model.
+        # Decode base64 to bytes for from_bytes
+        resume_bytes = base64.b64decode(resume_base64)
         
-        file_part = {
-            "mime_type": "application/pdf",
-            "data": resume_base64
-        }
-        
-        # Generate content
-        response = self.model.generate_content([prompt, file_part])
+        # Call the API using the recommended pattern
+        response = self.client.models.generate_content(
+            model='gemini-3-flash-preview', # Using a stable model name compatible with the new SDK, or revert to user's choice
+            contents=[
+                types.Part.from_bytes(
+                    data=resume_bytes,
+                    mime_type='application/pdf',
+                ),
+                prompt
+            ]
+        )
         
         # Extract text
         response_text = response.text