Update app.py

app.py

@@ -9,14 +9,15 @@ import base64
 from concurrent.futures import ThreadPoolExecutor
 from PIL import Image, ImageOps
 
-
 from fastapi import FastAPI, UploadFile, File, Form, HTTPException
 from fastapi.staticfiles import StaticFiles
 from fastapi.middleware.cors import CORSMiddleware
-from PIL import Image
 import rarfile
 import zipfile
-import google.generativeai as genai
+
+# --- MIGRATION: New SDK Imports ---
+from google import genai
+from google.genai import types
 
 # Configure logging
 logging.basicConfig(level=logging.INFO)
@@ -88,12 +89,9 @@ def parse_srt(content: str):
     return parsed
 
 
-logger = logging.getLogger(__name__)
-
 def compress_image(image_bytes, max_width=800, quality=80):
     """
     Compresses an image to WebP (best) or optimized JPEG.
-    Renamed back to 'compress_image' to fix your error.
     """
     try:
         img = Image.open(io.BytesIO(image_bytes))
@@ -104,7 +102,6 @@ def compress_image(image_bytes, max_width=800, quality=80):
         buffer = io.BytesIO()
 
         # 2. Try WebP first (Best quality/size ratio)
-        # If you strictly need JPEG, change use_webp to False
         use_webp = True 
         
         if use_webp:
@@ -136,14 +133,14 @@ def compress_image(image_bytes, max_width=800, quality=80):
 
     except Exception as e:
         logger.error(f"Image compression failed: {e}")
-        # If logging isn't setup, print the error so you can see it
-        print(f"Error: {e}")
         return None
 
+# --- MIGRATION: Updated Gemini Processing Function ---
 def process_batch_gemini(api_key, items, model_name):
     try:
-        genai.configure(api_key=api_key)
-        model = genai.GenerativeModel(model_name)
+        # 1. Instantiate the Client (New SDK pattern)
+        # This replaces genai.configure()
+        client = genai.Client(api_key=api_key)
 
         prompt_parts = [
             "You are a Subtitle Quality Control (QC) bot.",
@@ -158,13 +155,18 @@ def process_batch_gemini(api_key, items, model_name):
             prompt_parts.append(f"Index: {item['index']}")
             prompt_parts.append(f"Expected Text: \"{item['expected_text']}\"")
             prompt_parts.append(f"Image:")
+            
+            # The new SDK handles PIL images directly in the contents list just like the old one
             img = Image.open(io.BytesIO(item['image_data']))
             prompt_parts.append(img)
 
-        # Enforce JSON mode
-        response = model.generate_content(
-            prompt_parts,
-            generation_config={"response_mime_type": "application/json"}
+        # 2. Call generate_content via the client
+        response = client.models.generate_content(
+            model=model_name,
+            contents=prompt_parts,
+            config=types.GenerateContentConfig(
+                response_mime_type="application/json"
+            )
         )
         
         text = response.text.replace("```json", "").replace("```", "").strip()
@@ -173,7 +175,6 @@ def process_batch_gemini(api_key, items, model_name):
             return json.loads(text)
         except json.JSONDecodeError as e:
             # Handle Truncated JSON (Output Token Limit Exceeded)
-            # This happens if the batch size is too large for the model's output window
             logger.warning(f"JSON Parse Error (likely truncated response): {e}. Attempting repair...")
             
             # Repair Strategy: Find the last closing brace '}', discard everything after, and close the array ']'
@@ -201,7 +202,7 @@ async def analyze_subtitles(
     media_files: list[UploadFile] = File(...),
     api_keys: str = Form(...),
     batch_size: int = Form(20),
-    model_name: str = Form("gemini-3-flash-preview"),
+    model_name: str = Form("gemini-2.0-flash"), # Updated default model hint
     compression_quality: float = Form(0.7)
 ):
     temp_dir = tempfile.mkdtemp()