Fix batching, hopefully

correction.py

@@ -741,9 +741,17 @@ if __name__ == "__main__":
         else:
             print(f"Warning: --refaire flag used, but {refaire_path} not found.", file=sys.stderr)
 
+
     if args.batch:
-        batch_file = Path(INPUT_DIR) / "batch_requests.jsonl"
+        batch_flash_file = Path(INPUT_DIR) / "batch_requests_flash.jsonl"
-        with open(batch_file, "w", encoding="utf-8") as f:
+        batch_pro_file = Path(INPUT_DIR) / "batch_requests_pro.jsonl"
+
+        count_flash = 0
+        count_pro = 0
+
+        with open(batch_flash_file, "w", encoding="utf-8") as f_flash, \
+             open(batch_pro_file, "w", encoding="utf-8") as f_pro:
+
             for task in tasks_to_process:
                 file_path, label = task[0], task[1]
                 group_name = os.path.splitext(file_path)[0]
@@ -752,18 +760,14 @@ if __name__ == "__main__":
                 with open(json_path, 'r') as jf:
                     group_data = json.load(jf)
                 use_flash = len(group_data) >= 4 or group_data[-1][2] <= 500
-                model_to_use = MODEL_ID_flash if use_flash else MODEL_ID_pro
 
                 image_data = Path(file_path).read_bytes()
                 b64_img = base64.b64encode(image_data).decode("utf-8")
 
-                # Format payload. NOTE: adapt the JSON format if your specific Gemini
+                # Format payload matching Gemini Batch API file requirements
-                # Batch API endpoint expects a slightly different schema.
                 req = {
-                    "custom_id": file_path, # Mapping ID
+                    "key": file_path,  # The ID returned in the output file
-                    "method": "POST",
+                    "request": {
-                    "url": f"/v1beta/models/{model_to_use}:generateContent",
-                    "body": {
                         "contents": [{
                             "role": "user",
                             "parts": [
@@ -771,7 +775,7 @@ if __name__ == "__main__":
                                 {"text": make_prompt(label)}
                             ]
                         }],
-                        "generationConfig": {
+                        "generation_config": {
                             "temperature": 1.0,
                             "topP": 0.95,
                             "maxOutputTokens": 65535,
@@ -780,9 +784,18 @@ if __name__ == "__main__":
                         }
                     }
                 }
-                f.write(json.dumps(req) + "\n")
 
-        print(f"Batch generation complete. {len(tasks_to_process)} requests saved to {batch_file}")
+                if use_flash:
+                    f_flash.write(json.dumps(req) + "\n")
+                    count_flash += 1
+                else:
+                    f_pro.write(json.dumps(req) + "\n")
+                    count_pro += 1
+
+        print(f"Batch generation complete.")
+        print(f" - {count_flash} requests saved to {batch_flash_file} (for {MODEL_ID_flash})")
+        print(f" - {count_pro} requests saved to {batch_pro_file} (for {MODEL_ID_pro})")
+        print("Upload these files via the File API and create two separate batch jobs.")
         sys.exit(0)
 
     batched_responses = {}
@@ -794,19 +807,21 @@ if __name__ == "__main__":
                 for line in f:
                     if not line.strip(): continue
                     data = json.loads(line)
-                    task_id = data.get("custom_id")
+                    task_id = data.get("key") # Corresponds to the key sent in the request
 
-                    # Extract the JSON response text. Adapt this path to match your API output schema!
+                    if "response" in data:
                         try:
-                        resp_text = data["response"]["body"]["candidates"][0]["content"]["parts"][0]["text"]
+                            # Extract the JSON response text per standard Batch API schema
+                            resp_text = data["response"]["candidates"][0]["content"]["parts"][0]["text"]
                             batched_responses[task_id] = resp_text
-                    except (KeyError, IndexError):
+                        except (KeyError, IndexError) as e:
-                        batched_responses[task_id] = data.get("response_text", "")
+                            print(f"Warning: Could not parse response for {task_id}: {e}", file=sys.stderr)
+                    elif "error" in data:
+                        print(f"Batch API Error for {task_id}: {data['error']}", file=sys.stderr)
         else:
             print(f"Warning: Batch results file {batch_results_path} not found.", file=sys.stderr)
 
     print(f"Starting processing on {len(tasks_to_process)} tasks with {NB_THREADS} threads...")
-
     with concurrent.futures.ThreadPoolExecutor(max_workers=NB_THREADS) as executor:
         futures = {}
         for task in tasks_to_process: