Copies/gemini-batch.py

import sys
import os
import time
from google import genai
from google.genai import types
import base64
from pathlib import Path

if len(sys.argv) < 2:
    sys.exit("Usage: python script.py <directory_path>")

INPUT_DIR = sys.argv[1]
CUTLEFT_DIR = os.path.join(INPUT_DIR, 'Cutleft')


MODEL_ID = "gemini-3-flash-preview"
api_key="REMOVED_API_KEY"

my_prompt = """I'm giving you an image of the left columns of a written exam.
Students answer several exercises, which can have several questions.

The image consists of several columns, separated by vertical black
lines. The image should be read top to bottom and then left to right,
meaning first column, then second column, etc.

In their sheet, students delimit exercises and questions using
delimiters such as `Ex 1`, or `Exercice 1`, and `1)` or `a)`. You need
to give me the bounding boxes of each delimiter.

When giving the bounding box of the first question of an exercise, the
box should be large enough to contain both the exercice label
(`Exercice i`) and the question label (`1)`) parts.

You also need to give me the student name. It should appear on the top
left of the image. Disregard any mention of `MPSI 3`, it is their
class. A list of possible student names will be given below.

You will answer with a JSON object, containing a `name` field with the
name, and a `list` field, with the list of the bounding boxes and
their labels. The box_2d should be [ymin, xmin, ymax, xmax] normalized
to 0-1000.

Here is an example :
{\"name\" : \"John Doe\", \"list\" : [{\"box_2d\": (10, 20, 30, 40), \"label\" : \"Ex 1 : 1)\"}]}

Do not provide a box_2d for the name. Only for the labels.

You may find the same label present several times, as a student either
recall the current label on a new page, or adds content to its answer
later on. Give the position of each instance of each label.

For this exam you should look for the labels given below, separated by
newlines. A student need not have answered every question, so some may
be missing.

##labels##

Here's a list of the names of the students, pick the one that matches
the best or `\"Unknown\"` if you cannot read the name

##names##"""
from tqdm import tqdm

def process_batch(directory):
    client = genai.Client(api_key=api_key)
    image_files = list(Path(directory).glob("*.jpg"))

    if not image_files:
        print("No .jpg files found.")
        return

    # 1. Upload images to File API (Batch requirement)
    batch_requests = []
    print(f"Uploading {len(image_files)} images to File API...")

    for img_path in tqdm(image_files, unit="img"):
        # Upload file
        file_ref = client.files.upload(path=img_path)

        # Construct Request for JSONL
        # Note: We must serialize config manually for the JSONL body
        req_body = {
            "contents": [
                {"role": "user", "parts": [
                    {"fileData": {"mimeType": file_ref.mime_type, "fileUri": file_ref.uri}},
                    {"text": my_prompt}
                ]}
            ],
            "generationConfig": {
                "temperature": 1.0,
                "topP": 0.95,
                "maxOutputTokens": 65535,
                "thinkingConfig": {"thinkingBudget": -1}
            },
            "safetySettings": [
                {"category": cat, "threshold": "BLOCK_NONE"}
                for cat in ["HARM_CATEGORY_HATE_SPEECH", "HARM_CATEGORY_DANGEROUS_CONTENT",
                            "HARM_CATEGORY_SEXUALLY_EXPLICIT", "HARM_CATEGORY_HARASSMENT"]
            ]
        }

        # Batch Request Entry
        batch_requests.append({
            "custom_id": img_path.name,
            "method": "POST",
            "url": f"/v1beta/models/{MODEL_ID}:generateContent",
            "body": req_body
        })

    # 2. Create and Upload Batch Source File (JSONL)
    batch_file_path = os.path.join(INPUT_DIR, "batch_input.jsonl")
    with open(batch_file_path, "w") as f:
        for req in batch_requests:
            f.write(json.dumps(req) + "\n")

    batch_input_file = client.files.upload(path=batch_file_path)

    # 3. Submit Batch Job
    print("Submitting batch job...")
    job = client.batches.create(
        model=MODEL_ID,
        src=batch_input_file.name
    )
    print(f"Batch Job ID: {job.name}")

    # 4. Poll for Completion
    pbar = tqdm(desc="Processing Batch", unit="poll")
    while True:
        job = client.batches.get(name=job.name)
        if job.state == "ACTIVE":
            pbar.set_description("Processing")
        elif job.state == "SUCCEEDED" or job.state == "FAILED":
            break

        pbar.update(1)
        time.sleep(10) # Poll every 10 seconds

    pbar.close()

    if job.state == "FAILED":
        print(f"Batch job failed: {job.error}")
        return

    # 5. Retrieve and Save Results
    print("Downloading results...")
    # The output file is a remote URI, we download its content
    output_content = client.files.content(path=job.output_file.name)

    # Parse JSONL output and map back to files
    # Output format: {"custom_id": "...", "response": {...}}
    results_saved = 0
    for line in output_content.decode("utf-8").splitlines():
        if not line: continue
        result = json.loads(line)

        filename = result.get("custom_id")
        if filename:
            output_path = Path(directory) / f"{filename}.json"
            with open(output_path, "w", encoding="utf-8") as f:
                # Save the full response part
                json.dump(result.get("response", {}), f, indent=2)
            results_saved += 1

    print(f"Batch complete. Saved {results_saved} result files.")

process_batch(CUTLEFT_DIR)