gpt_fix.py

import os
import base64
import openai
from time import sleep
from tqdm import tqdm

# -------------------------------------------------------------------
# Setup
# -------------------------------------------------------------------
openai.api_key = "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"

# -------------------------------------------------------------------
# Helper: encode image as base64
# -------------------------------------------------------------------
def encode_image(image_path: str) -> str:
    with open(image_path, "rb") as f:
        return base64.b64encode(f.read()).decode("utf-8")

# -------------------------------------------------------------------
# Main function
# -------------------------------------------------------------------
def analyze_obstacles_in_folder(
    image_dir: str,
    output_path: str,
    model: str = "gpt-5",
    temperature: float = 1,
    sleep_time: float = 1.0,
):
    """
    For each .png in image_dir, send the image to GPT with the obstacle prompt
    and write results to output_path.
    """
    image_paths = [
        os.path.join(image_dir, f)
        for f in os.listdir(image_dir)
        if f.lower().endswith(".png")
    ]
    image_paths.sort()

    image_rest_names=["Flower_pot_Pos5_OOPS0.png","GarbageBag_Set1_Pos1_OOPS0.png","Safety_Cone_Pos3_OOPS1.png","trashcan_in_0L.png"]

    if not image_paths:
        print(f"No .png images found in {image_dir}")
        return

    # The list of 8 questions – stays in user message
    questions_prompt = (
        "1. Identify the obstacle on the sidewalk or walkable path ahead. "
        "2.Identify the single object most likely to be hit by a pedestrian moving straight ahead, and assign it a formal Out-of-Place Score (0–100) based only on its position.\n"
        "Scoring scale:\n"
        "0 = perfectly expected position (default/home location)\n"
        "50 = somewhat out of place from where it is typically expected\n"
        "100 = completely out of place and highly surprising\n\n"
    )

    # Correct system message (your exact paragraph)
    system_message_text = (
        "I am fully blind. You are a mobility assistant who analyzes the scene "
        "and describes obstacles for safe navigation. Be concise and accurate. "
    )

    with open(output_path, "a", encoding="utf-8") as out_f:
        for img_path in tqdm(image_paths, desc="Processing images"):
            cont_flag=True
            for fname in image_rest_names:
                if fname in img_path:
                    cont_flag=False
                    break
            if cont_flag:
                continue
            try:
                img_b64 = encode_image(img_path)

                response = openai.ChatCompletion.create(
                    model=model,
                    messages=[
                        {
                            "role": "system",
                            "content": system_message_text,
                        },
                        {
                            "role": "user",
                            "content": [
                                {"type": "text", "text": questions_prompt},
                                {
                                    "type": "image_url",
                                    "image_url": {
                                        "url": f"data:image/png;base64,{img_b64}"
                                    },
                                },
                            ],
                        },
                    ],
                    max_completion_tokens=2048
                )

                answer = response.choices[0].message.content

                out_f.write(f"IMAGE: {img_path}\n")
                out_f.write(answer.strip() + "\n")
                out_f.write("\n" + "-" * 80 + "\n\n")
                out_f.flush()

                sleep(sleep_time)

            except Exception as e:
                print(f"Error processing {img_path}: {e}")
                out_f.write(f"IMAGE: {img_path}\n")
                out_f.write(f"ERROR: {e}\n")
                out_f.write("\n" + "-" * 80 + "\n\n")
                out_f.flush()

    print(f"Done. Results saved to {output_path}")

# -------------------------------------------------------------------
# CLI
# -------------------------------------------------------------------
if __name__ == "__main__":
    import argparse

    parser = argparse.ArgumentParser(description="Process PNG images with GPT.")
    parser.add_argument("--image_dir", required=True)
    parser.add_argument("--output", required=True)
    parser.add_argument("--model", default="gpt-5")
    parser.add_argument("--temperature", type=float, default=0.2)
    parser.add_argument("--sleep", type=float, default=1.0)

    args = parser.parse_args()

    analyze_obstacles_in_folder(
        args.image_dir,
        args.output,
        model=args.model,
        temperature=args.temperature,
        sleep_time=args.sleep,
    )