gistfile1.txt

import sys
import os
import anthropic
from anthropic import Anthropic
from base64 import b64encode
from PIL import Image, ExifTags
import io

def fix_orientation(image):
    try:
        for orientation in ExifTags.TAGS.keys():
            if ExifTags.TAGS[orientation] == 'Orientation':
                break
        exif = dict(image._getexif().items())

        if exif[orientation] == 3:
            image = image.rotate(180, expand=True)
        elif exif[orientation] == 6:
            image = image.rotate(270, expand=True)
        elif exif[orientation] == 8:
            image = image.rotate(90, expand=True)
    except (AttributeError, KeyError, IndexError):
        # Cases: image don't have getexif
        pass
    return image

def resize_image(image_path, scale_factor=1, debug=False):
    try:
        with Image.open(image_path) as img:
            img = fix_orientation(img)
            width, height = img.size
            new_width = int(width * scale_factor)
            new_height = int(height * scale_factor)
            resized_img = img.resize((new_width, new_height), Image.LANCZOS)
            
            if debug:
                resized_img.save("testfile.jpg", "JPEG")
                print(f"Resized image saved as testfile.jpg")
                print(f"Original size: {width}x{height}, Resized: {new_width}x{new_height}")
            
            buffered = io.BytesIO()
            resized_img.save(buffered, format="JPEG")
            return b64encode(buffered.getvalue()).decode('utf-8')
    except Exception as e:
        print(f"Error processing image: {e}")
        return None

def classify_image(image_path):
    client = Anthropic(api_key=os.environ.get("ANTHROPIC_API_KEY"))

    base64_image = resize_image(image_path, scale_factor=0.2)
    if base64_image is None:
        return "Unable to process image"

    try:
        response = client.messages.create(
            model="claude-3-sonnet-20240229",
            max_tokens=1000,
            messages=[
                {
                    "role": "user",
                    "content": [
                        {
                            "type": "image",
                            "source": {
                                "type": "base64",
                                "media_type": "image/jpeg",
                                "data": base64_image
                            }
                        },
                        {
                            "type": "text",
                            "text": "Is this person an adult male, adult female, child with glasses, or child without glasses? Reply with only exactly one of those options or 'unable to classify'. Do not reply with any other text whatsoever."
                        }
                    ]
                }
            ]
        )

        classification = response.content[0].text.strip().lower()
#        print(f"Raw Claude response: {classification}")  # Debug print

        if classification == "adult male":
            return "dada"
        elif classification == "adult female":
            return "mama"
        elif classification == "child with glasses":
            return "capy"
        elif classification == "child without glasses":
            return "platy"
        else:
            return "Unable to classify"
    except Exception as e:
        print(f"Error calling Anthropic API: {e}")
        return "Error in classification process"

if __name__ == "__main__":
    if len(sys.argv) != 2:
        print("Usage: python script.py <image_filename>")
        sys.exit(1)

    image_path = sys.argv[1]
    if not os.path.exists(image_path):
        print(f"Error: File '{image_path}' does not exist.")
        sys.exit(1)

    result = classify_image(image_path)
    print(result)