enhanced_file_upload_ai.py

import base64
import io
import mimetypes
import os
from typing import Optional, Dict
from urllib.parse import urlparse
import requests
import json

def classify_file(fh: io.IOBase) -> Dict[str, str]:
    """Classify the file and extract metadata using an AI model."""
    # Placeholder for AI-based file classification and metadata extraction
    # Simulate AI model response
    return {
        "category": "document",
        "tags": "important, confidential",  # Assuming tags are provided as a single string
    }

def optimize_upload_parameters(file_size: int) -> Dict[str, int]:
    """Optimize upload parameters based on AI predictions."""
    # Placeholder for AI-based optimization of upload parameters
    # Simulate AI model response
    return {
        "chunk_size": 1024 * 1024,  # 1MB chunks
        "retry_limit": 5,
    }

def handle_upload_errors(error: Exception) -> None:
    """Handle errors using AI-driven retry mechanisms."""
    # Placeholder for AI-based error handling
    print(f"Error: {error}. AI retry logic will handle this.")

def upload_file(fh: io.IOBase, output_file_prefix: Optional[str] = None) -> str:
    fh.seek(0)

    # AI-based file classification
    metadata = classify_file(fh)
    print(f"File metadata: {json.dumps(metadata)}")

    if output_file_prefix is not None:
        name = getattr(fh, "name", "output")
        url = output_file_prefix + os.path.basename(name)
        try:
            resp = requests.put(url, files={"file": fh}, timeout=None)
            resp.raise_for_status()
            return url
        except requests.RequestException as e:
            handle_upload_errors(e)
            raise

    b = fh.read()
    if isinstance(b, str):
        b = b.encode("utf-8")
    encoded_body = base64.b64encode(b)
    if getattr(fh, "name", None):
        mime_type = mimetypes.guess_type(fh.name)[0]  # type: ignore
    else:
        mime_type = "application/octet-stream"
    s = encoded_body.decode("utf-8")
    return f"data:{mime_type};base64,{s}"

def put_file_to_signed_endpoint(
    fh: io.IOBase, endpoint: str, client: requests.Session, prediction_id: Optional[str]
) -> str:
    fh.seek(0)

    filename = guess_filename(fh)
    content_type, _ = mimetypes.guess_type(filename)

    # AI-based upload parameters optimization
    file_size = os.path.getsize(filename)
    upload_params = optimize_upload_parameters(file_size)

    headers = {
        "Content-Type": content_type,
        "Chunk-Size": str(upload_params["chunk_size"]),
        "Retry-Limit": str(upload_params["retry_limit"]),
    }
    if prediction_id is not None:
        headers["X-Prediction-ID"] = prediction_id

    try:
        resp = client.put(
            ensure_trailing_slash(endpoint) + filename,
            fh,
            headers=headers,
            timeout=(10, 15),
        )
        resp.raise_for_status()
    except requests.RequestException as e:
        handle_upload_errors(e)
        raise

    final_url = resp.url
    if "location" in resp.headers:
        final_url = resp.headers.get("location")

    return str(urlparse(final_url)._replace(query="").geturl())

def ensure_trailing_slash(url: str) -> str:
    return url if url.endswith("/") else url + "/"

def guess_filename(obj: io.IOBase) -> str:
    """Tries to guess the filename of the given object."""
    name = getattr(obj, "name", "file")
    return os.path.basename(name)