Enhancement of file upload script: AI, MIME Identification Compressing and Endpoint Optimization

enhanced_file_upload_ai.py

@@ -0,0 +1,106 @@
+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)