| import os |
| import json |
| from io import BytesIO |
|
|
| import boto3 |
| import requests |
| from openai import OpenAI |
|
|
| DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" |
| BEDROCK_MODEL_ID = "anthropic.claude-3-5-sonnet-20240620-v1:0" |
| OPENAI_MODEL_ID = "o4-mini" |
| OPENAI_MODEL_IMAGE_MODEL_ID = "gpt-4.1-mini" |
|
|
| bedrock_runtime = boto3.client("bedrock-runtime", region_name=os.getenv("AWS_REGION")) |
| openai_client = OpenAI(api_key=os.getenv("OPENAI_API_KEY")) |
|
|
|
|
| def invoke_bedrock_model(messages: list[dict]) -> str: |
| """Invokes a Bedrock model with the provided messages.""" |
| response = bedrock_runtime.invoke_model( |
| modelId=BEDROCK_MODEL_ID, |
| body=json.dumps({"anthropic_version": "bedrock-2023-05-31", "max_tokens": 4096, "messages": messages}), |
| )["body"].read() |
| return json.loads(response)["content"][0]["text"] |
|
|
|
|
| def invoke_openai_model(messages: list[dict]) -> str: |
| """Invokes an OpenAI model with the provided messages.""" |
| response = openai_client.responses.create( |
| model=OPENAI_MODEL_IMAGE_MODEL_ID, |
| input=messages, |
| ) |
| return response.output_text |
|
|
|
|
| def get_file(task_id: str) -> BytesIO: |
| """Fetches a file associated with a task ID from the default API URL. |
| |
| Parameters |
| ---------- |
| task_id : str |
| The ID of the task for which the file is to be fetched. |
| |
| Returns |
| ------- |
| BytesIO |
| A BytesIO object containing the file content. |
| Raises |
| ------ |
| requests.exceptions.RequestException |
| If there is an error during the HTTP request. |
| Exception |
| For any other unexpected errors that may occur. |
| """ |
| url = f"{DEFAULT_API_URL}/files/{task_id}" |
| try: |
| response = requests.get(url, timeout=15) |
| response.raise_for_status() |
| return BytesIO(response.content) |
| except requests.exceptions.RequestException as e: |
| print(f"Error fetching file for task {task_id}: {e}") |
| raise |
| except Exception as e: |
| print(f"An unexpected error occurred fetching file for task {task_id}: {e}") |
| raise |
|
|
|
|
| def s3_upload_file(file_content: BytesIO, bucket_name: str, object_name: str) -> None: |
| """Uploads a file to an S3 bucket. |
| |
| Parameters |
| ---------- |
| file_content : BytesIO |
| The content of the file to upload. |
| bucket_name : str |
| The name of the S3 bucket. |
| object_name : str |
| The name of the object in the S3 bucket. |
| |
| Raises |
| ------ |
| Exception |
| If there is an error during the upload process. |
| """ |
| try: |
| s3_client = boto3.client("s3") |
| s3_client.put_object(Bucket=bucket_name, Key=object_name, Body=file_content.getvalue()) |
| except Exception as e: |
| print(f"Error uploading file to S3: {e}") |
| raise |
|
|
|
|
| def s3_download_file(bucket_name: str, object_name: str) -> BytesIO: |
| """Downloads a file from an S3 bucket. |
| |
| Parameters |
| ---------- |
| bucket_name : str |
| The name of the S3 bucket. |
| object_name : str |
| The name of the object in the S3 bucket. |
| |
| Returns |
| ------- |
| BytesIO |
| A BytesIO object containing the downloaded file content. |
| |
| Raises |
| ------ |
| Exception |
| If there is an error during the download process. |
| """ |
| try: |
| s3_client = boto3.client("s3") |
| response = s3_client.get_object(Bucket=bucket_name, Key=object_name) |
| return BytesIO(response["Body"].read()) |
| except Exception as e: |
| print(f"Error downloading file from S3: {e}") |
| raise |
|
|