Spaces:
Runtime error
Runtime error
Thanush
Refactor transcript.py to support multiple API keys for youtube-transcript.io and enhance error handling; update llm_response functions to return cost alongside error messages.
97d69f2
| import boto3 | |
| import json | |
| from dotenv import load_dotenv | |
| import os | |
| load_dotenv() | |
| bedrock_claude_3_5 = boto3.client( | |
| service_name='bedrock-runtime', | |
| aws_access_key_id = os.getenv('AWS_ACCESS_KEY_ID'), | |
| aws_secret_access_key = os.getenv('AWS_SECRET_ACCESS_KEY'), | |
| region_name = "ap-south-1" | |
| ) | |
| bedrock_claude_3_7 = boto3.client( | |
| service_name='bedrock-runtime', | |
| aws_access_key_id=os.getenv('AWS_ACCESS_KEY_ID'), | |
| aws_secret_access_key=os.getenv('AWS_SECRET_ACCESS_KEY'), | |
| region_name="us-west-2" | |
| ) | |
| def llm_response(system_prompt, user_prompt): | |
| ip_cost = (3/1000000)*86.0 | |
| op_cost = (15/1000000)*86.0 | |
| payload = { | |
| "anthropic_version": "bedrock-2023-05-31", | |
| "max_tokens": 8000, | |
| "top_k": 250, | |
| "stop_sequences": [], | |
| "temperature": 0.5, | |
| "top_p": 0.999, | |
| "messages": [ | |
| { | |
| "role": "user", | |
| "content": [ | |
| { | |
| "type": "text", | |
| "text": system_prompt + "/n/n" + user_prompt | |
| } | |
| ] | |
| } | |
| ] | |
| } | |
| try: | |
| response = bedrock_claude_3_5.invoke_model( | |
| modelId="apac.anthropic.claude-3-5-sonnet-20241022-v2:0", | |
| contentType="application/json", | |
| accept="application/json", | |
| body=json.dumps(payload) | |
| ) | |
| response_body = json.loads(response['body'].read()) | |
| cost = ip_cost*response_body['usage']['input_tokens'] + op_cost*response_body['usage']['output_tokens'] | |
| return response_body['content'][0]['text'], cost | |
| except Exception as e: | |
| return f"Error: {e}",0 | |
| def llm_response_rsng(system_prompt, user_prompt): | |
| ip_cost = (15/1000000)*86.0 | |
| op_cost = (75/1000000)*86.0 | |
| payload = { | |
| "anthropic_version": "bedrock-2023-05-31", | |
| "max_tokens": 8000, | |
| "top_k": 250, | |
| "stop_sequences": [], | |
| "temperature": 0.5, | |
| "top_p": 0.999, | |
| "messages": [ | |
| { | |
| "role": "user", | |
| "content": [ | |
| { | |
| "type": "text", | |
| "text": system_prompt + "/n/n" + user_prompt | |
| } | |
| ] | |
| } | |
| ] | |
| } | |
| try: | |
| # Replace "correct-model-id" with the actual model identifier you intend to use | |
| response = bedrock_claude_3_7.invoke_model( | |
| modelId="us.anthropic.claude-3-7-sonnet-20250219-v1:0", | |
| contentType="application/json", | |
| accept="application/json", | |
| body=json.dumps(payload) | |
| ) | |
| # Parse the response body | |
| response_body = json.loads(response['body'].read()) | |
| cost = ip_cost*response_body['usage']['input_tokens'] + op_cost*response_body['usage']['output_tokens'] | |
| return response_body['content'][0]['text'], cost | |
| except Exception as e: | |
| return f"Error: {e}",0 | |
| # if __name__ == "__main__": | |
| # system_prompt ="Act as a 23" | |
| # user_prompt ="Provide a JD for digital marketing leader" | |
| # print(llm_response(system_prompt, user_prompt)[0], "Cost: ", llm_response(system_prompt, user_prompt)[1]) | |
| # print("\n\n\n", llm_response_rsng(system_prompt, user_prompt)[0], "Cost: ", llm_response_rsng(system_prompt, user_prompt)[1]) |