import gradio as gr
import boto3
import os
import json

"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
# 1. Access Gen AI Model from Hugging Face Inference API
def respond(
    message,
    history: list[tuple[str, str]],
    system_message
):


    response = ""
    
     # 1. Access Gen AI Model from AWS Bedrock service
    bedrock_runtime = boto3.client(
            service_name='bedrock-runtime',
            region_name=os.getenv('AWS_REGION'),
            aws_access_key_id=os.getenv('AWS_ACCESS_KEY_ID'),
            aws_secret_access_key=os.getenv('AWS_SECRET_ACCESS_KEY')
        )
        
         # 2. Pass the user text to the AWS Bedrock API 
    request_body = {
            "anthropic_version": "bedrock-2023-05-31",
            "max_tokens": 1000,
            "messages": [
                {"role": "user", "content": message}
            ]
        } 


    # 3. Get the response from the Gen AI Model
    # Make the API call
    response = bedrock_runtime.invoke_model(
            modelId="anthropic.claude-3-haiku-20240307-v1:0",  # ✅ Corrected key name
            body=json.dumps(request_body)
        )
        
    # 4. Print it on the output
    # Parse and print the response
    response_body = json.loads(response['body'].read())
    print(json.dumps(response_body, indent=2))

    # Display the final answer
    yield response_body["content"][0]["text"]


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a friendly Chatbot.", label="System message")
    ],
)


if __name__ == "__main__":
    demo.launch()