# Import necessary packages
from ibm_watsonx_ai import Credentials
from ibm_watsonx_ai import APIClient
from ibm_watsonx_ai.foundation_models import Model, ModelInference
from ibm_watsonx_ai.foundation_models.schema import TextChatParameters
from ibm_watsonx_ai.metanames import GenTextParamsMetaNames
import gradio as gr

# Model and project settings
model_id = "meta-llama/llama-3-2-11b-vision-instruct"  # Directly specifying the LLAMA3 model

watsonx_API="L0sx3BXcQRWNmz45mbBLxL1UiZGnftHFQTwITAci-523"
project_id="ed8f7a2c-e597-4a09-a98f-dbdcef57a0d0"

# Set credentials to use the model
credentials = {
                   "url" : "https://au-syd.ml.cloud.ibm.com",
                   "apikey": watsonx_API
}

# Generation parameters
params = TextChatParameters(
    temperature=0.7,
    max_tokens=512
)

# Initialize the model
model = ModelInference(
    model_id=model_id,
    credentials=credentials,
    project_id=project_id,
    params=params
)

# Function to polish the resume using the model, making polish_prompt optional
def polish_resume(position_name, resume_content, polish_prompt=""):
    # Check if polish_prompt is provided and adjust the combined_prompt accordingly
    if polish_prompt and polish_prompt.strip():
        prompt_use = f"Given the resume content: '{resume_content}', polish it based on the following instructions: {polish_prompt} for the {position_name} position."
    else:
        prompt_use = f"Suggest improvements for the following resume content: '{resume_content}' to better align with the requirements and expectations of a {position_name} position. Return the polished version, highlighting necessary adjustments for clarity, relevance, and impact in relation to the targeted role."

    messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": prompt_use
            },
        ]
    }
]
    # Generate a response using the model with parameters
    generated_response = model.chat(messages=messages)

    # Extract and return the generated text
    generated_text = generated_response['choices'][0]['message']['content']

    return generated_text

# Create Gradio interface for the resume polish application, marking polish_prompt as optional
resume_polish_application = gr.Interface(
    fn=polish_resume,
    flagging_mode="never", # Deactivate the flag function in gradio as it is not needed.
    inputs=[
        gr.Textbox(label="Position Name", placeholder="Enter the name of the position..."),
        gr.Textbox(label="Resume Content", placeholder="Paste your resume content here...", lines=20),
        gr.Textbox(label="Polish Instruction (Optional)", placeholder="Enter specific instructions or areas for improvement (optional)...", lines=2),
    ],
    outputs=gr.Textbox(label="Polished Content"),
    title="Resume Polish Application",
    description="This application helps you polish your resume. Enter the position your want to apply, your resume content, and specific instructions or areas for improvement (optional), then get a polished version of your content."
)

# Launch the application
resume_polish_application.launch(share=True)