# Import necessary packages
from ibm_watson_machine_learning.foundation_models import Model
import gradio as gr

import os

# Securely load the API key and project ID
watsonx_API = os.getenv("WATSONX_API")
project_id= os.getenv("PROJECT_ID")

# Model and project settings
model_id = "meta-llama/llama-2-13b-chat"  # Directly specifying the LLAMA2 model

# Generation parameters
gen_parms = {
    "max_new_tokens": 512,  # Adjust as needed for the length of the cover letter
    "temperature": 0.7  # Adjust for creativity
}

# Initialize the model
model = Model(model_id, watsonx_API, gen_parms, project_id)

# Function to generate customized career advice
def generate_career_advice(field, position_name, current_qualifications, likes, skills):
    # Craft the prompt for the model 
    prompt = f"Generate a customized career advice using field: {field}, \
    position_name: {position_name}, \
    current_qualifications: {current_qualifications}, \
    likes: {likes}, \
    skills: {skills}."

    generated_response = model.generate(prompt, gen_parms)

    # Extract the generated text
    career_advice = generated_response["results"][0]["generated_text"]
    return career_advice

# Create Gradio interface for the cover letter generation application
career_advice_app = gr.Interface(
    fn=generate_career_advice,
    allow_flagging="never",  # Deactivate the flag function in gradio as it is not needed.
    inputs=[
        gr.Textbox(label="Field of Interest (e.g., healthcare, trades, social service, etc., or enter 'not sure')", placeholder="Enter the field which you are interested in... or type 'not sure'."),
        gr.Textbox(label="Position Name (e.g., nurse, personal support worker, software developer, plumber, etc., or enter 'not sure')", placeholder="Enter the name of the position you are interested in... or type 'not sure'"),
        gr.Textbox(label="Current Qualifications (e.g., studying in high school, high school diploma, college diploma, etc.)", placeholder="Enter your current qualifications ..."),
        gr.Textbox(label="Likes (e.g., I like working with my hands, I like to work outside, I like to help people, I like teaching, ...", placeholder="Enter activities you like ...", lines=10),
        gr.Textbox(label="Skills (e.g., I am good at math, science, languages, computers, research, hand tools, etc.)", placeholder="Skills ...", lines=10),
    ],
    outputs=gr.Textbox(label="Customized Career Advice"),
    title="Customized Career Advice",
    description="Generate a customized career advice using field, position name, likes, and skills"
)

# Launch the application
career_advice_app.launch(server_name="0.0.0.0", debug=True, server_port=7860, share=True)