app.py

# 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=1024
)


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

# Function to generate career advice
def generate_career_advice(position_applied, job_description, resume_content):
    # The prompt for the model
    prompt = f"Considering the job description: {job_description}, and the resume provided: {resume_content}, identify areas for enhancement in the resume. Offer specific suggestions on how to improve these aspects to better match the job requirements and increase the likelihood of being selected for the position of {position_applied}."

    messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": prompt
            },
        ]
    }
]

    # Generate a response using the model with parameters
    generated_response = model.chat(messages=messages)

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

# Create Gradio interface for the career advice application
career_advice_app = gr.Interface(
    fn=generate_career_advice,
    flagging_mode="never", # Deactivate the flag function in gradio as it is not needed.
    inputs=[
        gr.Textbox(label="Position Applied For", placeholder="Enter the position you are applying for..."),
        gr.Textbox(label="Job Description Information", placeholder="Paste the job description here...", lines=10),
        gr.Textbox(label="Your Resume Content", placeholder="Paste your resume content here...", lines=10),
    ],
    outputs=gr.Textbox(label="Advice"),
    title="Career Advisor",
    description="Enter the position you're applying for, paste the job description, and your resume content to get advice on what to improve for getting this job."
)

# Launch the application
career_advice_app.launch()