import streamlit as st
from huggingface_hub import InferenceClient
from dotenv import load_dotenv
import os

# Load environment variables
load_dotenv()
api_key = os.getenv("HF_API_KEY")

# Initialize Hugging Face client
client = InferenceClient(api_key=api_key)

# Streamlit UI
st.title("Job Fit Assessment")
st.write("Find out if your resume is a strong fit for the job you're applying to.")

# User inputs
job_title = st.text_input("Job Title")
job_description = st.text_area("Job Description")
resume_description = st.text_area("Resume Description")
educational_qualifications = st.text_area("Educational Qualifications")

if st.button("Assess Job Fit"):
    # Combine inputs into a message for the model
    input_text = (
        f"Based on the following:\n"
        f"- Job Title: {job_title}\n"
        f"- Job Description: {job_description}\n"
        f"- Resume Description: {resume_description}\n"
        f"- Educational Qualifications: {educational_qualifications}\n\n"
        "Evaluate if this candidate's profile is a strong fit for the job."
    )
    
    messages = [{"role": "user", "content": input_text}]
    
    # Make the API call
    result = ""
    try:
        stream = client.chat.completions.create(
            model="meta-llama/Llama-3.2-1B-Instruct",
            messages=messages,
            max_tokens=500,
            stream=True
        )
        
        # Collect the response in chunks
        for chunk in stream:
            content = chunk.choices[0].delta.content
            result += content
        
        # Display the full result at once
        st.write("Job Fit Assessment Result:")
        st.write(result)

    except Exception as e:
        st.error(f"Error: {e}")