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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}")
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