pages/model.py

import os
import streamlit as st
from langchain_community.document_loaders import UnstructuredPDFLoader
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
import tempfile

os.environ["HF_TOKEN"]=os.getenv('HF_Token')
os.environ["HUGGINGFACEHUB_API_KEY"]=os.getenv('HF_Token')

llm_skeleton = HuggingFaceEndpoint(
    repo_id="deepseek-ai/DeepSeek-R1",
    provider="nebius",
    temperature=0.7,
    max_new_tokens=150,
    task="conversational"
)

llm = ChatHuggingFace(
    llm=llm_skeleton,
    repo_id="deepseek-ai/DeepSeek-R1",
    provider="nebius",
    temperature=0.7,
    max_new_tokens=150,
    task="conversational"
)

def extract_text_from_pdf(uploaded_file):
    try:
        with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp_file:
            tmp_file.write(uploaded_file.read())
            tmp_path = tmp_file.name
        loader = UnstructuredPDFLoader(tmp_path)
        return loader.load()[0].page_content
    except Exception as e:
        st.error(f"⚠️ Error reading PDF file: {e}")
        return ""

def extract_text_from_txt(uploaded_file):
    try:
        return uploaded_file.read().decode("utf-8")
    except Exception as e:
        st.error(f"⚠️ Error reading text file: {e}")
        return ""

st.set_page_config(page_title="SmartHire AI – Resume & JD Analyzer", layout="centered")
st.markdown("<h1 style='text-align:center;'>🤖 SmartHire AI</h1>", unsafe_allow_html=True)
st.markdown("<h4 style='text-align:center;color:gray;'>AI-powered Resume & Job Description Analyzer</h4><br>", unsafe_allow_html=True)

with st.container():
    st.markdown("### 📂 Upload Center")
    col1, col2 = st.columns(2)
    with col1:
        resume_file = st.file_uploader("Upload Candidate Resume (PDF)", type=["pdf"])
    with col2:
        jd_file = st.file_uploader("Upload Job Description (PDF or TXT)", type=["pdf", "txt"])
    
    jd_text = st.text_area("📋 Or paste the Job Description text below", height=150)


if st.button("🚀 Analyze Now"):
    if not resume_file and not (jd_file or jd_text):
        st.warning("⚠️ Please upload at least one resume or job description.")
    else:
        if resume_file:
            resume_text = extract_text_from_pdf(resume_file)
            if resume_text:
                resume_prompt = (
                    "Extract the following from the resume:\n"
                    "1. Name\n2. Education\n3. Experience\n4. Skills\n5. Project Names and Results\n\n"
                    f"Resume:\n{resume_text}"
                )
                resume_data = llm.invoke(resume_prompt)
                st.markdown("---")
                st.subheader("📘 Candidate Summary")
                st.markdown(f"""
                <div style='background-color:#eef6fb;padding:15px;border-left:5px solid #0099cc;border-radius:6px'>
                    {resume_data}
                </div>
                """, unsafe_allow_html=True)

        jd_text_extracted = ""
        if jd_file:
            if jd_file.type == "application/pdf":
                jd_text_extracted = extract_text_from_pdf(jd_file)
            elif jd_file.type == "text/plain":
                jd_text_extracted = extract_text_from_txt(jd_file)
        elif jd_text:
            jd_text_extracted = jd_text

        if jd_text_extracted:
            jd_prompt = (
                "Extract the following from the job description:\n"
                "1. Job ID\n2. Company Name\n3. Role\n4. Experience Required\n5. Skills Required\n"
                "6. Education Required\n7. Location\n\n"
                f"Job Description:\n{jd_text_extracted}"
            )
            jd_data = llm.invoke(jd_prompt)
            st.markdown("---")
            st.subheader("📄 Job Role Breakdown")
            st.markdown(f"""
            <div style='background-color:#fff7e6;padding:15px;border-left:5px solid #ffaa00;border-radius:6px'>
                {jd_data}
            </div>
            """, unsafe_allow_html=True)