app.py

import streamlit as st
import os
import tempfile
import docx2txt
from pdfminer.high_level import extract_text
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace

# Handle Hugging Face token
hf_token = os.getenv("HF_TOKEN")  # For local dev
if hf_token:
    os.environ["HUGGINGFACEHUB_API_KEY"] = hf_token

# UI Configuration
st.set_page_config(page_title="Resume Validator", layout="centered", page_icon="📄")

st.markdown("""
    <h1 style='text-align: center;'>📄 AI Resume Validator</h1>
    <p style='text-align: center;'>Upload your resume and receive instant feedback with suggestions for improvement</p>
    <br>
""", unsafe_allow_html=True)

# File upload
uploaded_file = st.file_uploader("📤 Upload Resume (PDF or DOCX)", type=["pdf", "docx"])

resume_text = ""

if uploaded_file:
    with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(uploaded_file.name)[-1]) as tmp_file:
        tmp_file.write(uploaded_file.read())
        temp_path = tmp_file.name

    # Extract text
    if uploaded_file.name.endswith(".pdf"):
        resume_text = extract_text(temp_path)
    else:
        resume_text = docx2txt.process(temp_path)

    # Remove temp file
    os.remove(temp_path)

    st.markdown("### 📃 Extracted Resume Text")
    st.text_area("Resume Text", resume_text, height=300)

    # Prompt template
    template = """
    You are an expert HR recruiter.

    Here is the content of a resume:
    {resume_text}

    Evaluate the resume on the following criteria:
    1. Clarity and grammar
    2. Relevance of skills and keywords
    3. Structure (sections like Education, Experience, Projects, etc.)
    4. Overall impact

    Provide:
    - A rating out of 10
    - Key strengths
    - Weaknesses
    - Actionable suggestions to improve
    """

    prompt = PromptTemplate(input_variables=["resume_text"], template=template)

    # LLM Configuration
    # llm = HuggingFaceEndpoint(
    #     repo_id="mistralai/Mistral-7B-Instruct-v0.3",
    #     temperature=0.5,
    #     max_new_tokens=10,
    #     task="text-generation"
    # )
    # from langchain_huggingface import HuggingFaceEndpoint

    llm = HuggingFaceEndpoint(
    repo_id="mistralai/Mistral-7B-Instruct-v0.3",
    temperature=0.5,
    max_new_tokens=10,
    task="text-generation",
    huggingfacehub_api_token=os.getenv("HF"))


    model = ChatHuggingFace(llm=llm)

    chain = LLMChain(llm=model, prompt=prompt)

    if st.button("✅ Validate Resume"):
        with st.spinner("Analyzing your resume..."):
            try:
                result = chain.run(resume_text=resume_text)
                st.success("✅ Resume Analysis Completed")
                st.markdown("### 📊 Feedback")
                st.markdown(result)
            except Exception as e:
                st.error(f"⚠️ An error occurred: {e}")
else:
    st.markdown("<center><i>Please upload your resume to start validation.</i></center>", unsafe_allow_html=True)