app.py

import streamlit as st
import os
import faiss
import numpy as np
import PyPDF2
from sentence_transformers import SentenceTransformer
from groq import Groq

# Set Groq API key from environment variable
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
groq_client = Groq(api_key=GROQ_API_KEY)

# ✅ Use a compatible SentenceTransformer model for Hugging Face Spaces
embedder = SentenceTransformer("paraphrase-MiniLM-L6-v2", device='cpu')

# Example job descriptions
job_data = {
    "Data Scientist": "Python, Machine Learning, Data Analysis, Pandas, Scikit-learn, SQL",
    "Software Engineer": "C++, Java, Git, Object Oriented Programming, System Design",
    "Web Developer": "HTML, CSS, JavaScript, React, Responsive Design, Web APIs"
}
job_chunks = [f"{k}: {v}" for k, v in job_data.items()]
job_embeddings = embedder.encode(job_chunks)
faiss_index = faiss.IndexFlatL2(job_embeddings.shape[1])
faiss_index.add(np.array(job_embeddings).astype("float32"))

# ----------- Functions -----------
def extract_text_from_pdf(uploaded_file):
    reader = PyPDF2.PdfReader(uploaded_file)
    return " ".join([page.extract_text() for page in reader.pages if page.extract_text()])

def find_best_job_match(resume_text):
    query_embed = embedder.encode([resume_text])
    _, indices = faiss_index.search(np.array(query_embed).astype("float32"), 1)
    return job_chunks[indices[0][0]]

def query_llm(context, question):
    prompt = f"""You are an expert job coach. Based on the resume below and the job context:

Context: {context}

Resume: {question}

Give a simple analysis of how well this resume fits and what's missing."""

    response = groq_client.chat.completions.create(
        model="meta-llama/llama-guard-4-12b",
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message.content

# ----------- Streamlit UI -----------
st.set_page_config(page_title="🧠 Resume Analyzer + Job Match AI")
st.title("📄 Resume Analyzer + Job Match AI")

uploaded_file = st.file_uploader("Upload Your Resume (PDF)", type=["pdf"])
if uploaded_file:
    resume_text = extract_text_from_pdf(uploaded_file)
    st.success("Resume Uploaded Successfully!")
    
    st.subheader("💼 Analyzing for Job Match...")
    best_job = find_best_job_match(resume_text)
    st.write(f"🔍 Best Job Match Found: **{best_job}**")
    
    with st.spinner("Analyzing Resume Fit..."):
        response = query_llm(best_job, resume_text)
    
    st.subheader("🧾 AI Feedback:")
    st.write(response)

st.info("This app is for guidance only. Tailor your resume per job posting!")