import streamlit as st
import PyPDF2
from huggingface_hub import InferenceClient

# Initialize Hugging Face Inference client
client = InferenceClient()

# Models
GRAMMAR_MODEL = "pszemraj/grammar-synthesis-base"
SENTIMENT_MODEL = "distilbert-base-uncased-finetuned-sst-2-english"

st.set_page_config(page_title="Resume & Cover Letter Analyzer", layout="wide")

st.title("📄 Resume & Cover Letter Analyzer")
st.write("Upload your resume or cover letter (PDF/Text) and get instant AI feedback.")

# File uploader
uploaded_file = st.file_uploader("Upload PDF or paste text below", type=["pdf", "txt"])

resume_text = ""

if uploaded_file:
    if uploaded_file.type == "application/pdf":
        pdf_reader = PyPDF2.PdfReader(uploaded_file)
        for page in pdf_reader.pages:
            resume_text += page.extract_text() + "\n"
    else:
        resume_text = uploaded_file.read().decode("utf-8")

# Text area input
manual_text = st.text_area("Or paste your resume / cover letter text here:")

if manual_text.strip():
    resume_text = manual_text

if resume_text:
    st.subheader("Extracted Text")
    st.write(resume_text[:1000] + "..." if len(resume_text) > 1000 else resume_text)

    st.subheader("AI Feedback")

    # Grammar correction
    with st.spinner("Checking grammar..."):
        response = client.text_generation(
            model=GRAMMAR_MODEL,
            prompt=resume_text[:500],  # shorten to avoid token limits
            max_new_tokens=200
        )
        st.markdown("✅ **Grammar Suggestions:**")
        st.write(response)

    # Sentiment analysis
    with st.spinner("Analyzing tone..."):
        sentiment = client.text_classification(
            model=SENTIMENT_MODEL,
            inputs=resume_text[:500]
        )
        st.markdown("🎭 **Tone & Sentiment:**")
        st.json(sentiment)
else:
    st.info("⬆️ Upload a file or paste text to get started.")