app.py

import streamlit as st
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
from peft import PeftModel
import torch

# Load model and tokenizer with adapter
@st.cache_resource
def load_model():
    base_model = "Qwen/Qwen3-0.6B"
    adapter_path = "faizabenatmane/Qwen-3-0.6B"

    tokenizer = AutoTokenizer.from_pretrained(base_model)

    base = AutoModelForSequenceClassification.from_pretrained(
        base_model,
        num_labels=2,
        device_map="cpu"
    )

    model = PeftModel.from_pretrained(base, adapter_path)
    model = model.merge_and_unload()

    # ✅ Text classification pipeline
    pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
    return pipe

# Load pipeline once
classifier = load_model()

# Streamlit UI
st.title("📰 Fake News Detection")
text = st.text_area("Enter a news statement or claim:", height=200)

if st.button("Check"):
    with st.spinner("Analyzing..."):
        # Get classification result
        result = classifier(text)[0]
        label = result['label']
        score = result['score']

        # Optional: format label nicely
        if "1" in label or "POSITIVE" in label.upper():
            verdict = "Real"
            emoji = "✅"
        else:
            verdict = "Fake"
            emoji = "❌"

        # Show result
        st.subheader("Prediction")
        st.success(f"{emoji} The statement is likely: **{verdict}** (confidence: {score:.2f})")