app.py

import streamlit as st
from transformers import AutoModelForCausalLM, 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 = AutoModelForCausalLM.from_pretrained(base_model, torch_dtype=torch.float16, device_map="auto")

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

    pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
    return pipe

generator = load_model()

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

if st.button("Check"):
    with st.spinner("Analyzing..."):
        prompt = f"Is the following statement real or fake?\n\n{text}\n\nAnswer:"
        output = generator(prompt, max_length=50, do_sample=False)[0]['generated_text']
        answer = output.split("Answer:")[-1].strip().split()[0].lower()

        if "real" in answer:
            emoji = "✅"
            verdict = "Real"
        elif "fake" in answer:
            emoji = "❌"
            verdict = "Fake"
        else:
            emoji = "🤔"
            verdict = f"Unclear: {answer}"

        st.subheader("Prediction")
        st.success(f"{emoji} The statement is likely: **{verdict}**")