import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import PyPDF2
import torch

st.set_page_config(page_title="Perplexity-style Q&A (Mistral)", layout="wide")
st.title("🧠 Perplexity-style AI Study Assistant using Mistral 7B")

# Load Mistral model and tokenizer
@st.cache_resource
def load_model():
    tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
    model = AutoModelForCausalLM.from_pretrained(
        "mistralai/Mistral-7B-Instruct-v0.1",
        torch_dtype=torch.float16,
        device_map="auto"
    )
    pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512)
    return pipe

textgen = load_model()

# Extract text from uploaded PDF
def extract_text_from_pdf(file):
    reader = PyPDF2.PdfReader(file)
    text = ""
    for page in reader.pages:
        text += page.extract_text() + "\n"
    return text.strip()

# UI Layout
query = st.text_input("Ask a question or enter a topic:")

uploaded_file = st.file_uploader("Or upload a PDF to use as context:", type=["pdf"])

context = ""
if uploaded_file:
    context = extract_text_from_pdf(uploaded_file)
    st.text_area("📄 Extracted PDF Text", context, height=200)

if st.button("Generate Answer"):
    with st.spinner("Generating answer with Mistral 7B..."):
        prompt = query
        if context:
            prompt = f"[INST] Use the following context to answer the question:\n\n{context}\n\nQuestion: {query} [/INST]"
        else:
            prompt = f"[INST] {query} [/INST]"
        output = textgen(prompt)[0]["generated_text"]
        st.success("Answer:")
        st.write(output.replace(prompt, "").strip())