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import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import PyPDF2
import torch
import os
st.set_page_config(page_title="Perplexity-style Q&A (Mistral Auth)", layout="wide")
st.title("🧠 AI Study Assistant using Mistral 7B (Authenticated)")
# ✅ Load Hugging Face token from secrets
hf_token = os.getenv("HF_TOKEN")
@st.cache_resource
def load_model():
tokenizer = AutoTokenizer.from_pretrained(
"mistralai/Mistral-7B-Instruct-v0.1",
token=hf_token
)
model = AutoModelForCausalLM.from_pretrained(
"mistralai/Mistral-7B-Instruct-v0.1",
torch_dtype=torch.float16,
device_map="auto",
token=hf_token
)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512)
return pipe
textgen = load_model()
def extract_text_from_pdf(file):
reader = PyPDF2.PdfReader(file)
return "\n".join([p.extract_text() for p in reader.pages if p.extract_text()])
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..."):
prompt = f"[INST] Use the following context to answer the question:\n\n{context}\n\nQuestion: {query} [/INST]"
result = textgen(prompt)[0]["generated_text"]
st.success("Answer:")
st.write(result.replace(prompt, "").strip()) |