app.py

import streamlit as st
from transformers import (
    AutoTokenizer,
    AutoModelForCausalLM,
    TextIteratorStreamer
)
from huggingface_hub import login
from threading import Thread
import PyPDF2
import pandas as pd
import torch
import time
import os

# 🔐 Hugging Face Token via Environment Variable
HF_TOKEN = os.environ.get("HF_TOKEN")
if not HF_TOKEN:
    raise ValueError("Missing Hugging Face Token. Please set the HF_TOKEN environment variable.")

# ✅ Only PT-T5 Model
MODEL_NAME = "amiguel/Meta-Llama-3.1-8B-Instruct-lei-geral-trabalho"  #"amiguel/lgt-Angola" #"amiguel/lgt-2025"##

# UI Setup
st.set_page_config(page_title="Assistente LGT | Angola", page_icon="🚀", layout="centered")
st.title("🚀 Assistente LGT | Angola 🚀")

USER_AVATAR = "https://raw.githubusercontent.com/achilela/vila_fofoka_analysis/9904d9a0d445ab0488cf7395cb863cce7621d897/USER_AVATAR.png"
BOT_AVATAR = "https://raw.githubusercontent.com/achilela/vila_fofoka_analysis/991f4c6e4e1dc7a8e24876ca5aae5228bcdb4dba/Ataliba_Avatar.jpg"

# Upload sidebar
with st.sidebar:
    st.header("Upload Documentos 📂")
    uploaded_file = st.file_uploader("Escolhe um ficheiro PDF ou XLSX", type=["pdf", "xlsx"], label_visibility="collapsed")

# Cache file processing
@st.cache_data
def process_file(uploaded_file):
    if uploaded_file is None:
        return ""
    try:
        if uploaded_file.type == "application/pdf":
            reader = PyPDF2.PdfReader(uploaded_file)
            return "\n".join(page.extract_text() or "" for page in reader.pages)
        elif uploaded_file.type == "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet":
            df = pd.read_excel(uploaded_file)
            return df.to_markdown()
    except Exception as e:
        st.error(f"📄 Erro ao processar o ficheiro: {str(e)}")
        return ""

# Cache model loading
@st.cache_resource
def load_model():
    try:
        login(token=HF_TOKEN)
        tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, token=HF_TOKEN, use_fast=False)
        model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch.float32).to("cuda" if torch.cuda.is_available() else "cpu")
        return model, tokenizer
    except Exception as e:
        st.error(f"🤖 Erro ao carregar o modelo: {str(e)}")
        return None, None

# Streaming response generation
def generate_response(prompt, context, model, tokenizer):
    full_prompt = f"Contexto:\n{context}\n\nPergunta: {prompt}\nResposta:"
    inputs = tokenizer(full_prompt, return_tensors="pt", truncation=True).to(model.device)
    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
    generation_kwargs = {
        "input_ids": inputs["input_ids"],
        "attention_mask": inputs["attention_mask"],
        "max_new_tokens": 512,
        "temperature": 0.7,
        "top_p": 0.9,
        "repetition_penalty": 1.1,
        "do_sample": True,
        "use_cache": True,
        "streamer": streamer
    }
    Thread(target=model.generate, kwargs=generation_kwargs).start()
    return streamer

# Store chat history
if "messages" not in st.session_state:
    st.session_state.messages = []

# Show chat history
for message in st.session_state.messages:
    avatar = USER_AVATAR if message["role"] == "user" else BOT_AVATAR
    with st.chat_message(message["role"], avatar=avatar):
        st.markdown(message["content"])

# Chat input
if prompt := st.chat_input("Faca uma pergunta sobre a LGT..."):
    with st.chat_message("user", avatar=USER_AVATAR):
        st.markdown(prompt)
    st.session_state.messages.append({"role": "user", "content": prompt})

    # Load model if not loaded
    if "model" not in st.session_state:
        with st.spinner("🔄 A carregar o modelo PT-T5..."):
            model, tokenizer = load_model()
            if not model:
                st.stop()
            st.session_state.model = model
            st.session_state.tokenizer = tokenizer
    else:
        model = st.session_state.model
        tokenizer = st.session_state.tokenizer

    context = process_file(uploaded_file) or "Sem contexto adicional disponível."

    # Generate assistant response
    with st.chat_message("assistant", avatar=BOT_AVATAR):
        response_box = st.empty()
        full_response = ""
        try:
            start_time = time.time()
            streamer = generate_response(prompt, context, model, tokenizer)
            for chunk in streamer:
                full_response += chunk.strip() + " "
                response_box.markdown(full_response + "▌", unsafe_allow_html=True)
            end_time = time.time()
            input_tokens = len(tokenizer(prompt)["input_ids"])
            output_tokens = len(tokenizer(full_response)["input_ids"])
            speed = output_tokens / (end_time - start_time)
            cost_usd = ((input_tokens / 1e6) * 0.0001) + ((output_tokens / 1e6) * 0.0001)
            cost_aoa = cost_usd * 1160
            st.caption(
                f"🔑 Tokens: {input_tokens} → {output_tokens} | 🕒 Velocidade: {speed:.1f}t/s | "
                f"💰 USD: ${cost_usd:.4f} | 🇦🇴 AOA: {cost_aoa:.2f}"
            )
            response_box.markdown(full_response.strip())
            st.session_state.messages.append({"role": "assistant", "content": full_response.strip()})
        except Exception as e:
            st.error(f"⚡ Erro ao gerar resposta: {str(e)}")