app.py

# learning_with_fun_app.py

import os
import tempfile
import streamlit as st
import requests
from langchain_community.vectorstores import FAISS
from langchain_community.document_loaders import PyMuPDFLoader, Docx2txtLoader, UnstructuredImageLoader
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_core.documents import Document
from gtts import gTTS
import base64
import shutil

# ----------------------------- UI SETUP --------------------------------------
st.set_page_config(page_title="Learning with Fun", layout="wide")
st.markdown("""
    <style>
    .main {
        background-color: #f0f8ff;
    }
    .block-container {
        padding-top: 2rem;
    }
    .stSelectbox > label, .stTextInput > label {
        font-size: 18px;
        font-weight: bold;
        color: #2e7d32;
    }
    .stTextInput input {
        font-size: 16px;
        padding: 10px;
        border-radius: 10px;
    }
    .title-container {
        display: flex;
        align-items: center;
        gap: 20px;
    }
    .title-container img {
        height: 80px;
    }
    </style>
""", unsafe_allow_html=True)

st.markdown("""
<div class="title-container">
    <img src="https://cdn-icons-png.flaticon.com/512/201/201623.png" alt="Kids Book">
    <div>
        <h1>🌈 Learning with Fun 🎓</h1>
        <h4>Helping Kids Learn Through Interactive Books, Questions & Stories!</h4>
    </div>
</div>
""", unsafe_allow_html=True)

# ----------------------------- USER INPUT -----------------------------------
grade = st.selectbox("Select your Grade", ["Grade 5", "Grade 6"])
subject = st.selectbox("Select Subject", ["Science", "Math", "English"])

uploaded_files = st.file_uploader("Upload textbook files (PDF, DOCX, JPEG)", type=["pdf", "docx", "jpg", "jpeg"], accept_multiple_files=True)
question = st.text_input("Ask your question in English or Urdu", value="" if 'last_question' not in st.session_state else st.session_state.last_question)

submit_btn = st.button("💬 Submit Question")
clear_btn = st.button("🧹 Clear")

# ----------------------------- ENV VAR SETUP -----------------------------------
groq_api_key = os.getenv("GROQ_API_KEY", "")
if not groq_api_key:
    st.warning("GROQ API key is not set in the environment. Please configure it as a Hugging Face Secret with the name 'GROQ_API_KEY'.")

# ------------------------- SETUP TEMP FOLDER -------------------------------
temp_dir = tempfile.mkdtemp()

# ------------------------- UTILITY FUNCTIONS -------------------------------
def load_documents(uploaded_files):
    docs = []
    for file in uploaded_files:
        ext = file.name.split(".")[-1].lower()
        path = os.path.join(temp_dir, file.name)
        with open(path, "wb") as f:
            f.write(file.read())

        if ext == "pdf":
            loader = PyMuPDFLoader(path)
        elif ext == "docx":
            loader = Docx2txtLoader(path)
        elif ext in ["jpg", "jpeg"]:
            loader = UnstructuredImageLoader(path)
        else:
            continue
        docs.extend(loader.load())
    return docs

def split_documents(documents):
    splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
    return splitter.split_documents(documents)

def create_vector_store(chunks):
    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
    return FAISS.from_documents(chunks, embeddings)

def retrieve_docs(query, vector_store):
    return vector_store.similarity_search(query, k=3)

def query_llm_groq(context, query, groq_api_key):
    url = "https://api.groq.com/openai/v1/chat/completions"
    headers = {
        "Authorization": f"Bearer {groq_api_key}",
        "Content-Type": "application/json"
    }
    prompt = f"""
    Context:
    {context}

    Question:
    {query}

    Provide two outputs:
    1. A simple, educational explanation in English + Urdu.
    2. A creative storytelling version mixing English and Urdu.
    """
    data = {
        "model": "llama3-8b-8192",
        "messages": [
            {"role": "user", "content": prompt}
        ],
        "temperature": 0.7
    }
    response = requests.post(url, headers=headers, json=data)
    response.raise_for_status()
    result = response.json()
    return result["choices"][0]["message"]["content"]

def generate_audio(text, lang='ur'):
    tts = gTTS(text, lang=lang)
    audio_path = os.path.join(temp_dir, "response.mp3")
    tts.save(audio_path)
    with open(audio_path, "rb") as audio_file:
        audio_bytes = audio_file.read()
    b64 = base64.b64encode(audio_bytes).decode()
    audio_html = f'<audio controls><source src="data:audio/mp3;base64,{b64}" type="audio/mp3"></audio>'
    return audio_html

# ----------------------------- MAIN LOGIC ----------------------------------
if submit_btn and question and uploaded_files and groq_api_key:
    with st.spinner("Processing your documents and generating answer..."):
        documents = load_documents(uploaded_files)
        chunks = split_documents(documents)
        vector_db = create_vector_store(chunks)

        results = retrieve_docs(question, vector_db)
        context_text = "\n".join([doc.page_content for doc in results])
        answer = query_llm_groq(context_text, question, groq_api_key)

        st.session_state.answer = answer
        st.session_state.last_question = question

if 'answer' in st.session_state:
    st.markdown("### 📘 Answer")
    parts = st.session_state.answer.split("2.")
    if len(parts) == 2:
        st.markdown(f"**Explanation:**\n{parts[0]}")
        st.markdown(f"**Storytelling:**\n{parts[1]}")

        if st.button("🔊 Play Storytelling Voice"):
            st.markdown(generate_audio(parts[1]), unsafe_allow_html=True)
    else:
        st.markdown(st.session_state.answer)

if clear_btn:
    if 'answer' in st.session_state:
        del st.session_state['answer']

# ----------------------------- CLEANUP --------------------------------------
if os.path.exists(temp_dir):
    shutil.rmtree(temp_dir)