app.py

# app.py
"""
AI Study Assistant - Streamlit Application
Features:
- Upload PDF, extract text (pdfplumber / PyPDF2 fallback)
- Summarize document using OpenAI Chat API
- Generate 25+ MCQs (4 options each) using OpenAI
- Retrieval-based Q&A (embeddings + similarity)
- Handwriting-style fonts and professional UI
- Download combined output (summary, MCQs, Q&A history) as markdown (.md/.txt)
- Caching and basic cost-optimizations
"""

import os
import io
import time
import base64
import openai
#import pypdf2
from PyPDF2 import PdfReader
import pdfplumber
import dotenv # Corrected from python-dotenv
from typing import List, Tuple, Dict, Optional

import streamlit as st
import pdfplumber

import pandas as pd
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from dotenv import load_dotenv
import openai
# Load .env if present (local dev)
load_dotenv()

# Streamlit page config
st.set_page_config(page_title="AI Study Assistant", layout="wide", initial_sidebar_state="expanded")

# -------------------------
# CSS / Fonts (handwriting)
# -------------------------
HANDWRITING_FONTS = [
    "Patrick Hand",
    "Caveat",
    "Indie Flower",
    "Reenie Beanie"
]
google_fonts = "+".join([f"{f.replace(' ', '+')}:wght@400;700" for f in HANDWRITING_FONTS])
st.markdown(
    f"<link href=\"https://fonts.googleapis.com/css2?family=Patrick+Hand&family=Caveat&family=Indie+Flower&family=Reenie+Beanie&display=swap\" rel=\"stylesheet\">",
    unsafe_allow_html=True
)

st.markdown(
    f"""
    <style>
      :root {{
        --handwriting: "{HANDWRITING_FONTS[0]}", "{HANDWRITING_FONTS[1]}", cursive, sans-serif;
      }}
      body {{
        background: linear-gradient(180deg,#fbfbff,#ffffff);
      }}
      .handwriting {{
        font-family: var(--handwriting);
      }}
      .mcq-block {{
        white-space: pre-wrap;
        font-family: var(--handwriting);
        padding: 12px;
        border-radius: 8px;
        background: #fffdf7;
        border: 1px solid #f1e6d6;
      }}
      .qa-box {{
        background: #ffffff;
        border-radius: 8px;
        padding: 10px;
        box-shadow: 0 2px 8px rgba(12,12,12,0.05);
      }}
      .small-muted {{
        font-size:12px;color:#6b7280;
      }}
      .download-link {{
        margin-top: 8px;
      }}
    </style>
    """,
    unsafe_allow_html=True
)

# -------------------------
# Sidebar inputs / config
# -------------------------
st.sidebar.title("AI Study Assistant — Settings")

# API Key input (secure)
openai_key = st.sidebar.text_input("OpenAI API Key (start with sk-)", type="password", help="Your OpenAI API key. For Spaces add it to Secrets.")
if openai_key:
    os.environ["OPENAI_API_KEY"] = openai_key
elif "OPENAI_API_KEY" in os.environ:
    openai_key = os.environ.get("OPENAI_API_KEY")

# Model selection
model_choice = st.sidebar.selectbox("Generation model", options=["gpt-4", "gpt-4o", "gpt-3.5-turbo"], index=0)
emb_model_choice = st.sidebar.selectbox("Embedding model", options=["text-embedding-3-small", "text-embedding-3-large"], index=0)

# MCQ count (min 25)
mcq_target = st.sidebar.number_input("Target number of MCQs", min_value=25, max_value=200, value=30, step=1)

# Chunk/retrieval settings
chunk_size = st.sidebar.number_input("Chunk size (words)", min_value=200, max_value=2000, value=700, step=50)
chunk_overlap = st.sidebar.number_input("Chunk overlap (words)", min_value=50, max_value=500, value=150, step=10)
retrieval_k = st.sidebar.number_input("Retrieval top-k", min_value=1, max_value=8, value=4, step=1)

st.sidebar.markdown("---")
st.sidebar.markdown("**Tips:** Use PDFs with selectable text for best results. Scanned PDFs may require OCR.")

# -------------------------
# OpenAI initialization
# -------------------------
def ensure_openai_key():
    key = os.environ.get("OPENAI_API_KEY", None)
    if not key:
        raise RuntimeError("OpenAI API key not found. Set it in the sidebar or add OPENAI_API_KEY to environment.")
    openai.api_key = key

# -------------------------
# PDF extraction utilities
# -------------------------
@st.cache_data(show_spinner=False)
def extract_text_pdfplumber(file_bytes: bytes) -> str:
    """Extract text using pdfplumber (best for most PDFs). Cached to avoid repeated work."""
    text_pages = []
    try:
        with pdfplumber.open(io.BytesIO(file_bytes)) as pdf:
            for p in pdf.pages:
                txt = p.extract_text()
                if txt:
                    text_pages.append(txt)
    except Exception as e:
        # Let caller fallback to PyPDF2
        raise e
    return "\n\n".join(text_pages).strip()

@st.cache_data(show_spinner=False)
def extract_text_pypdf2(file_bytes: bytes) -> str:
    """Fallback extraction using PyPDF2."""
    text_pages = []
    try:
        reader = PyPDF2.PdfReader(io.BytesIO(file_bytes))
        for page in reader.pages:
            try:
                txt = page.extract_text()
            except Exception:
                txt = None
            if txt:
                text_pages.append(txt)
    except Exception as e:
        raise e
    return "\n\n".join(text_pages).strip()

def extract_text(file_bytes: bytes) -> str:
    """Robust extraction: try pdfplumber first, fallback to PyPDF2."""
    text = ""
    try:
        text = extract_text_pdfplumber(file_bytes)
        if not text:
            raise ValueError("pdfplumber returned empty text.")
    except Exception:
        text = extract_text_pypdf2(file_bytes)
    return text

# -------------------------
# Chunking / embeddings / retrieval
# -------------------------
@st.cache_data(show_spinner=False)
def chunk_text(text: str, words_per_chunk: int = 700, overlap: int = 150) -> List[str]:
    words = text.split()
    chunks = []
    start = 0
    L = len(words)
    while start < L:
        end = min(start + words_per_chunk, L)
        chunk = " ".join(words[start:end])
        chunks.append(chunk)
        start = end - overlap
        if start < 0:
            start = 0
    return chunks

@st.cache_data(show_spinner=False)
def get_embeddings(texts: List[str], model: str) -> List[List[float]]:
    ensure_openai_key()
    # Batch call to embeddings API
    resp = openai.Embedding.create(model=model, input=texts)
    embeddings = [row["embedding"] for row in resp["data"]]
    return embeddings

def top_k_chunks(question: str, chunks: List[str], chunk_embs: List[List[float]], k: int = 4, emb_model: str = "text-embedding-3-small"):
    ensure_openai_key()
    # compute question embedding
    q_emb = get_embeddings([question], model=emb_model)[0]
    sims = cosine_similarity([q_emb], chunk_embs)[0]
    idx = np.argsort(sims)[-k:][::-1]
    selected = [chunks[i] for i in idx]
    return selected, idx

# -------------------------
# OpenAI Chat wrappers
# -------------------------
def call_chat_completion(messages: List[Dict], model: str = "gpt-3.5-turbo", max_tokens: int = 700, temperature: float = 0.2):
    ensure_openai_key()
    try:
        resp = openai.ChatCompletion.create(
            model=model,
            messages=messages,
            max_tokens=max_tokens,
            temperature=temperature
        )
        return resp["choices"][0]["message"]["content"].strip()
    except openai.error.OpenAIError as e:
        raise RuntimeError(f"OpenAI API error: {e}")

# -------------------------
# Prompt engineering functions
# -------------------------
def generate_summary(full_text: str, model: str = "gpt-4") -> str:
    """
    Create a concise but comprehensive summary with headings and key bullets.
    To reduce tokens we can ask the model to summarize sections first (but here we send full text).
    """
    prompt = [
        {
            "role": "system",
            "content": "You are an assistant that summarizes documents for study and revision."
        },
        {
            "role": "user",
            "content": (
                "Summarize the following document for exam revision. "
                "Provide a concise executive summary (3-6 sentences), then key takeaways as bullet points, and a short list of important terms and definitions. "
                "Use clear headings. Keep the style formal and compact.\n\n"
                f"Document:\n\n{full_text}"
            )
        }
    ]
    # Limit tokens to protect cost; large docs may need chunked summarization — user can call again if needed
    return call_chat_completion(prompt, model=model, max_tokens=900, temperature=0.2)

def generate_mcqs(full_text: str, model: str = "gpt-4", count: int = 30) -> str:
    """
    Generate MCQs formatted consistently. We ask the model to return plaintext in a structured format.
    """
    instruction = (
        f"Create {count} multiple-choice questions (MCQs) based on the document below. "
        "Each question must have 4 options labeled A, B, C, D and one correct answer. "
        "Make questions diverse (recall, concept, application). Mark the correct answer on a separate 'Answer:' line. "
        "Format EXACTLY like this for each question:\n\n"
        "Question <n>: <question text>\n\n"
        "    A. <option A>\n"
        "    B. <option B>\n"
        "    C. <option C>\n"
        "D. <option D>\n\n"
        "Answer: <LETTER>\n\n"
        "Do NOT include explanations. Keep each question short and clear."
    )
    prompt = [
        {"role": "system", "content": "You are an experienced instructor who writes high-quality MCQs."},
        {"role": "user", "content": instruction + "\n\nDocument:\n\n" + full_text}
    ]
    return call_chat_completion(prompt, model=model, max_tokens=2200, temperature=0.3)

def answer_question(question: str, chunks: List[str], chunk_embs: List[List[float]], emb_model: str, gen_model: str, top_k: int = 4) -> str:
    """
    Retrieval-augmented answer: pick top_k chunks and ask model to answer using only that context.
    """
    selected_chunks, idx = top_k_chunks(question, chunks, chunk_embs, k=top_k, emb_model=emb_model)
    context = "\n\n---\n\n".join(selected_chunks)
    prompt = [
        {"role": "system", "content": "You are an assistant that answers questions using the provided context. If the answer is not in the context, say you could not find it."},
        {"role": "user", "content": f"Context:\n\n{context}\n\nQuestion: {question}\n\nAnswer concisely and cite which chunk indexes (0-based) you used."}
    ]
    return call_chat_completion(prompt, model=gen_model, max_tokens=400, temperature=0.2)

# -------------------------
# Download helpers
# -------------------------
def make_text_download(content: str, filename: str = "study_package.md"):
    b64 = base64.b64encode(content.encode()).decode()
    href = f'<a class="download-link" href="data:text/markdown;base64,{b64}" download="{filename}">Download {filename}</a>'
    return href

# -------------------------
# Session state initialization
# -------------------------
if "qa_history" not in st.session_state:
    st.session_state["qa_history"] = []  # list of dicts: question, answer, time

if "summary" not in st.session_state:
    st.session_state["summary"] = None

if "mcq_text" not in st.session_state:
    st.session_state["mcq_text"] = None

if "chunks" not in st.session_state:
    st.session_state["chunks"] = None

if "chunk_embeddings" not in st.session_state:
    st.session_state["chunk_embeddings"] = None

# -------------------------
# App UI layout
# -------------------------
st.title("📘 AI Study Assistant")
st.caption("Upload a PDF and generate a summary, 25+ MCQs, and interactively ask questions about the content.")

# Main layout: left column for upload + actions, right for results
left_col, right_col = st.columns([1.4, 2])

with left_col:
    st.header("Upload & Settings")
    uploaded_file = st.file_uploader("Upload a PDF", type=["pdf"], help="Choose a PDF with selectable text for best results.")
    if uploaded_file:
        # Read bytes
        file_bytes = uploaded_file.read()
        st.write(f"**Filename:** {uploaded_file.name} — {len(file_bytes)//1024} KB")
        # Try extracting text
        with st.spinner("Extracting text from PDF..."):
            try:
                full_text = extract_text(file_bytes)
                if not full_text or len(full_text.strip()) < 50:
                    st.warning("Extracted text is short or empty. The PDF may be scanned images. Try another PDF or enable OCR.")
                else:
                    st.success(f"Extracted {len(full_text.split())} words from PDF.")
                    # Save in session
                    st.session_state["full_text"] = full_text
            except Exception as e:
                st.error(f"Failed to extract text: {e}")
                st.stop()
    else:
        st.info("Please upload a PDF to enable summary and MCQ generation.")

    # Action buttons
    st.markdown("---")
    st.header("Generate Content")
    colA, colB = st.columns([1,1])
    with colA:
        if st.button("Generate Summary"):
            if not uploaded_file:
                st.error("Upload a PDF first.")
            else:
                try:
                    with st.spinner("Generating summary (OpenAI)..."):
                        ensure_openai_key()
                        # If document is very large, you might want to chunk and summarize iteratively.
                        summary_text = generate_summary(st.session_state["full_text"], model=model_choice)
                        st.session_state["summary"] = summary_text
                        st.success("Summary generated.")
                except Exception as e:
                    st.error(f"Summary generation failed: {e}")

    with colB:
        if st.button(f"Generate {mcq_target} MCQs"):
            if not uploaded_file:
                st.error("Upload a PDF first.")
            else:
                try:
                    with st.spinner("Generating MCQs (this may take a moment)..."):
                        ensure_openai_key()
                        mcq_text = generate_mcqs(st.session_state["full_text"], model=model_choice, count=int(mcq_target))
                        st.session_state["mcq_text"] = mcq_text
                        st.success("MCQs generated.")
                except Exception as e:
                    st.error(f"MCQ generation failed: {e}")

    # Generate both
    if st.button("Generate Summary + MCQs"):
        if not uploaded_file:
            st.error("Upload a PDF first.")
        else:
            try:
                with st.spinner("Generating summary + MCQs..."):
                    ensure_openai_key()
                    st.session_state["summary"] = generate_summary(st.session_state["full_text"], model=model_choice)
                    st.session_state["mcq_text"] = generate_mcqs(st.session_state["full_text"], model=model_choice, count=int(mcq_target))
                    st.success("Summary and MCQs generated.")
            except Exception as e:
                st.error(f"Combined generation failed: {e}")

    # Prepare retrieval infrastructure
    if uploaded_file and ("full_text" in st.session_state):
        if st.button("Prepare Q&A (create embeddings)"):
            try:
                with st.spinner("Chunking document and computing embeddings (costly operation)..."):
                    chunks = chunk_text(st.session_state["full_text"], words_per_chunk=int(chunk_size), overlap=int(chunk_overlap))
                    st.session_state["chunks"] = chunks
                    # Compute embeddings (cached)
                    chunk_embs = get_embeddings(chunks, model=emb_model_choice)
                    st.session_state["chunk_embeddings"] = chunk_embs
                    st.success(f"Prepared {len(chunks)} chunks and embeddings for retrieval.")
            except Exception as e:
                st.error(f"Failed to prepare embeddings: {e}")

    st.markdown("---")
    st.header("Download / Export")
    st.markdown("After generating content, download a combined study package.")
    if st.session_state.get("summary") or st.session_state.get("mcq_text") or st.session_state["qa_history"]:
        # Compose markdown
        composed = []
        if st.session_state.get("summary"):
            composed.append("# Summary\n\n" + st.session_state["summary"] + "\n\n")
        if st.session_state.get("mcq_text"):
            composed.append("# MCQs\n\n" + st.session_state["mcq_text"] + "\n\n")
        if st.session_state.get("qa_history"):
            qalist = ["# Q&A History\n"]
            for qa in st.session_state["qa_history"]:
                qalist.append(f"**Q:** {qa['question']}\n\n**A:** {qa['answer']}\n\n_Time:_ {qa['time']}\n\n")
            composed.append("\n".join(qalist))
        package_md = "\n".join(composed)
        st.markdown(make_text_download(package_md, filename=f"{uploaded_file.name}_study_package.md"), unsafe_allow_html=True)
        st.download_button("Download study package (.md)", package_md, file_name=f"{uploaded_file.name}_study_package.md", mime="text/markdown")
    else:
        st.info("No generated content yet. Run summary/MCQ generation first.")

with right_col:
    # Tabs: Summary, MCQ Quiz, Q&A
    tab1, tab2, tab3 = st.tabs(["\U0001f4d1 Summary", "\U0001f4dd MCQ Quiz", "\u2753 Q&A Dashboard"])

    with tab1:
        st.header("Document Summary")
        if st.session_state.get("summary"):
            st.markdown("<div class='qa-box handwriting'>", unsafe_allow_html=True)
            st.markdown(st.session_state["summary"], unsafe_allow_html=True)
            st.markdown("</div>", unsafe_allow_html=True)
        else:
            st.info("No summary yet. Click 'Generate Summary' in the left panel.")

    with tab2:
        st.header("Generated MCQs")
        if st.session_state.get("mcq_text"):
            # Display with formatting: question line and indented options vertically
            st.markdown("<div class='mcq-block'>", unsafe_allow_html=True)
            # We display as preformatted but with handwriting font and indentation
            st.text_area("MCQs (read-only)", value=st.session_state["mcq_text"], height=420, key="mcq_display")
            st.markdown("</div>", unsafe_allow_html=True)

            # Also provide CSV download parsed
            def parse_mcqs_to_df(mcq_text: str) -> pd.DataFrame:
                lines = mcq_text.splitlines()
                rows = []
                q_text = None
                opts = {"A":"","B":"","C":"","D":""}
                answer = ""
                for ln in lines:
                    if not ln.strip():
                        continue
                    # Question detection: starts with "Question" or "Q"
                    if ln.strip().lower().startswith("question"):
                        if q_text:
                            rows.append({"question": q_text.strip(), "A": opts["A"].strip(), "B": opts["B"].strip(), "C": opts["C"].strip(), "D": opts["D"].strip(), "answer": answer.strip()})
                        # reset
                        parts = ln.split(":",1)
                        if len(parts) > 1:
                            q_text = parts[1].strip()
                        else:
                            q_text = ln.strip()
                        opts = {"A":"","B":"","C":"","D":""}
                        answer = ""
                    elif ln.strip().startswith("A.") or ln.strip().startswith("A)"):
                        opts["A"] = ln.strip()[2:].strip()
                    elif ln.strip().startswith("B.") or ln.strip().startswith("B)"):
                        opts["B"] = ln.strip()[2:].strip()
                    elif ln.strip().startswith("C.") or ln.strip().startswith("C)"):
                        opts["C"] = ln.strip()[2:].strip()
                    elif ln.strip().startswith("D.") or ln.strip().startswith("D)"):
                        opts["D"] = ln.strip()[2:].strip()
                    elif ln.strip().lower().startswith("answer"):
                        parts = ln.split(":",1)
                        if len(parts) > 1:
                            answer = parts[1].strip()
                if q_text:
                    rows.append({"question": q_text.strip(), "A": opts["A"].strip(), "B": opts["B"].strip(), "C": opts["C"].strip(), "D": opts["D"].strip(), "answer": answer.strip()})
                return pd.DataFrame(rows)

            df_mcq = parse_mcqs_to_df(st.session_state["mcq_text"])
            if not df_mcq.empty:
                st.download_button("Download MCQs as CSV", df_mcq.to_csv(index=False), file_name=f"{uploaded_file.name}_mcqs.csv", mime="text/csv")
        else:
            st.info("No MCQs generated yet. Click 'Generate MCQs' in the left panel.")

    with tab3:
        st.header("Q&A Dashboard")
        st.markdown("Ask questions about the PDF. Use 'Prepare Q&A' first (computes embeddings).")
        question_input = st.text_input("Enter your question here:")
        if st.button("Ask question"):
            if not st.session_state.get("chunks") or not st.session_state.get("chunk_embeddings"):
                st.warning("Please click 'Prepare Q&A (create embeddings)' in the left panel first.")
            elif not question_input.strip():
                st.error("Please type a question.")
            else:
                try:
                    with st.spinner("Retrieving context and generating answer..."):
                        ans = answer_question(question_input, st.session_state["chunks"], st.session_state["chunk_embeddings"], emb_model_choice, model_choice, top_k=int(retrieval_k))
                        timestamp = time.strftime("%Y-%m-%d %H:%M:%S")
                        st.session_state["qa_history"].append({"question": question_input, "answer": ans, "time": timestamp})
                        st.success("Answer generated.")
                except Exception as e:
                    st.error(f"Q&A failed: {e}")

        # Show history
        if st.session_state["qa_history"]:
            st.markdown("### Recent Q&A")
            for qa in reversed(st.session_state["qa_history"][-8:]):
                st.markdown(f"<div class='qa-box'><strong>Q:</strong> {qa['question']}<br/><strong>A:</strong> {qa['answer']}<div class='small-muted'>Time: {qa['time']}</div></div>", unsafe_allow_html=True)
            # Download Q&A
            qa_md = "\n\n".join([f"Q: {qa['question']}\nA: {qa['answer']}\nTime: {qa['time']}" for qa in st.session_state["qa_history"]])
            st.download_button("Download Q&A history (.txt)", qa_md, file_name=f"{uploaded_file.name}_qa_history.txt", mime="text/plain")
        else:
            st.info("No Q&A history yet.")

# -------------------------
# Footer
# -------------------------
st.markdown("---")
st.markdown("Developed as **AI Study Assistant** — Upload a PDF, generate summary & MCQs, and ask questions!")

# End of app.py