app.py

import os
import tempfile
import re
import torch
import gradio as gr
from PyPDF2 import PdfReader
from docx import Document as DocxDocument
from pptx import Presentation
from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_core.documents import Document

# Load Reasoning Model (lightweight + CPU friendly)
model_id = "google/flan-t5-base"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)

reasoning_pipeline = pipeline(
    "text2text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.9
)

# Embedding Model
embedding_model = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
vectorstore = None

# Summarizer
summary_pipeline = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")

def clean_text(text):
    lines = text.split("\n")
    cleaned = []
    for line in lines:
        line = line.strip()
        if re.search(r'(Page \d+|Slide \d+|CS583|UIC|Bing Liu)', line, re.IGNORECASE):
            continue
        if len(line) < 3:
            continue
        line = re.sub(r'[^\x00-\x7F]+', ' ', line)
        cleaned.append(line)
    return "\n".join(cleaned)

def extract_text(file_path, ext):
    if ext == ".pdf":
        reader = PdfReader(file_path)
        return "\n".join([page.extract_text() or "" for page in reader.pages])
    elif ext == ".docx":
        doc = DocxDocument(file_path)
        return "\n".join([p.text for p in doc.paragraphs])
    elif ext == ".txt":
        with open(file_path, "r", encoding="utf-8", errors="replace") as f:
            return f.read()
    elif ext == ".pptx":
        prs = Presentation(file_path)
        return "\n".join(shape.text for slide in prs.slides for shape in slide.shapes if hasattr(shape, "text"))
    else:
        raise ValueError("Unsupported file format")

def process_file(file):
    global vectorstore
    try:
        filename = getattr(file, "name", None)
        ext = os.path.splitext(filename)[1].lower() if filename else ".pdf"

        with tempfile.NamedTemporaryFile(delete=False, suffix=ext) as tmp:
            if hasattr(file, "read"):
                file_bytes = file.read()
            elif isinstance(file, str) and os.path.exists(file):
                with open(file, "rb") as f:
                    file_bytes = f.read()
            elif isinstance(file, bytes):
                file_bytes = file
            else:
                return "❌ Error: Could not process uploaded file."

            tmp.write(file_bytes)
            tmp.flush()
            full_text = extract_text(tmp.name, ext)

        cleaned = clean_text(full_text)
        splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=150)
        chunks = splitter.split_text(cleaned)
        docs = [Document(page_content=c) for c in chunks]
        vectorstore = FAISS.from_documents(docs, embedding_model)

        return "✅ File processed. You can now ask questions."
    except Exception as e:
        return f"❌ Error: {str(e)}"

def generate_prompt(context, question):
    return f"""Context:
{context}

Question:
{question}

Instructions:
- Answer ONLY using the above context.
- Use structured academic language.
- Think carefully and answer concisely.
- If context lacks information, say: "The material does not contain enough information to answer this accurately."
""".strip()

def detect_question_type(q):
    q = q.lower().strip()
    if q.startswith(("what is", "define", "give definition")):
        return "definition"
    elif q.startswith(("how", "explain", "why")):
        return "explanation"
    elif "difference between" in q or "compare" in q:
        return "comparison"
    elif q.startswith("list") or "types of" in q:
        return "list"
    return "general"

def post_process_output(answer_text, question):
    qtype = detect_question_type(question)
    label_map = {
        "definition": "📘 **Definition**",
        "explanation": "📘 **Explanation**",
        "comparison": "📘 **Comparison**",
        "list": "📘 **Key Points**",
        "general": "📘 **Insight**",
    }
    clean_answer = answer_text.strip()

    if clean_answer.lower().startswith("context:") or "instructions:" in clean_answer:
        for marker in ["Context:", "Question:", "Instructions:"]:
            clean_answer = clean_answer.replace(marker, "").strip()

    if len(clean_answer.split()) > 80:
        summary = summary_pipeline(clean_answer, max_length=60, min_length=25, do_sample=False)[0]['summary_text']
        clean_answer += f"\n\n📝 **Summary:** {summary.strip()}"

    return f"{label_map.get(qtype)}\n\n{clean_answer}"

def ask_question(question):
    global vectorstore
    if vectorstore is None:
        return "❌ Please upload and process a file first."

    docs = vectorstore.similarity_search(question, k=3)
    if not docs:
        return "❌ No relevant information found."

    context = "\n".join([doc.page_content for doc in docs])
    prompt = generate_prompt(context, question)
    raw_output = reasoning_pipeline(prompt)[0]['generated_text']

    # 🧠 Smart cleanup: remove prompt leakage or echoed instructions
    for section in ["Context:", "Question:", "Instructions:", "Use structured academic language"]:
        raw_output = raw_output.replace(section, "").strip()

    # Remove anything before answer starts if needed
    if "Answer:" in raw_output:
        raw_output = raw_output.split("Answer:")[-1].strip()

    # Trim trailing junk
    if "." in raw_output:
        raw_output = raw_output.rsplit(".", 1)[0] + "."

    return post_process_output(raw_output.strip(), question)


# Gradio UI
with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
    gr.Markdown("""
    # 📚 AI Study Assistant
    Upload your lecture slide/text file, ask questions, and get intelligent answers powered by Flan-T5.
    """)

    with gr.Tab("Upload & Ask"):
        with gr.Row():
            file_input = gr.File(label="📄 Upload File", file_types=[".pdf", ".docx", ".pptx", ".txt"])
            upload_btn = gr.Button("Upload")
        upload_output = gr.Textbox(label="Upload Status", interactive=False)
        upload_btn.click(fn=process_file, inputs=[file_input], outputs=[upload_output])

        gr.Markdown("---")

        with gr.Row():
            question = gr.Textbox(label="❓ Ask a question")
            ask_btn = gr.Button("Ask")
        answer = gr.Textbox(label="💡 Answer", interactive=False)
        ask_btn.click(fn=ask_question, inputs=[question], outputs=[answer])

    with gr.Tab("History"):
        gr.Markdown("""
        **⏳ Coming Soon**: Question-answer history, summarization view, and more!
        """)

if __name__ == "__main__":
    demo.launch()