# app.py
"""from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
import fitz, docx, pptx, openpyxl, re, nltk, tempfile, os, easyocr, datetime, hashlib
from nltk.tokenize import sent_tokenize
from fpdf import FPDF
from gtts import gTTS

nltk.download('punkt', quiet=True)

# Load models
MODEL_NAME = "facebook/bart-large-cnn"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)
model.eval()
summarizer = pipeline("summarization", model=model, tokenizer=tokenizer, device=-1, batch_size=4)
reader = easyocr.Reader(['en'], gpu=False)

summary_cache = {}

def clean_text(text: str) -> str:
    text = re.sub(r'\s+', ' ', text)
    text = re.sub(r'\u2022\s*|\d\.\s+', '', text)
    text = re.sub(r'\[.*?\]|\(.*?\)', '', text)
    text = re.sub(r'\bPage\s*\d+\b', '', text, flags=re.IGNORECASE)
    return text.strip()

def extract_text(file_path: str, ext: str):
    try:
        if ext == "pdf":
            with fitz.open(file_path) as doc:
                text = "\n".join(page.get_text("text") for page in doc)
                if len(text.strip()) < 50:
                    images = [page.get_pixmap() for page in doc]
                    temp_img = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
                    images[0].save(temp_img.name)
                    text = "\n".join(reader.readtext(temp_img.name, detail=0))
                    os.unlink(temp_img.name)
        elif ext == "docx":
            doc = docx.Document(file_path)
            text = "\n".join(p.text for p in doc.paragraphs)
        elif ext == "pptx":
            prs = pptx.Presentation(file_path)
            text = "\n".join(shape.text for slide in prs.slides for shape in slide.shapes if hasattr(shape, "text"))
        elif ext == "xlsx":
            wb = openpyxl.load_workbook(file_path, read_only=True)
            text = "\n".join([" ".join(str(cell) for cell in row if cell) for sheet in wb.sheetnames for row in wb[sheet].iter_rows(values_only=True)])
        else:
            text = ""
    except Exception as e:
        return "", f"Error extracting text: {str(e)}"

    return clean_text(text), ""

def chunk_text(text: str, max_tokens: int = 950):
    sentences = sent_tokenize(text)
    chunks, current_chunk = [], ""
    for sentence in sentences:
        if len(tokenizer.encode(current_chunk + " " + sentence)) <= max_tokens:
            current_chunk += " " + sentence
        else:
            chunks.append(current_chunk.strip())
            current_chunk = sentence
    if current_chunk:
        chunks.append(current_chunk.strip())
    return chunks

def generate_summary(text: str, length: str = "medium"):
    cache_key = hashlib.md5((text + length).encode()).hexdigest()
    if cache_key in summary_cache:
        return summary_cache[cache_key]

    length_params = {
        "short": {"max_length": 80, "min_length": 30},
        "medium": {"max_length": 200, "min_length": 80},
        "long": {"max_length": 300, "min_length": 210}
    }

    chunks = chunk_text(text)
    summaries = summarizer(
        chunks,
        max_length=length_params[length]["max_length"],
        min_length=length_params[length]["min_length"],
        do_sample=False,
        truncation=True,
        no_repeat_ngram_size=2,
        num_beams=2,
        early_stopping=True
    )
    final_summary = " ".join(s['summary_text'] for s in summaries)
    final_summary = ". ".join(s.strip().capitalize() for s in final_summary.split(". ") if s.strip())
    final_summary = final_summary if len(final_summary) > 25 else "Summary too short."

    summary_cache[cache_key] = final_summary
    return final_summary

def text_to_speech(text: str):
    try:
        tts = gTTS(text)
        temp_audio = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3")
        tts.save(temp_audio.name)
        return temp_audio.name
    except:
        return ""

def create_pdf(summary: str, filename: str):
    try:
        pdf = FPDF()
        pdf.add_page()
        pdf.set_font("Arial", size=12)
        pdf.multi_cell(0, 10, summary)
        temp_pdf = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf")
        pdf.output(temp_pdf.name)
        return temp_pdf.name
    except:
        return ""

async def summarize_document(file, length="medium"):
    contents = await file.read()
    with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
        tmp_file.write(contents)
        tmp_path = tmp_file.name

    ext = file.filename.split('.')[-1].lower()
    text, error = extract_text(tmp_path, ext)

    if error:
        raise Exception(error)

    if not text or len(text.split()) < 30:
        raise Exception("Document too short to summarize.")

    summary = generate_summary(text, length)
    audio_path = text_to_speech(summary)
    pdf_path = create_pdf(summary, file.filename)

    result = {"summary": summary}
    if audio_path:
        result["audioUrl"] = f"/files/{os.path.basename(audio_path)}"
    if pdf_path:
        result["pdfUrl"] = f"/files/{os.path.basename(pdf_path)}"
    return result"""
# app.py

from fastapi import UploadFile, File
from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
import fitz  # PyMuPDF
import docx
import pptx
import openpyxl
import re
import nltk
import torch
from nltk.tokenize import sent_tokenize
from gtts import gTTS
from fpdf import FPDF
import tempfile
import os
import easyocr
import datetime
import hashlib

# Setup
nltk.download('punkt', quiet=True)

# Load Models
MODEL_NAME = "facebook/bart-large-cnn"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)
model.eval()
summarizer = pipeline("summarization", model=model, tokenizer=tokenizer, device=-1, batch_size=4)

reader = easyocr.Reader(['en','fr'], gpu=torch.cuda.is_available())

summary_cache = {}

# Allowed file extensions
ALLOWED_EXTENSIONS = {'pdf', 'docx', 'pptx', 'xlsx'}

# --- Helper Functions ---

def clean_text(text: str) -> str:
    text = re.sub(r'\s+', ' ', text)
    text = re.sub(r'\u2022\s*|\d\.\s+', '', text)
    text = re.sub(r'\[.*?\]|\(.*?\)', '', text)
    text = re.sub(r'\bPage\s*\d+\b', '', text, flags=re.IGNORECASE)
    return text.strip()

def extract_text(file_path: str, extension: str):
    try:
        if extension == "pdf":
            with fitz.open(file_path) as doc:
                text = "\n".join(page.get_text("text") for page in doc)
                if len(text.strip()) < 50:
                    images = [page.get_pixmap() for page in doc]
                    temp_img = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
                    images[0].save(temp_img.name)
                    ocr_result = reader.readtext(temp_img.name, detail=0)
                    os.unlink(temp_img.name)
                    text = "\n".join(ocr_result) if ocr_result else text
        elif extension == "docx":
            doc = docx.Document(file_path)
            text = "\n".join(p.text for p in doc.paragraphs)
        elif extension == "pptx":
            prs = pptx.Presentation(file_path)
            text = "\n".join(shape.text for slide in prs.slides for shape in slide.shapes if hasattr(shape, "text"))
        elif extension == "xlsx":
            wb = openpyxl.load_workbook(file_path, read_only=True)
            text = "\n".join(
                [" ".join(str(cell) for cell in row if cell) for sheet in wb.sheetnames for row in wb[sheet].iter_rows(values_only=True)]
            )
        else:
            return "", "Unsupported file format."
        
        return clean_text(text), ""

    except Exception as e:
        return "", f"Error reading {extension.upper()} file: {str(e)}"

def chunk_text(text: str, max_tokens: int = 950):
    try:
        sentences = sent_tokenize(text)
    except:
        words = text.split()
        sentences = [' '.join(words[i:i+20]) for i in range(0, len(words), 20)]

    chunks = []
    current_chunk = ""
    for sentence in sentences:
        token_length = len(tokenizer.encode(current_chunk + " " + sentence))
        if token_length <= max_tokens:
            current_chunk += " " + sentence
        else:
            if current_chunk.strip():
                chunks.append(current_chunk.strip())
            current_chunk = sentence

    if current_chunk.strip():
        chunks.append(current_chunk.strip())

    return chunks

def generate_summary(text: str, length: str = "medium"):
    cache_key = hashlib.md5((text + length).encode()).hexdigest()
    if cache_key in summary_cache:
        return summary_cache[cache_key]

    length_params = {
        "short": {"max_length": 50, "min_length": 30},
        "medium": {"max_length": 200, "min_length": 80},
        "long": {"max_length": 300, "min_length": 210}
    }
    chunks = chunk_text(text)

    summaries = summarizer(
        chunks,
        max_length=length_params[length]["max_length"],
        min_length=length_params[length]["min_length"],
        do_sample=False,
        truncation=True,
        no_repeat_ngram_size=2,
        num_beams=2,
        early_stopping=True
    )
    summary_texts = [s['summary_text'] for s in summaries]

    final_summary = " ".join(summary_texts)
    final_summary = ". ".join(s.strip().capitalize() for s in final_summary.split(". ") if s.strip())
    final_summary = final_summary if len(final_summary) > 25 else "Summary too short - document may be too brief"

    summary_cache[cache_key] = final_summary
    return final_summary

def text_to_speech(text: str):
    try:
        tts = gTTS(text)
        temp_audio = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3")
        tts.save(temp_audio.name)
        return temp_audio.name
    except Exception:
        return ""

def create_pdf(summary: str, filename: str):
    try:
        pdf = FPDF()
        pdf.add_page()
        pdf.set_font("Arial", 'B', 16)
        pdf.cell(200, 10, txt=f"Summary of {filename}", ln=1, align='C')
        pdf.set_font("Arial", size=12)
        pdf.cell(200, 10, txt=f"Generated on: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}", ln=1)
        pdf.ln(10)
        pdf.set_font("Arial", size=10)
        pdf.multi_cell(0, 10, txt=summary)
        temp_pdf = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf")
        pdf.output(temp_pdf.name)
        return temp_pdf.name
    except Exception:
        return ""

# --- Public API Function ---

async def summarize_document(file: UploadFile, length: str = "medium"):
    try:
        filename = file.filename
        extension = os.path.splitext(filename)[-1].lower().replace('.', '')

        if extension not in ALLOWED_EXTENSIONS:
            raise Exception(f"Unsupported file type: {extension.upper()}. Only PDF, DOCX, PPTX, XLSX are allowed.")

        # Save uploaded file
        with tempfile.NamedTemporaryFile(delete=False, suffix=f".{extension}") as tmp_file:
            tmp_file.write(await file.read())
            tmp_path = tmp_file.name

        # Extract text
        text, error = extract_text(tmp_path, extension)

        if error:
            os.unlink(tmp_path)
            raise Exception(error)

        if not text or len(text.split()) < 30:
            os.unlink(tmp_path)
            raise Exception("Document too short to summarize.")

        # Summarize
        summary = generate_summary(text, length)

        # Create audio + PDF
        audio_path = text_to_speech(summary)
        pdf_path = create_pdf(summary, filename)

        # Clean temp file
        os.unlink(tmp_path)

        # Prepare response
        response = {"summary": summary}
        if audio_path:
            response["audioUrl"] = f"/files/{os.path.basename(audio_path)}"
        if pdf_path:
            response["pdfUrl"] = f"/files/{os.path.basename(pdf_path)}"

        return response

    except Exception as e:
        raise Exception(f"Summarization failed: {str(e)}")