app.py

import os
import io
import logging
from flask import Flask, request, jsonify
from werkzeug.utils import secure_filename
from PyPDF2 import PdfReader
from docx import Document
from pptx import Presentation
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize, sent_tokenize

# Configure logging for debugging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

app = Flask(__name__)

# Use the NLTK data path set by the Dockerfile
nltk.data.path.append(os.getenv("NLTK_DATA", "/app/nltk_data"))

# Verify NLTK data is accessible (optional, for debugging)
try:
    nltk.data.find('tokenizers/punkt')
    nltk.data.find('corpora/stopwords')
    logger.info("NLTK data loaded successfully.")
except LookupError:
    logger.error("NLTK data not found. Check Dockerfile setup.")

ALLOWED_EXTENSIONS = {"pdf", "docx", "pptx", "txt"}

def allowed_file(filename):
    return "." in filename and filename.rsplit(".", 1)[1].lower() in ALLOWED_EXTENSIONS

def extractive_summary(text, num_sentences=5):
    stop_words = set(stopwords.words('english'))
    words = word_tokenize(text)
    sentences = sent_tokenize(text)
    if len(sentences) <= num_sentences:
        return text
    freq_table = {}
    for word in words:
        word = word.lower()
        if word not in stop_words and word.isalnum():
            freq_table[word] = freq_table.get(word, 0) + 1
    sentence_scores = {}
    for sentence in sentences:
        for word, freq in freq_table.items():
            if word in sentence.lower():
                sentence_scores[sentence] = sentence_scores.get(sentence, 0) + freq
    summary_sentences = sorted(sentence_scores, key=sentence_scores.get, reverse=True)[:num_sentences]
    return ' '.join(summary_sentences)

@app.route("/", methods=["GET"])
def index():
    logger.info("Root endpoint accessed.")
    return "Document Summarizer API is running! Use /summarize endpoint for POST requests."

@app.route("/summarize", methods=["POST"])
def summarize():
    logger.info("Summarize endpoint called.")
    if "file" not in request.files:
        logger.error("No file uploaded.")
        return jsonify({"error": "No file uploaded"}), 400
    file = request.files["file"]
    if file.filename == "":
        logger.error("No file selected.")
        return jsonify({"error": "No selected file"}), 400
    if not allowed_file(file.filename):
        logger.error(f"Unsupported file format: {file.filename}")
        return jsonify({"error": "Unsupported file format"}), 400
    filename = secure_filename(file.filename)
    file_content = file.read()
    file_ext = filename.rsplit(".", 1)[1].lower()
    try:
        if file_ext == "pdf":
            summary = summarize_pdf(file_content)
        elif file_ext == "docx":
            summary = summarize_docx(file_content)
        elif file_ext == "pptx":
            summary = summarize_pptx(file_content)
        elif file_ext == "txt":
            summary = summarize_txt(file_content)
        logger.info(f"File {filename} summarized successfully.")
        return jsonify({"filename": filename, "summary": summary})
    except Exception as e:
        logger.error(f"Error processing file {filename}: {str(e)}")
        return jsonify({"error": f"Error processing file: {str(e)}"}), 500

def summarize_pdf(file_content):
    reader = PdfReader(io.BytesIO(file_content))
    text = "\n".join([page.extract_text() for page in reader.pages if page.extract_text()])
    return extractive_summary(text, num_sentences=5)

def summarize_docx(file_content):
    doc = Document(io.BytesIO(file_content))
    text = "\n".join([para.text for para in doc.paragraphs])
    return extractive_summary(text, num_sentences=5)

def summarize_pptx(file_content):
    ppt = Presentation(io.BytesIO(file_content))
    text = []
    for slide in ppt.slides:
        for shape in slide.shapes:
            if hasattr(shape, "text"):
                text.append(shape.text)
    full_text = "\n".join(text)
    return extractive_summary(full_text, num_sentences=5)

def summarize_txt(file_content):
    text = file_content.decode("utf-8")
    return extractive_summary(text, num_sentences=5)

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=7860, debug=True)