app.py

"""import gradio as gr
from transformers import pipeline
import fitz  # PyMuPDF
import docx
import pptx
import openpyxl
import os

from fastapi import FastAPI
from fastapi.responses import RedirectResponse

# Load your custom summarization model
pipe = pipeline("summarization", model="facebook/bart-large-cnn", tokenizer="facebook/bart-large-cnn")

# Document text extraction function
def extract_text(file):
    ext = file.name.split(".")[-1].lower()
    path = file.name

    if ext == "pdf":
        try:
            with fitz.open(path) as doc:
                return "\n".join([page.get_text("text") for page in doc])
        except Exception as e:
            return f"Error reading PDF: {e}"

    elif ext == "docx":
        try:
            doc = docx.Document(path)
            return "\n".join([p.text for p in doc.paragraphs])
        except Exception as e:
            return f"Error reading DOCX: {e}"

    elif ext == "pptx":
        try:
            prs = pptx.Presentation(path)
            text = ""
            for slide in prs.slides:
                for shape in slide.shapes:
                    if hasattr(shape, "text"):
                        text += shape.text + "\n"
            return text
        except Exception as e:
            return f"Error reading PPTX: {e}"

    elif ext == "xlsx":
        try:
            wb = openpyxl.load_workbook(path)
            text = ""
            for sheet in wb.sheetnames:
                for row in wb[sheet].iter_rows(values_only=True):
                    text += " ".join([str(cell) for cell in row if cell]) + "\n"
            return text
        except Exception as e:
            return f"Error reading XLSX: {e}"
    else:
        return "Unsupported file format"

# Summarization logic
def summarize_document(file):
    text = extract_text(file)
    if "Error" in text or "Unsupported" in text:
        return text

    word_count = len(text.split())
    max_summary_len = max(20, int(word_count * 0.2))

    try:
        summary = pipe(text, max_length=max_summary_len, min_length=int(max_summary_len * 0.6), do_sample=False)
        # Print the summary to debug its structure
        print(summary)
        return summary[0]['summary_text']  # Access the correct key for the output
    except Exception as e:
        return f"Error during summarization: {e}"

# Gradio Interface
demo = gr.Interface(
    fn=summarize_document,
    inputs=gr.File(label="Upload a document (PDF, DOCX, PPTX, XLSX)", file_types=[".pdf", ".docx", ".pptx", ".xlsx"]),
    outputs=gr.Textbox(label="20% Summary"),
    title="📄 Document Summarizer (20% Length)",
    description="Upload a document and get a concise summary generated by your custom Hugging Face model."
)

# FastAPI setup
app = FastAPI()

# Mount Gradio at "/"
app = gr.mount_gradio_app(app, demo, path="/")

# Optional root redirect
@app.get("/")
def redirect_to_interface():
    return RedirectResponse(url="/")"""
import gradio as gr
from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
import fitz  # PyMuPDF
import docx
import pptx
import openpyxl
import re
import nltk
from nltk.tokenize import sent_tokenize
import torch
from fastapi import FastAPI
from fastapi.responses import RedirectResponse

# Download required NLTK data
nltk.download('punkt', quiet=True)

# Initialize components
app = FastAPI()

# Load summarization model (CPU optimized)
MODEL_NAME = "facebook/bart-large-cnn"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)
summarizer = pipeline(
    "summarization",
    model=model,
    tokenizer=tokenizer,
    device=-1,  # Force CPU usage
    torch_dtype=torch.float32
)

def clean_text(text: str) -> str:
    """Clean and normalize document text"""
    text = re.sub(r'\s+', ' ', text)  # Normalize whitespace
    text = re.sub(r'•\s*|\d\.\s+', '', text)  # Remove bullets and numbering
    text = re.sub(r'\[.*?\]|\(.*?\)', '', text)  # Remove brackets/parentheses
    text = re.sub(r'\bPage\s*\d+\b', '', text, flags=re.IGNORECASE)  # Remove page numbers
    return text.strip()

def extract_text(file_path: str, file_extension: str) -> tuple[str, str]:
    """Extract text from various document formats"""
    try:
        if file_extension == "pdf":
            with fitz.open(file_path) as doc:
                return clean_text("\n".join(page.get_text("text") for page in doc)), ""
            
        elif file_extension == "docx":
            doc = docx.Document(file_path)
            return clean_text("\n".join(p.text for p in doc.paragraphs)), ""
            
        elif file_extension == "pptx":
            prs = pptx.Presentation(file_path)
            text = []
            for slide in prs.slides:
                for shape in slide.shapes:
                    if hasattr(shape, "text"):
                        text.append(shape.text)
            return clean_text("\n".join(text)), ""
            
        elif file_extension == "xlsx":
            wb = openpyxl.load_workbook(file_path, read_only=True)
            text = []
            for sheet in wb.sheetnames:
                for row in wb[sheet].iter_rows(values_only=True):
                    text.append(" ".join(str(cell) for cell in row if cell))
            return clean_text("\n".join(text)), ""
            
        return "", "Unsupported file format"
    except Exception as e:
        return "", f"Error reading {file_extension.upper()} file: {str(e)}"

def chunk_text(text: str, max_tokens: int = 768) -> list[str]:
    """Split text into manageable chunks for summarization"""
    try:
        sentences = sent_tokenize(text)
    except:
        # Fallback if sentence tokenization fails
        words = text.split()
        sentences = [' '.join(words[i:i+20]) for i in range(0, len(words), 20)]
    
    chunks = []
    current_chunk = ""
    
    for sentence in sentences:
        if len(current_chunk.split()) + len(sentence.split()) <= 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") -> str:
    """Generate summary with appropriate length parameters"""
    length_params = {
        "short": {"max_length": 80, "min_length": 30},
        "medium": {"max_length": 150, "min_length": 60},
        "long": {"max_length": 200, "min_length": 80}
    }
    
    chunks = chunk_text(text)
    summaries = []
    
    for chunk in chunks:
        try:
            summary = summarizer(
                chunk,
                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
            )
            summaries.append(summary[0]['summary_text'])
        except Exception as e:
            summaries.append(f"[Chunk error: {str(e)}]")
    
    # Combine and format the final summary
    final_summary = " ".join(summaries)
    final_summary = ". ".join(s.strip().capitalize() for s in final_summary.split(". ") if s.strip())
    return final_summary if len(final_summary) > 25 else "Summary too short - document may be too brief"

def summarize_document(file, summary_length: str):
    """Main processing function for Gradio interface"""
    if file is None:
        return "Please upload a document first", "Ready"
    
    file_path = file.name
    file_extension = file_path.split(".")[-1].lower()
    
    text, error = extract_text(file_path, file_extension)
    if error:
        return error, "Error"
    
    if not text or len(text.split()) < 30:
        return "Document is too short or contains too little text to summarize", "Ready"
    
    try:
        summary = generate_summary(text, summary_length)
        return summary, "Summary complete"
    except Exception as e:
        return f"Summarization error: {str(e)}", "Error"

# Gradio Interface
with gr.Blocks(title="Document Summarizer", theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 📄 Document Summarizer")
    gr.Markdown("Upload a document to generate a concise summary")
    
    with gr.Row():
        with gr.Column():
            file_input = gr.File(
                label="Upload Document",
                file_types=[".pdf", ".docx", ".pptx", ".xlsx"],
                type="filepath"
            )
            length_radio = gr.Radio(
                ["short", "medium", "long"],
                value="medium",
                label="Summary Length"
            )
            submit_btn = gr.Button("Generate Summary", variant="primary")
        
        with gr.Column():
            output = gr.Textbox(label="Summary", lines=10)
            status = gr.Textbox(label="Status", interactive=False)
    
    submit_btn.click(
        fn=summarize_document,
        inputs=[file_input, length_radio],
        outputs=[output, status],
        api_name="summarize"
    )

# Mount Gradio app to FastAPI
app = gr.mount_gradio_app(app, demo, path="/")

@app.get("/")
def redirect_to_interface():
    return RedirectResponse(url="/")