app.py

# KEEPING YOUR ORIGINAL IMPORTS
import gradio as gr
import PyPDF2
import io
from transformers import pipeline, AutoTokenizer
import torch
import re
from typing import List, Tuple
import warnings
warnings.filterwarnings("ignore")

# QUESTION-ANSWERING ADDITION
qa_pipeline = pipeline("question-answering", model="deepset/roberta-base-squad2")

# === SUMMARIZER CLASS ===
class PDFSummarizer:
    def __init__(self):
        self.model_name = "sshleifer/distilbart-cnn-12-6"
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        print(f"Using device: {self.device}")
        
        try:
            self.summarizer = pipeline(
                "summarization",
                model=self.model_name,
                device=0 if self.device == "cuda" else -1,
                framework="pt",
                model_kwargs={"torch_dtype": torch.float16 if self.device == "cuda" else torch.float32}
            )
            self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
            print("Model loaded successfully")
        except Exception as e:
            print(f"Error loading model: {e}")
            self.model_name = "facebook/bart-large-cnn"
            self.summarizer = pipeline("summarization", model=self.model_name, device=-1)
            self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
            print("Fallback model loaded")

    def extract_text_from_pdf(self, pdf_file) -> str:
        try:
            pdf_reader = PyPDF2.PdfReader(io.BytesIO(pdf_file))
            text = ""
            for page_num, page in enumerate(pdf_reader.pages):
                page_text = page.extract_text()
                if page_text.strip():
                    text += f"\n--- Page {page_num + 1} ---\n"
                    text += page_text
            return text.strip()
        except Exception as e:
            raise Exception(f"Error extracting text from PDF: {str(e)}")

    def clean_text(self, text: str) -> str:
        text = re.sub(r'\s+', ' ', text)
        text = re.sub(r'[^\w\s.,!?;:()\-"]', ' ', text)
        text = re.sub(r'--- Page \d+ ---', '', text)
        return text.strip()

    def chunk_text(self, text: str, max_chunk_length: int = 512) -> List[str]:
        sentences = text.split('. ')
        chunks = []
        current_chunk = ""
        for sentence in sentences:
            potential_chunk = current_chunk + sentence + ". "
            if len(potential_chunk.split()) <= max_chunk_length:
                current_chunk = potential_chunk
            else:
                if current_chunk:
                    chunks.append(current_chunk.strip())
                current_chunk = sentence + ". "
        if current_chunk:
            chunks.append(current_chunk.strip())
        return chunks[:5]

    def summarize_chunk(self, chunk: str, max_length: int = 100, min_length: int = 30) -> str:
        try:
            summary = self.summarizer(
                chunk,
                max_length=max_length,
                min_length=min_length,
                do_sample=False,
                truncation=True,
                early_stopping=True,
                num_beams=2
            )
            return summary[0]['summary_text']
        except Exception as e:
            return f"Error summarizing chunk: {str(e)}"

    def process_pdf(self, pdf_file, summary_type: str) -> Tuple[str, str, str]:
        try:
            raw_text = self.extract_text_from_pdf(pdf_file)
            if not raw_text.strip():
                return "❌ Error: No text could be extracted from the PDF.", "", ""
            cleaned_text = self.clean_text(raw_text)
            word_count = len(cleaned_text.split())
            char_count = len(cleaned_text)
            if word_count < 50:
                return "❌ Error: PDF contains too little text to summarize.", "", ""
            chunks = self.chunk_text(cleaned_text)
            if summary_type == "Brief (Quick)":
                max_len, min_len = 60, 20
            elif summary_type == "Detailed":
                max_len, min_len = 100, 40
            else:
                max_len, min_len = 150, 60
            chunk_summaries = []
            for i, chunk in enumerate(chunks):
                print(f"Processing chunk {i+1}/{len(chunks)}")
                summary = self.summarize_chunk(chunk, max_len, min_len)
                chunk_summaries.append(summary)
            combined_summary = " ".join(chunk_summaries)
            if len(chunks) <= 2:
                final_summary = combined_summary
            else:
                final_summary = self.summarize_chunk(
                    combined_summary, 
                    max_length=min(200, max_len * 1.5), 
                    min_length=min_len
                )
            summary_stats = f"""
📊 **Document Statistics:**
- Original word count: {word_count:,}
- Original character count: {char_count:,}
- Pages processed: {len(chunks)}
- Summary word count: {len(final_summary.split()):,}
- Compression ratio: {word_count / len(final_summary.split()):.1f}:1
            """
            return final_summary, summary_stats, "✅ Summary generated successfully!"
        except Exception as e:
            return f"❌ Error processing PDF: {str(e)}", "", ""

pdf_summarizer = PDFSummarizer()
global_pdf_text = ""  # used for QA

def summarize_pdf_interface(pdf_file, summary_type):
    global global_pdf_text
    if pdf_file is None:
        return "❌ Please upload a PDF file.", "", ""
    try:
        with open(pdf_file, 'rb') as f:
            pdf_content = f.read()
        global_pdf_text = pdf_summarizer.clean_text(pdf_summarizer.extract_text_from_pdf(pdf_content))
        summary, stats, status = pdf_summarizer.process_pdf(pdf_content, summary_type)
        return summary, stats, status
    except Exception as e:
        return f"❌ Error: {str(e)}", "", ""

# === NEW: QA FUNCTION ===
def answer_question_interface(question):
    if not global_pdf_text:
        return "❌ Please upload and summarize a PDF first."
    try:
        answer = qa_pipeline(question=question, context=global_pdf_text)
        return answer["answer"]
    except Exception as e:
        return f"❌ Error: {str(e)}"

# === GRADIO INTERFACE ===
def create_interface():
    with gr.Blocks(title="📄 AI PDF Summarizer & QA", theme=gr.themes.Soft()) as interface:
        gr.Markdown("# 📄 PDF Summarizer + 💬 Question Answering")

        with gr.Row():
            with gr.Column(scale=1):
                pdf_input = gr.File(label="📁 Upload PDF", file_types=[".pdf"], type="filepath")
                summary_type = gr.Radio(
                    choices=["Brief (Quick)", "Detailed", "Comprehensive"],
                    value="Detailed",
                    label="📏 Summary Length"
                )
                summarize_btn = gr.Button("🚀 Generate Summary", variant="primary")
                status_output = gr.Textbox(label="📋 Status", interactive=False, max_lines=2)
            with gr.Column(scale=2):
                summary_output = gr.Textbox(label="📝 Summary", lines=15, interactive=False)
                stats_output = gr.Markdown(label="📊 Document Statistics")

        summarize_btn.click(
            fn=summarize_pdf_interface,
            inputs=[pdf_input, summary_type],
            outputs=[summary_output, stats_output, status_output]
        )
        pdf_input.change(
            fn=summarize_pdf_interface,
            inputs=[pdf_input, summary_type],
            outputs=[summary_output, stats_output, status_output]
        )

        gr.Markdown("## 💬 Ask a Question About the PDF")
        with gr.Row():
            question_input = gr.Textbox(label="❓ Your Question", placeholder="e.g. What is the main finding?")
            answer_output = gr.Textbox(label="💡 Answer", interactive=False)
        question_input.submit(fn=answer_question_interface, inputs=question_input, outputs=answer_output)

    return interface

# === MAIN ===
if __name__ == "__main__":
    interface = create_interface()
    interface.launch()