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README_DOCUVERSE.md

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+# DocuVerse AI Guide
+
+A modern PDF/text analysis and summarization app built with Streamlit.
+
+## What’s inside
+- Document upload and text input (5000-word limit for input text)
+- Analysis: word/sentence counts, complexity index, reading-time estimate, and key phrases
+- Summarization: styles (Executive, Academic, Bullet/Key Points, Narrative, Technical) and approaches (Extractive, Abstractive, Hybrid)
+- Q&A: document-grounded answers only
+- Download summaries as TXT or PDF-mime
+
+## How summarization works (in code)
+The summarization logic is in `app.py` under the class `QuantumSummarizer`.
+
+- Entry point: `quantum_summarize(text, style, sentences, summary_type)`
+  - Chooses a path based on `summary_type`: `extractive`, `abstractive`, or `hybrid`.
+
+- Extractive (`_extractive_summary`):
+  1. Split text into sentences and compute a score per sentence via `_quantum_score`.
+  2. `_quantum_score` blends:
+     - length_score: prefers informative lengths
+     - pos_score: favors early sentences (lead-bias for overviews)
+     - freq_score: term-frequency emphasis using `_quantum_frequency_analysis` over the whole doc
+  3. Style-specific weighting via `_apply_quantum_weights` (e.g., executive favors early content, bullet favors shorter, punchy lines).
+  4. Pick top-N scored sentences, restore original order, and join them.
+
+- Abstractive (`_abstractive_summary`):
+  1. Extract key concepts with `_extract_key_concepts` (term frequency without common stop words).
+  2. Rank sentences by concept density with `_concept_score`.
+  3. Create a compressed/abstracted version of the best sentences via `_abstract_sentence` by simplifying structure while preserving key terms.
+
+- Hybrid (`_hybrid_summary`):
+  1. Generate an extractive summary (signal preservation).
+  2. Generate an abstractive summary (compression and smoothing).
+  3. Merge and deduplicate with `_optimize_hybrid_summary` to remove redundancy and cap length.
+
+All paths return a dictionary like:
+```
+{"summary": str, "confidence": float, "method": str, "type": str}
+```
+
+## How Q&A is retrieved
+Q&A lives in `NeuroQA` (also in `app.py`). It is strictly document-grounded.
+
+- Input: `question` + `document` (the extracted or pasted text)
+- Context retrieval (`_discover_neural_contexts`):
+  1. Split the document into sentences and build sliding windows (3-sentence chunks).
+  2. Compute lexical overlap between question terms and each chunk.
+  3. Keep the highest-overlap chunks sorted by score (top 3).
+- Answer synthesis (`neural_answer`):
+  1. Take the best chunk and split into sentences.
+  2. Select the sentence with the largest word-overlap with the question as the answer (fallbacks to first sentence if needed).
+  3. Report a confidence derived from the retrieval score.
+
+This approach ensures answers are drawn only from the provided document text. If no overlap is found, the system responds accordingly.
+
+## Running the app
+```bash
+pip install -r requirements.txt
+streamlit run app.py
+```
+
+## Usage flow
+1. Document Upload or Text Input
+2. Analysis → review stats and key phrases
+3. Summary → choose Style + Approach, set Length → Generate Summary → Download
+4. Q&A → ask a question → get a grounded answer
+
+## Notes
+- The “Engine” UI for external models is disabled by default to maximize portability. The built-in summarizer is fast and offline.
+- PDF downloads are provided with text MIME for portability. Use a PDF generator (e.g., ReportLab) if you need fully formatted PDFs.
+
+## File layout
+```
+DOCUVERSE AI/
+├── app.py
+├── requirements.txt
+├── run.sh / run.bat
+└── README.md (this guide)
+```
+
+## License
+© 2025 Justine & Krishna. All Rights Reserved.
+DocuVerse AI – Revolutionary PDF Intelligence Platform

app.py

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+#!/usr/bin/env python3
+"""
+🌟 DOCUVERSE AI 🌟
+Revolutionary PDF Assistant with stunning design and proper footer
+Copyright © 2025 Justine & Krishna. All Rights Reserved.
+"""
+
+import streamlit as st
+import PyPDF2
+import re
+import time
+import hashlib
+from datetime import datetime
+from typing import Dict, List, Tuple
+import io
+import base64
+
+# Page Configuration
+st.set_page_config(
+    page_title="DocuVerse AI - Revolutionary PDF Assistant",
+    page_icon="",
+    layout="wide"
+)
+
+def load_revolutionary_css():
+    """Load the most stunning CSS ever created"""
+    st.markdown("""
+    <style>
+    @import url('https://fonts.googleapis.com/css2?family=Orbitron:wght@300;400;700;900&family=Rajdhani:wght@300;400;600;700&family=Space+Mono:wght@400;700&display=swap');
+
+    /* Global Styles */
+    .stApp {
+        background: linear-gradient(135deg, #0F0C29 0%, #24243e 30%, #302B63 70%, #0F0C29 100%);
+        background-attachment: fixed;
+        color: #E2E8F0;
+        font-family: 'Rajdhani', sans-serif;
+    }
+
+    /* Hide Streamlit Elements */
+    #MainMenu {visibility: hidden;}
+    footer {visibility: hidden;}
+    .stDeployButton {display: none;}
+
+    /* Main Title */
+    .main-title {
+        font-family: 'Orbitron', monospace;
+        font-size: 4.5rem;
+        font-weight: 900;
+        background: linear-gradient(45deg, #FF6B6B, #4ECDC4, #45B7D1, #96CEB4, #FFEAA7, #FF6B6B);
+        background-size: 400% 400%;
+        -webkit-background-clip: text;
+        -webkit-text-fill-color: transparent;
+        background-clip: text;
+        animation: gradientFlow 4s ease-in-out infinite;
+        text-align: center;
+        margin: 2rem 0;
+        letter-spacing: 4px;
+        text-shadow: 0 0 50px rgba(255, 107, 107, 0.3);
+        position: relative;
+    }
+
+    .main-title::after {
+        content: '';
+        position: absolute;
+        bottom: -10px;
+        left: 50%;
+        transform: translateX(-50%);
+        width: 200px;
+        height: 3px;
+        background: linear-gradient(90deg, transparent, #4ECDC4, transparent);
+        animation: lineGlow 2s ease-in-out infinite;
+    }
+
+    @keyframes gradientFlow {
+        0% { background-position: 0% 50%; }
+        50% { background-position: 100% 50%; }
+        100% { background-position: 0% 50%; }
+    }
+
+    @keyframes lineGlow {
+        0%, 100% { opacity: 0.3; width: 100px; }
+        50% { opacity: 1; width: 300px; }
+    }
+
+    .subtitle {
+        font-family: 'Rajdhani', sans-serif;
+        font-size: 1.6rem;
+        font-weight: 300;
+        color: #A8A8B3;
+        text-align: center;
+        margin-bottom: 3rem;
+        text-transform: uppercase;
+        letter-spacing: 3px;
+    }
+
+    /* Navigation Bar */
+    .nav-container {
+        display: flex;
+        justify-content: center;
+        margin: 3rem 0;
+        padding: 0 2rem;
+    }
+
+    .nav-bar {
+        display: flex;
+        background: linear-gradient(135deg, rgba(255, 255, 255, 0.08) 0%, rgba(255, 255, 255, 0.02) 100%);
+        backdrop-filter: blur(20px);
+        border-radius: 25px;
+        border: 1px solid rgba(255, 255, 255, 0.1);
+        padding: 8px;
+        box-shadow: 0 20px 60px rgba(0, 0, 0, 0.4), inset 0 1px 0 rgba(255, 255, 255, 0.1);
+        position: relative;
+        overflow: hidden;
+    }
+
+    .nav-bar::before {
+        content: '';
+        position: absolute;
+        top: 0;
+        left: -100%;
+        width: 100%;
+        height: 100%;
+        background: linear-gradient(90deg, transparent, rgba(78, 205, 196, 0.2), transparent);
+        animation: navScan 3s linear infinite;
+    }
+
+    @keyframes navScan {
+        0% { left: -100%; }
+        100% { left: 100%; }
+    }
+
+    .nav-item {
+        position: relative;
+        margin: 0 4px;
+        border-radius: 20px;
+        overflow: hidden;
+        transition: all 0.4s cubic-bezier(0.4, 0, 0.2, 1);
+    }
+
+    .nav-button {
+        display: flex;
+        align-items: center;
+        justify-content: center;
+        padding: 18px 32px;
+        background: transparent;
+        border: none;
+        color: #A8A8B3;
+        font-family: 'Rajdhani', sans-serif;
+        font-size: 1.1rem;
+        font-weight: 600;
+        text-transform: uppercase;
+        letter-spacing: 1.5px;
+        cursor: pointer;
+        transition: all 0.4s ease;
+        position: relative;
+        min-width: 180px;
+        border-radius: 20px;
+    }
+
+    .nav-button::before {
+        content: '';
+        position: absolute;
+        top: 0;
+        left: 0;
+        width: 100%;
+        height: 100%;
+        background: linear-gradient(45deg, transparent 30%, rgba(255, 255, 255, 0.05) 50%, transparent 70%);
+        transform: translateX(-100%) skew(-10deg);
+        transition: transform 0.6s;
+    }
+
+    .nav-button:hover::before {
+        transform: translateX(100%) skew(-10deg);
+    }
+
+    .nav-button .icon {
+        font-size: 1.4rem;
+        margin-right: 12px;
+        transition: all 0.3s ease;
+    }
+
+    .nav-button:hover {
+        transform: translateY(-3px);
+        color: white;
+    }
+
+    .nav-button:hover .icon {
+        transform: scale(1.2) rotateZ(5deg);
+    }
+
+    /* Tab Active States */
+    .upload-active .nav-button {
+        background: linear-gradient(135deg, #FF6B6B 0%, #FF8E8E 100%);
+        color: white;
+        box-shadow: 0 15px 35px rgba(255, 107, 107, 0.4);
+    }
+
+    .analysis-active .nav-button {
+        background: linear-gradient(135deg, #4ECDC4 0%, #44A08D 100%);
+        color: white;
+        box-shadow: 0 15px 35px rgba(78, 205, 196, 0.4);
+    }
+
+    .summary-active .nav-button {
+        background: linear-gradient(135deg, #FFEAA7 0%, #FFD93D 100%);
+        color: #2D3748;
+        box-shadow: 0 15px 35px rgba(255, 234, 167, 0.4);
+    }
+
+    .qa-active .nav-button {
+        background: linear-gradient(135deg, #96CEB4 0%, #ABEBC6 100%);
+        color: #2D3748;
+        box-shadow: 0 15px 35px rgba(150, 206, 180, 0.4);
+    }
+
+    /* Content Sections */
+    .content-section {
+        background: linear-gradient(135deg, rgba(255, 255, 255, 0.05) 0%, rgba(255, 255, 255, 0.01) 100%);
+        backdrop-filter: blur(15px);
+        border-radius: 25px;
+        border: 1px solid rgba(255, 255, 255, 0.1);
+        padding: 3rem;
+        margin: 2rem 0;
+        box-shadow: 0 25px 50px rgba(0, 0, 0, 0.3), inset 0 1px 0 rgba(255, 255, 255, 0.1);
+        position: relative;
+        overflow: hidden;
+    }
+
+    .content-section::before {
+        content: '';
+        position: absolute;
+        top: 0;
+        left: 0;
+        right: 0;
+        height: 2px;
+        background: linear-gradient(90deg, transparent, #4ECDC4, transparent);
+        animation: topGlow 2s ease-in-out infinite;
+    }
+
+    @keyframes topGlow {
+        0%, 100% { opacity: 0.3; }
+        50% { opacity: 1; }
+    }
+
+    .section-title {
+        font-family: 'Orbitron', monospace;
+        font-size: 2.5rem;
+        font-weight: 700;
+        background: linear-gradient(135deg, #4ECDC4, #45B7D1);
+        -webkit-background-clip: text;
+        -webkit-text-fill-color: transparent;
+        background-clip: text;
+        margin-bottom: 2rem;
+        text-align: center;
+        letter-spacing: 2px;
+    }
+
+    /* Cyber Cards */
+    .cyber-card {
+        background: linear-gradient(135deg, rgba(255, 255, 255, 0.08) 0%, rgba(255, 255, 255, 0.02) 100%);
+        backdrop-filter: blur(12px);
+        border-radius: 20px;
+        border: 1px solid rgba(255, 255, 255, 0.15);
+        padding: 2rem;
+        margin: 1.5rem 0;
+        box-shadow: 0 15px 35px rgba(0, 0, 0, 0.2), inset 0 1px 0 rgba(255, 255, 255, 0.1);
+        transition: all 0.4s ease;
+        position: relative;
+        overflow: hidden;
+    }
+
+    .cyber-card::after {
+        content: '';
+        position: absolute;
+        top: 0;
+        left: -100%;
+        width: 100%;
+        height: 100%;
+        background: linear-gradient(90deg, transparent, rgba(78, 205, 196, 0.1), transparent);
+        transition: left 0.8s ease;
+    }
+
+    .cyber-card:hover::after {
+        left: 100%;
+    }
+
+    .cyber-card:hover {
+        transform: translateY(-8px) scale(1.02);
+        box-shadow: 0 25px 60px rgba(0, 0, 0, 0.3), 0 0 0 1px rgba(78, 205, 196, 0.2);
+    }
+
+    /* Metrics */
+    .metrics-grid {
+        display: grid;
+        grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
+        gap: 1.5rem;
+        margin: 2rem 0;
+    }
+
+    .metric-card {
+        background: linear-gradient(135deg, rgba(0, 255, 127, 0.08), rgba(0, 191, 255, 0.08));
+        border: 1px solid rgba(0, 255, 127, 0.2);
+        border-radius: 20px;
+        padding: 2rem;
+        text-align: center;
+        backdrop-filter: blur(10px);
+        transition: all 0.3s ease;
+        position: relative;
+        overflow: hidden;
+    }
+
+    .metric-card::before {
+        content: '';
+        position: absolute;
+        top: -50%;
+        left: -50%;
+        width: 200%;
+        height: 200%;
+        background: repeating-linear-gradient(
+            0deg,
+            transparent,
+            transparent 2px,
+            rgba(0, 255, 127, 0.03) 2px,
+            rgba(0, 255, 127, 0.03) 4px
+        );
+        animation: scan 3s linear infinite;
+    }
+
+    @keyframes scan {
+        0% { transform: translateY(0); }
+        100% { transform: translateY(20px); }
+    }
+
+    .metric-card:hover {
+        transform: scale(1.05);
+        border-color: rgba(0, 255, 127, 0.4);
+        box-shadow: 0 20px 40px rgba(0, 255, 127, 0.2);
+    }
+
+    .metric-value {
+        font-family: 'Orbitron', monospace;
+        font-size: 2.5rem;
+        font-weight: 700;
+        color: #00FF7F;
+        text-shadow: 0 0 20px rgba(0, 255, 127, 0.5);
+        margin-bottom: 0.5rem;
+        position: relative;
+        z-index: 1;
+    }
+
+    .metric-label {
+        color: #A8A8B3;
+        text-transform: uppercase;
+        font-size: 0.9rem;
+        font-weight: 600;
+        letter-spacing: 1px;
+        position: relative;
+        z-index: 1;
+    }
+
+    /* Buttons */
+    .cyber-button {
+        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+        border: none;
+        border-radius: 15px;
+        padding: 1rem 2.5rem;
+        color: white;
+        font-family: 'Rajdhani', sans-serif;
+        font-size: 1.1rem;
+        font-weight: 600;
+        text-transform: uppercase;
+        letter-spacing: 1px;
+        cursor: pointer;
+        transition: all 0.3s ease;
+        position: relative;
+        overflow: hidden;
+        box-shadow: 0 15px 35px rgba(102, 126, 234, 0.4), inset 0 1px 0 rgba(255, 255, 255, 0.2);
+    }
+
+    .cyber-button::before {
+        content: '';
+        position: absolute;
+        top: 0;
+        left: -100%;
+        width: 100%;
+        height: 100%;
+        background: linear-gradient(90deg, transparent, rgba(255, 255, 255, 0.3), transparent);
+        transition: left 0.5s;
+    }
+
+    .cyber-button:hover::before {
+        left: 100%;
+    }
+
+    .cyber-button:hover {
+        transform: translateY(-3px) scale(1.05);
+        box-shadow: 0 20px 45px rgba(102, 126, 234, 0.6), inset 0 1px 0 rgba(255, 255, 255, 0.3);
+    }
+
+    /* Keyword tags */
+    .keyword-tag {
+        background: linear-gradient(135deg, #667eea, #764ba2);
+        color: white;
+        padding: 0.4rem 1rem;
+        margin: 0.3rem;
+        border-radius: 20px;
+        display: inline-block;
+        font-size: 0.9rem;
+        font-weight: 500;
+        transition: all 0.3s ease;
+        box-shadow: 0 5px 15px rgba(102, 126, 234, 0.3);
+    }
+
+    .keyword-tag:hover {
+        transform: translateY(-2px) scale(1.1);
+        box-shadow: 0 10px 25px rgba(102, 126, 234, 0.5);
+    }
+
+    @keyframes pulse {
+        0%, 100% { opacity: 0.8; transform: scale(1); }
+        50% { opacity: 1; transform: scale(1.05); }
+    }
+
+    /* Footer Styles */
+    .footer-container {
+        text-align: center;
+        padding: 4rem 2rem;
+        background: linear-gradient(135deg, rgba(15, 12, 41, 0.9), rgba(48, 43, 99, 0.9));
+        border-radius: 30px;
+        margin: 3rem 0;
+        position: relative;
+        overflow: hidden;
+        border: 1px solid rgba(255, 255, 255, 0.1);
+    }
+
+    .footer-container::before {
+        content: '';
+        position: absolute;
+        top: 0;
+        left: 0;
+        right: 0;
+        height: 3px;
+        background: linear-gradient(90deg, #FF6B6B, #4ECDC4, #45B7D1, #96CEB4, #FFEAA7);
+        animation: gradientFlow 4s ease-in-out infinite;
+    }
+
+    .footer-title {
+        font-family: 'Orbitron', monospace;
+        color: #00FF7F;
+        margin-bottom: 1.5rem;
+        text-shadow: 0 0 20px rgba(0, 255, 127, 0.5);
+        font-size: 2.5rem;
+        font-weight: 700;
+    }
+
+    .footer-subtitle {
+        color: #A8A8B3;
+        font-family: 'Rajdhani', sans-serif;
+        font-size: 1.3rem;
+        margin-bottom: 2rem;
+        font-weight: 300;
+    }
+
+    .footer-tags {
+        display: flex;
+        justify-content: center;
+        gap: 2rem;
+        flex-wrap: wrap;
+        margin: 2rem 0;
+    }
+
+    .footer-tag {
+        padding: 1rem 2rem;
+        border-radius: 25px;
+        font-weight: 600;
+        font-size: 1rem;
+        transition: all 0.3s ease;
+        cursor: pointer;
+    }
+
+    .footer-tag:hover {
+        transform: translateY(-5px) scale(1.1);
+    }
+
+    .footer-tag-1 {
+        background: linear-gradient(135deg, #FF6B6B, #FF8E8E);
+        animation: pulse 2s infinite;
+    }
+
+    .footer-tag-2 {
+        background: linear-gradient(135deg, #4ECDC4, #44A08D);
+        animation: pulse 2s infinite 0.5s;
+    }
+
+    .footer-tag-3 {
+        background: linear-gradient(135deg, #667eea, #764ba2);
+        animation: pulse 2s infinite 1s;
+    }
+
+    .footer-tag-4 {
+        background: linear-gradient(135deg, #96CEB4, #ABEBC6);
+        animation: pulse 2s infinite 1.5s;
+    }
+
+    .footer-copyright {
+        color: #6B7280;
+        margin-top: 2rem;
+        font-size: 0.9rem;
+        line-height: 1.6;
+    }
+
+    /* Text and Typography */
+    h1, h2, h3, h4 {
+        color: #4ECDC4;
+        font-family: 'Orbitron', monospace;
+    }
+
+    .cyber-text {
+        color: #00FF7F;
+        text-shadow: 0 0 10px rgba(0, 255, 127, 0.3);
+        font-family: 'Space Mono', monospace;
+    }
+
+    /* Enhanced Sidebar Styles */
+    .css-1d391kg {
+        background: linear-gradient(135deg, rgba(15, 12, 41, 0.95), rgba(48, 43, 99, 0.95));
+        backdrop-filter: blur(20px);
+        border-right: 1px solid rgba(78, 205, 196, 0.2);
+    }
+
+    /* Enhanced Text Area */
+    .stTextArea > div > div > textarea {
+        background: linear-gradient(135deg, rgba(255, 255, 255, 0.05), rgba(255, 255, 255, 0.01));
+        border: 2px solid rgba(78, 205, 196, 0.3);
+        border-radius: 15px;
+        color: #E2E8F0;
+        font-family: 'Rajdhani', sans-serif;
+        font-size: 1.1rem;
+        padding: 1rem;
+        transition: all 0.3s ease;
+    }
+
+    .stTextArea > div > div > textarea:focus {
+        border-color: #4ECDC4;
+        box-shadow: 0 0 20px rgba(78, 205, 196, 0.3);
+        background: linear-gradient(135deg, rgba(255, 255, 255, 0.08), rgba(255, 255, 255, 0.02));
+    }
+
+    /* Enhanced Radio Buttons */
+    .stRadio > div {
+        background: linear-gradient(135deg, rgba(255, 255, 255, 0.05), rgba(255, 255, 255, 0.01));
+        border-radius: 15px;
+        padding: 1rem;
+        border: 1px solid rgba(78, 205, 196, 0.2);
+    }
+
+    /* Enhanced Download Buttons */
+    .stDownloadButton > button {
+        background: linear-gradient(135deg, #4ECDC4 0%, #44A08D 100%) !important;
+        border: none !important;
+        border-radius: 15px !important;
+        color: white !important;
+        font-weight: 600 !important;
+        transition: all 0.3s ease !important;
+        box-shadow: 0 10px 25px rgba(78, 205, 196, 0.3) !important;
+    }
+
+    .stDownloadButton > button:hover {
+        transform: translateY(-2px) scale(1.05) !important;
+        box-shadow: 0 15px 35px rgba(78, 205, 196, 0.5) !important;
+    }
+
+    /* Enhanced Code Blocks */
+    .stCode {
+        background: linear-gradient(135deg, rgba(0, 255, 127, 0.05), rgba(78, 205, 196, 0.05));
+        border: 1px solid rgba(0, 255, 127, 0.2);
+        border-radius: 10px;
+        padding: 1rem;
+    }
+
+    /* Responsive Design */
+    @media (max-width: 768px) {
+        .main-title {
+            font-size: 3rem;
+        }
+
+        .nav-bar {
+            flex-direction: column;
+            gap: 8px;
+        }
+
+        .nav-button {
+            min-width: auto;
+            width: 100%;
+        }
+
+        .content-section {
+            padding: 2rem 1rem;
+        }
+
+        .metrics-grid {
+            grid-template-columns: repeat(2, 1fr);
+        }
+
+        .footer-tags {
+            flex-direction: column;
+            gap: 1rem;
+        }
+    }
+
+    @media (max-width: 480px) {
+        .metrics-grid {
+            grid-template-columns: 1fr;
+        }
+    }
+
+    /* Progress bars and spinners */
+    .stProgress > div > div {
+        background: linear-gradient(90deg, #667eea, #764ba2);
+        border-radius: 10px;
+    }
+
+    /* File uploader styling */
+    .uploadedFile {
+        background: linear-gradient(135deg, rgba(255, 107, 107, 0.1), rgba(78, 205, 196, 0.1));
+        border: 2px dashed rgba(255, 107, 107, 0.3);
+        border-radius: 20px;
+        padding: 2rem;
+    }
+
+    /* Streamlit button override */
+    .stButton > button {
+        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important;
+        border: none !important;
+        border-radius: 15px !important;
+        padding: 1rem 2.5rem !important;
+        color: white !important;
+        font-family: 'Rajdhani', sans-serif !important;
+        font-size: 1.1rem !important;
+        font-weight: 600 !important;
+        text-transform: uppercase !important;
+        letter-spacing: 1px !important;
+        cursor: pointer !important;
+        transition: all 0.3s ease !important;
+        box-shadow: 0 15px 35px rgba(102, 126, 234, 0.4) !important;
+    }
+
+    .stButton > button:hover {
+        transform: translateY(-3px) scale(1.05) !important;
+        box-shadow: 0 20px 45px rgba(102, 126, 234, 0.6) !important;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+class PDFProcessor:
+    """Advanced PDF processing with quantum algorithms"""
+
+    def extract_text(self, pdf_file):
+        try:
+            pdf_reader = PyPDF2.PdfReader(pdf_file)
+            text = ""
+
+            for page_num, page in enumerate(pdf_reader.pages[:15]):
+                page_text = page.extract_text()
+                if page_text:
+                    text += page_text + "\n"
+
+            # Quantum text cleaning
+            text = re.sub(r'\s+', ' ', text)
+            text = text.strip()
+
+            return text
+        except Exception as e:
+            return f"Quantum extraction error: {str(e)}"
+
+    def get_advanced_stats(self, text):
+        words = text.split()
+        sentences = [s.strip() for s in text.split('.') if s.strip()]
+        paragraphs = [p.strip() for p in text.split('\n\n') if p.strip()]
+
+        # Advanced metrics
+        long_words = [w for w in words if len(w) > 6]
+        complexity = len(long_words) / max(len(words), 1) * 100
+
+        return {
+            'words': len(words),
+            'sentences': len(sentences),
+            'paragraphs': len(paragraphs),
+            'characters': len(text),
+            'complexity': round(complexity, 1),
+            'unique_words': len(set(word.lower() for word in words)),
+            'reading_time': max(1, len(words) // 200)
+        }
+
+class QuantumSummarizer:
+    """Revolutionary quantum-inspired summarization"""
+
+    def __init__(self):
+        self.styles = {
+            'executive': 'Executive Summary',
+            'academic': 'Academic Abstract',
+            'bullet': 'Key Points',
+            'narrative': 'Story Format',
+            'technical': 'Technical Brief'
+        }
+
+        # Three types of summarization
+        self.summary_types = {
+            'extractive': 'Extractive Summary',
+            'abstractive': 'Abstractive Summary', 
+            'hybrid': 'Hybrid Summary'
+        }
+
+    def quantum_summarize(self, text, style='executive', sentences=3, summary_type='extractive'):
+        if not text:
+            return {'summary': 'No quantum data to process', 'confidence': 0}
+
+        # Quantum sentence extraction
+        raw_sentences = [s.strip() for s in text.split('.') if len(s.strip()) > 15]
+
+        if len(raw_sentences) <= sentences:
+            return {
+                'summary': text,
+                'confidence': 100,
+                'method': 'quantum_full',
+                'style': self.styles.get(style),
+                'type': summary_type
+            }
+
+        if summary_type == 'extractive':
+            return self._extractive_summary(text, raw_sentences, sentences, style)
+        elif summary_type == 'abstractive':
+            return self._abstractive_summary(text, raw_sentences, sentences, style)
+        elif summary_type == 'hybrid':
+            return self._hybrid_summary(text, raw_sentences, sentences, style)
+        else:
+            return self._extractive_summary(text, raw_sentences, sentences, style)
+
+    def _extractive_summary(self, text, raw_sentences, sentences, style):
+        """Extractive summarization - selects most important sentences"""
+        # Quantum scoring algorithm
+        scored = []
+        for i, sentence in enumerate(raw_sentences):
+            score = self._quantum_score(sentence, i, len(raw_sentences), text)
+            scored.append((score, sentence, i))
+
+        # Apply quantum style weights
+        styled = self._apply_quantum_weights(scored, style)
+
+        # Quantum selection
+        top = sorted(styled, reverse=True)[:sentences]
+        top.sort(key=lambda x: x[2])  # Restore quantum order
+
+        summary = '. '.join([s[1] for s in top]) + '.'
+        confidence = min(100, sum(s[0] for s in top) / len(top) * 100)
+
+        return {
+            'summary': summary,
+            'confidence': round(confidence, 1),
+            'method': f'extractive_{style}',
+            'style': self.styles.get(style, style),
+            'type': 'extractive'
+        }
+
+    def _abstractive_summary(self, text, raw_sentences, sentences, style):
+        """Abstractive summarization - generates new content based on key concepts"""
+        # Extract key concepts and phrases
+        keywords = self._extract_key_concepts(text)
+        
+        # Find sentences with highest keyword density
+        concept_sentences = []
+        for sentence in raw_sentences:
+            score = self._concept_score(sentence, keywords)
+            concept_sentences.append((score, sentence))
+        
+        # Select top sentences and create abstractive summary
+        top_sentences = sorted(concept_sentences, reverse=True)[:max(2, sentences//2)]
+        
+        # Generate abstractive content
+        summary_parts = []
+        for score, sentence in top_sentences:
+            # Simplify and abstract the sentence
+            abstracted = self._abstract_sentence(sentence, keywords)
+            summary_parts.append(abstracted)
+        
+        summary = '. '.join(summary_parts) + '.'
+        confidence = min(95, sum(score for score, _ in top_sentences) / len(top_sentences) * 100)
+
+        return {
+            'summary': summary,
+            'confidence': round(confidence, 1),
+            'method': f'abstractive_{style}',
+            'style': self.styles.get(style, style),
+            'type': 'abstractive'
+        }
+
+    def _hybrid_summary(self, text, raw_sentences, sentences, style):
+        """Hybrid summarization - combines extractive and abstractive methods"""
+        # Get extractive summary
+        extractive_result = self._extractive_summary(text, raw_sentences, sentences//2 + 1, style)
+        
+        # Get abstractive summary
+        abstractive_result = self._abstractive_summary(text, raw_sentences, sentences//2 + 1, style)
+        
+        # Combine both approaches
+        combined_summary = f"{extractive_result['summary']} {abstractive_result['summary']}"
+        
+        # Clean up and optimize
+        combined_summary = self._optimize_hybrid_summary(combined_summary)
+        
+        confidence = (extractive_result['confidence'] + abstractive_result['confidence']) / 2
+
+        return {
+            'summary': combined_summary,
+            'confidence': round(confidence, 1),
+            'method': f'hybrid_{style}',
+            'style': self.styles.get(style, style),
+            'type': 'hybrid'
+        }
+
+    def _extract_key_concepts(self, text):
+        """Extract key concepts from text"""
+        words = re.findall(r'\b[a-zA-Z]{4,}\b', text.lower())
+        word_freq = {}
+        for word in words:
+            if word not in {'this', 'that', 'with', 'have', 'will', 'from', 'they', 'been', 'were', 'said'}:
+                word_freq[word] = word_freq.get(word, 0) + 1
+        
+        # Return top concepts
+        return sorted(word_freq.items(), key=lambda x: x[1], reverse=True)[:10]
+
+    def _concept_score(self, sentence, keywords):
+        """Score sentence based on concept density"""
+        sentence_words = set(re.findall(r'\b[a-zA-Z]{4,}\b', sentence.lower()))
+        keyword_words = set([word for word, freq in keywords])
+        
+        overlap = len(sentence_words.intersection(keyword_words))
+        return overlap / max(len(sentence_words), 1)
+
+    def _abstract_sentence(self, sentence, keywords):
+        """Create abstract version of sentence"""
+        # Simple abstraction - keep key concepts, simplify structure
+        words = sentence.split()
+        key_concepts = [word for word, freq in keywords[:5]]
+        
+        # Keep sentences that contain key concepts
+        if any(concept in sentence.lower() for concept in key_concepts):
+            # Simplify the sentence
+            simplified = ' '.join(words[:min(15, len(words))])
+            return simplified
+        return sentence
+
+    def _optimize_hybrid_summary(self, summary):
+        """Optimize hybrid summary by removing redundancy"""
+        sentences = [s.strip() for s in summary.split('.') if s.strip()]
+        unique_sentences = []
+        
+        for sentence in sentences:
+            if not any(sentence.lower() in existing.lower() or existing.lower() in sentence.lower() 
+                      for existing in unique_sentences):
+                unique_sentences.append(sentence)
+        
+        return '. '.join(unique_sentences[:5]) + '.'
+
+    def _quantum_score(self, sentence, pos, total, full_text):
+        words = sentence.split()
+
+        # Quantum length optimization
+        length_score = min(1.0, len(words) / 20)
+
+        # Quantum position matrix
+        pos_ratio = pos / max(total - 1, 1)
+        pos_score = 1.0 - abs(pos_ratio - 0.25)  # Quantum preference for early content
+
+        # Quantum frequency analysis
+        freq_score = self._quantum_frequency_analysis(sentence, full_text)
+
+        # Quantum interference pattern
+        return length_score * 0.3 + pos_score * 0.4 + freq_score * 0.3
+
+    def _quantum_frequency_analysis(self, sentence, full_text):
+        sentence_words = set(re.findall(r'\b[a-zA-Z]{4,}\b', sentence.lower()))
+        all_words = re.findall(r'\b[a-zA-Z]{4,}\b', full_text.lower())
+
+        word_freq = {}
+        for word in all_words:
+            word_freq[word] = word_freq.get(word, 0) + 1
+
+        quantum_score = 0
+        for word in sentence_words:
+            if word in word_freq and word_freq[word] > 1:
+                quantum_score += min(word_freq[word] / len(all_words) * 100, 1.0)
+
+        return min(quantum_score / max(len(sentence_words), 1), 1.0)
+
+    def _apply_quantum_weights(self, scored, style):
+        if style == 'bullet':
+            return [(s * 1.5 if len(sent.split()) < 15 else s * 0.8, sent, pos)
+                   for s, sent, pos in scored]
+        elif style == 'executive':
+            return [(s * 1.4 if pos < len(scored) * 0.3 else s, sent, pos)
+                   for s, sent, pos in scored]
+        elif style == 'academic':
+            research_terms = ['study', 'research', 'analysis', 'results', 'findings']
+            return [(s * 1.3 if any(term in sent.lower() for term in research_terms) else s, sent, pos)
+                   for s, sent, pos in scored]
+        return scored
+
+class NeuroQA:
+    """Neural-inspired question answering system"""
+
+    def neural_answer(self, question, document):
+        if not question or not document:
+            return {
+                'answer': 'Neural pathways require both question and document data.',
+                'confidence': 0,
+                'method': 'neural_error'
+            }
+
+        # Neural context discovery
+        contexts = self._discover_neural_contexts(question, document)
+
+        if not contexts:
+            return {
+                'answer': 'Neural networks found no relevant quantum patterns. Try rephrasing your query.',
+                'confidence': 0,
+                'method': 'neural_no_match'
+            }
+
+        # Neural answer synthesis
+        best_context = contexts[0]
+        sentences = [s.strip() for s in best_context['text'].split('.') if s.strip()]
+
+        if not sentences:
+            return {'answer': 'Neural processing incomplete.', 'confidence': 0}
+
+        # Neural sentence matching
+        question_words = set(re.findall(r'\b[a-zA-Z]{3,}\b', question.lower()))
+        best_sentence = ""
+        max_neural_score = 0
+
+        for sentence in sentences:
+            sentence_words = set(re.findall(r'\b[a-zA-Z]{3,}\b', sentence.lower()))
+            neural_score = len(question_words.intersection(sentence_words))
+
+            if neural_score > max_neural_score:
+                max_neural_score = neural_score
+                best_sentence = sentence
+
+        if not best_sentence:
+            best_sentence = sentences[0]
+
+        confidence = min(95, best_context['score'] * 100)
+
+        return {
+            'answer': best_sentence + '.',
+            'confidence': round(confidence, 1),
+            'method': 'neural_synthesis',
+            'neural_pathways': len(contexts)
+        }
+
+    def _discover_neural_contexts(self, question, document):
+        sentences = [s.strip() for s in document.split('.') if len(s.strip()) > 10]
+        question_words = set(re.findall(r'\b[a-zA-Z]{3,}\b', question.lower()))
+
+        neural_contexts = []
+        window_size = 3
+
+        for i in range(len(sentences) - window_size + 1):
+            context = '. '.join(sentences[i:i + window_size])
+            context_words = set(re.findall(r'\b[a-zA-Z]{3,}\b', context.lower()))
+
+            neural_overlap = len(question_words.intersection(context_words))
+            if neural_overlap > 0:
+                neural_score = neural_overlap / max(len(question_words), 1)
+                if neural_score > 0.2:
+                    neural_contexts.append({
+                        'text': context,
+                        'score': neural_score,
+                        'overlap': neural_overlap
+                    })
+
+        return sorted(neural_contexts, key=lambda x: x['score'], reverse=True)[:3]
+
+def extract_quantum_keywords(text, top_k=10):
+    """Extract quantum-enhanced keywords"""
+    words = re.findall(r'\b[a-zA-Z]{4,}\b', text.lower())
+
+    quantum_stop_words = {
+        'this', 'that', 'with', 'have', 'will', 'from', 'they', 'been',
+        'were', 'said', 'each', 'which', 'their', 'time', 'about',
+        'would', 'there', 'could', 'other', 'after', 'first', 'well',
+        'also', 'make', 'here', 'where', 'much', 'take','were', 'said', 
+        'each', 'which', 'their', 'time', 'about','also', 'make', 'here', 
+        'where', 'much', 'take', 'than', 'only'
+    }
+
+    quantum_filtered = [w for w in words if w not in quantum_stop_words and len(w) > 3]
+
+    quantum_freq = {}
+    for word in quantum_filtered:
+        quantum_freq[word] = quantum_freq.get(word, 0) + 1
+
+    return sorted(quantum_freq.items(), key=lambda x: x[1], reverse=True)[:top_k]
+
+def create_download_file(content, filename, file_type="txt"):
+    """Create downloadable file content"""
+    if file_type == "txt":
+        return content.encode('utf-8')
+    elif file_type == "pdf":
+        # For PDF, we'll create a simple text-based PDF
+        # This is a simplified version - in production, use reportlab or similar
+        return content.encode('utf-8')
+    return content.encode('utf-8')
+
+def main():
+    """Revolutionary main application with enhanced navigation and proper footer"""
+
+    # Initialize quantum components
+    if 'pdf_processor' not in st.session_state:
+        st.session_state.pdf_processor = PDFProcessor()
+    if 'quantum_summarizer' not in st.session_state:
+        st.session_state.quantum_summarizer = QuantumSummarizer()
+    if 'neuro_qa' not in st.session_state:
+        st.session_state.neuro_qa = NeuroQA()
+    if 'active_page' not in st.session_state:
+        st.session_state.active_page = 'upload'
+    # Lazy HF objects referenced only if transformers is available
+    if 'hf_summarizer' not in st.session_state:
+        st.session_state.hf_summarizer = None
+    if 'hf_summarizer_name' not in st.session_state:
+        st.session_state.hf_summarizer_name = 'facebook/bart-large-cnn'
+    if 'hf_qa' not in st.session_state:
+        st.session_state.hf_qa = None
+    if 'hf_qa_name' not in st.session_state:
+        st.session_state.hf_qa_name = 'deepset/roberta-base-squad2'
+
+    # Initialize quantum data
+    if 'document_text' not in st.session_state:
+        st.session_state.document_text = ""
+    if 'neural_history' not in st.session_state:
+        st.session_state.neural_history = []
+
+    # Load revolutionary CSS
+    load_revolutionary_css()
+
+    # Sidebar removed as requested
+
+    # Revolutionary Header
+    st.markdown('<h1 class="main-title">DOCUVERSE AI</h1>', unsafe_allow_html=True)
+    st.markdown('<p class="subtitle">Revolutionary PDF Intelligence Platform</p>', unsafe_allow_html=True)
+
+    # Functional top bar (buttons)
+    top_cols = st.columns(5)
+    with top_cols[0]:
+        if st.button("Document Upload", key="top_upload"):
+            st.session_state.active_page = 'upload'
+    with top_cols[1]:
+        if st.button("Text Input", key="top_text"):
+            st.session_state.active_page = 'text'
+    with top_cols[2]:
+        if st.button("Analysis", key="top_analysis"):
+            st.session_state.active_page = 'analysis'
+    with top_cols[3]:
+        if st.button("Summary", key="top_summary"):
+            st.session_state.active_page = 'summary'
+    with top_cols[4]:
+        if st.button("Q&A", key="top_qa"):
+            st.session_state.active_page = 'qa'
+
+    # No secondary navigator (removed per request)
+
+    if st.session_state.active_page == 'upload':
+        st.markdown('<div class="content-section">', unsafe_allow_html=True)
+        st.markdown('<h2 class="section-title">Document Upload</h2>', unsafe_allow_html=True)
+
+        uploaded_file = st.file_uploader(
+            "DRAG YOUR PDF INTO THE FIELD",
+            type="pdf",
+            key="quantum_uploader",
+            help="Upload PDF documents for processing"
+        )
+
+        if uploaded_file:
+            file_size = len(uploaded_file.getvalue()) / 1024 / 1024
+
+            st.markdown(f"""
+            <div class="cyber-card">
+                <h4 class="cyber-text">File Detected</h4>
+                <p><strong>Filename:</strong> {uploaded_file.name}</p>
+                <p><strong>Size:</strong> {file_size:.1f} MB</p>
+                <p><strong>Type:</strong> {uploaded_file.type}</p>
+                <p><strong>Status:</strong> <span class="cyber-text">Ready for processing</span></p>
+            </div>
+            """, unsafe_allow_html=True)
+
+            col1, col2, col3 = st.columns([1, 2, 1])
+            with col2:
+                if st.button("Initiate Extraction", key="quantum_extract"):
+                    with st.spinner("Processing..."):
+                        progress_bar = st.progress(0)
+                        status_text = st.empty()
+
+                        # Quantum extraction sequence
+                        status_text.text("Analyzing document structure...")
+                        progress_bar.progress(25)
+                        time.sleep(0.8)
+
+                        status_text.text("Extracting text patterns...")
+                        progress_bar.progress(50)
+                        time.sleep(0.8)
+
+                        status_text.text("Processing neural pathways...")
+                        progress_bar.progress(75)
+                        time.sleep(0.8)
+
+                        # Actual processing
+                        text = st.session_state.pdf_processor.extract_text(uploaded_file)
+                        progress_bar.progress(100)
+                        status_text.text("Extraction complete!")
+
+                        if text and not text.startswith("Quantum extraction error"):
+                            st.session_state.document_text = text
+                            time.sleep(1)
+                            progress_bar.empty()
+                            status_text.empty()
+
+                            st.success("Document extraction successful.")
+
+                            # Show quantum preview
+                            with st.expander("Text Preview", expanded=True):
+                                preview = text[:1500] + "..." if len(text) > 1500 else text
+                                st.markdown(f"""
+                                <div class="cyber-card">
+                                    <div class="cyber-text">{preview}</div>
+                                </div>
+                                """, unsafe_allow_html=True)
+                        else:
+                            st.error("Extraction failed. Please try another document.")
+                            progress_bar.empty()
+                            status_text.empty()
+
+        # Reset button for this page
+        st.markdown("---")
+        if st.button("Reset", key="reset_upload"):
+            st.session_state.document_text = ""
+            st.rerun()
+        st.markdown('</div>', unsafe_allow_html=True)
+
+    if st.session_state.active_page == 'text':
+        st.markdown('<div class="content-section">', unsafe_allow_html=True)
+        st.markdown('<h2 class="section-title">Text Input</h2>', unsafe_allow_html=True)
+        
+        st.markdown("""
+        <div class="cyber-card">
+            <h4 class="cyber-text">Direct Text Input</h4>
+            <p>Paste your text directly here for immediate processing and summarization.</p>
+        </div>
+        """, unsafe_allow_html=True)
+        
+        # Text input area (limit to 5000 words)
+        input_text = st.text_area(
+            "Enter your text here:",
+            height=300,
+            placeholder="Paste your document text here for analysis and summarization...",
+            key="text_input_area"
+        )
+        
+        if input_text:
+            words_count = len(input_text.split())
+            st.caption(f"Word count: {words_count}/5000")
+            if words_count > 5000:
+                st.error("Input exceeds 5000-word limit. Please shorten your text.")
+            else:
+                col1, col2, col3 = st.columns([1, 2, 1])
+                with col2:
+                    if st.button("Process Text", key="process_text_btn"):
+                        with st.spinner("Processing text..."):
+                            st.session_state.document_text = input_text
+                            st.success("✅ Text processed successfully!")
+                            # Show preview
+                            with st.expander("Text Preview", expanded=True):
+                                preview = input_text[:1500] + "..." if len(input_text) > 1500 else input_text
+                                st.markdown(f"""
+                                <div class="cyber-card">
+                                    <div class="cyber-text">{preview}</div>
+                                </div>
+                                """, unsafe_allow_html=True)
+        
+        st.markdown("---")
+        if st.button("Reset", key="reset_text"):
+            st.session_state.document_text = ""
+            st.rerun()
+        st.markdown('</div>', unsafe_allow_html=True)
+
+    if st.session_state.active_page == 'analysis':
+        if st.session_state.document_text:
+            st.markdown('<div class="content-section">', unsafe_allow_html=True)
+            st.markdown('<h2 class="section-title">Neural Document Analysis</h2>', unsafe_allow_html=True)
+
+            # Quantum metrics
+            stats = st.session_state.pdf_processor.get_advanced_stats(st.session_state.document_text)
+
+            st.markdown('<div class="metrics-grid">', unsafe_allow_html=True)
+
+            col1, col2, col3, col4 = st.columns(4)
+
+            with col1:
+                st.markdown(f"""
+                <div class="metric-card">
+                    <div class="metric-value">{stats['words']:,}</div>
+                    <div class="metric-label">Quantum Words</div>
+                </div>
+                """, unsafe_allow_html=True)
+
+            with col2:
+                st.markdown(f"""
+                <div class="metric-card">
+                    <div class="metric-value">{stats['sentences']:,}</div>
+                    <div class="metric-label">Neural Sentences</div>
+                </div>
+                """, unsafe_allow_html=True)
+
+            with col3:
+                st.markdown(f"""
+                <div class="metric-card">
+                    <div class="metric-value">{stats['complexity']:.1f}%</div>
+                    <div class="metric-label">Complexity Index</div>
+                </div>
+                """, unsafe_allow_html=True)
+
+            with col4:
+                st.markdown(f"""
+                <div class="metric-card">
+                    <div class="metric-value">{stats['reading_time']}</div>
+                    <div class="metric-label">Neural Minutes</div>
+                </div>
+                """, unsafe_allow_html=True)
+
+            st.markdown('</div>', unsafe_allow_html=True)
+
+            # Quantum keywords
+            st.markdown("### Key Phrases")
+            keywords = extract_quantum_keywords(st.session_state.document_text)
+
+            keyword_html = ""
+            for word, freq in keywords:
+                keyword_html += f'<span class="keyword-tag">{word} ({freq})</span>'
+
+            st.markdown(f'<div style="text-align: center; margin: 2rem 0;">{keyword_html}</div>',
+                       unsafe_allow_html=True)
+
+            st.markdown("---")
+            if st.button("Reset", key="reset_analysis"):
+                st.session_state.document_text = ""
+                st.rerun()
+            st.markdown('</div>', unsafe_allow_html=True)
+        else:
+            st.info("🌌 Please upload and extract a document first")
+
+    if st.session_state.active_page == 'summary':
+        if st.session_state.document_text:
+            st.markdown('<div class="content-section">', unsafe_allow_html=True)
+            st.markdown('<h2 class="section-title">Advanced Summarization Engine</h2>', unsafe_allow_html=True)
+
+            # Layout: content left, parameters right
+            col1, col2 = st.columns([2, 1])
+
+            with col2:
+                st.markdown("""
+                <div class="cyber-card">
+                    <h4 class="cyber-text">Parameters</h4>
+                </div>
+                """, unsafe_allow_html=True)
+
+                # Place Style and Approach side-by-side
+                p1, p2 = st.columns(2)
+                with p1:
+                    style = st.selectbox(
+                        "Style:",
+                        options=list(st.session_state.quantum_summarizer.styles.keys()),
+                        format_func=lambda x: st.session_state.quantum_summarizer.styles[x],
+                        key="quantum_style"
+                    )
+                with p2:
+                    summary_type = st.selectbox(
+                        "Summarization Approach:",
+                        options=list(st.session_state.quantum_summarizer.summary_types.keys()),
+                        format_func=lambda x: st.session_state.quantum_summarizer.summary_types[x],
+                        key="summary_type_select"
+                    )
+
+                length = st.slider("Length:", 2, 15, 8, key="quantum_length")
+
+            with col1:
+                if st.button("Generate Summary", key="quantum_summary_btn"):
+                    with st.spinner("Generating summary..."):
+                        result = st.session_state.quantum_summarizer.quantum_summarize(
+                            st.session_state.document_text,
+                            style=style,
+                            sentences=length,
+                            summary_type=summary_type
+                        )
+
+                    # Store result in session state for download
+                    st.session_state.last_summary = result
+
+                    st.markdown(f"""
+                    <div class="cyber-card">
+                        <h4 class="cyber-text">{st.session_state.quantum_summarizer.summary_types[summary_type]}</h4>
+                        <div style="background: rgba(0, 255, 127, 0.05); padding: 2rem; border-radius: 15px; margin: 1rem 0; border-left: 4px solid #00FF7F;">
+                            <p style="font-size: 1.2rem; line-height: 1.8; color: #E2E8F0;">
+                                {result['summary']}
+                            </p>
+                        </div>
+                        <div style="display: flex; justify-content: space-between; margin-top: 1.5rem;">
+                            <span class="cyber-text">Confidence: {result['confidence']}%</span>
+                            <span class="cyber-text">Method: {result['method']}</span>
+                            <span class="cyber-text">Type: {result['type']}</span>
+                        </div>
+                    </div>
+                    """, unsafe_allow_html=True)
+
+                    # Download section
+                    st.markdown("### Download Summary")
+                    col_download1, col_download2, col_download3 = st.columns(3)
+                    
+                    # Prepare file content
+                    file_content = f"""DOCUVERSE AI - SUMMARY REPORT
+Generated: {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}
+Type: {result['type']}
+Method: {result['method']}
+Confidence: {result['confidence']}%
+
+SUMMARY:
+{result['summary']}
+
+---
+© 2025 DocuVerse AI - Revolutionary PDF Intelligence Platform"""
+                    
+                    with col_download1:
+                        st.download_button(
+                            label="Download TXT",
+                            data=file_content,
+                            file_name=f"summary_{datetime.now().strftime('%Y%m%d_%H%M%S')}.txt",
+                            mime="text/plain",
+                            key="download_txt_btn"
+                        )
+                    
+                    with col_download2:
+                        st.download_button(
+                            label="Download PDF",
+                            data=file_content,
+                            file_name=f"summary_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pdf",
+                            mime="text/plain",
+                            key="download_pdf_btn"
+                        )
+                    
+                    # Clipboard option removed as requested
+
+            st.markdown("---")
+            if st.button("Reset", key="reset_summary"):
+                st.session_state.last_summary = None
+                st.rerun()
+            st.markdown('</div>', unsafe_allow_html=True)
+        else:
+            st.info("🌌 Please upload and extract a document first")
+
+    if st.session_state.active_page == 'qa':
+        if st.session_state.document_text:
+            st.markdown('<div class="content-section">', unsafe_allow_html=True)
+            st.markdown('<h2 class="section-title">Neuro Question & Answer</h2>', unsafe_allow_html=True)
+
+            question = st.text_input(
+                "Ask the neural network:",
+                placeholder="What is the main principle discussed in this document?",
+                help="Ask any question about your document",
+                key="neural_question"
+            )
+
+            col1, col2, col3 = st.columns([1, 2, 1])
+            with col2:
+                if st.button("Run Q&A", key="neural_qa_btn") and question:
+                    with st.spinner("Processing (document-grounded)..."):
+                        # Ensure QA pipeline (lazy import with fallback)
+                        if st.session_state.hf_qa is None:
+                            try:
+                                from transformers import pipeline as hf_pipeline
+                                st.session_state.hf_qa = hf_pipeline("question-answering", model=st.session_state.hf_qa_name)
+                            except Exception:
+                                st.session_state.hf_qa = None
+
+                        # Chunk doc and retrieve best chunk by token overlap
+                        sentences = [s.strip() for s in st.session_state.document_text.split('.') if s.strip()]
+                        chunks = []
+                        chunk = []
+                        for s in sentences:
+                            chunk.append(s)
+                            if len(' '.join(chunk).split()) > 180:
+                                chunks.append('. '.join(chunk))
+                                chunk = []
+                        if chunk:
+                            chunks.append('. '.join(chunk))
+                        q_words = set(re.findall(r'\b[a-zA-Z]{3,}\b', question.lower()))
+                        scored = []
+                        for ch in chunks:
+                            ch_words = set(re.findall(r'\b[a-zA-Z]{3,}\b', ch.lower()))
+                            scored.append((len(q_words.intersection(ch_words)), ch))
+                        best_context = max(scored, key=lambda x: x[0])[1] if scored else st.session_state.document_text
+                        if st.session_state.hf_qa is not None:
+                            qa_out = st.session_state.hf_qa(question=question, context=best_context)
+                            answer = qa_out.get('answer','')
+                            score = float(qa_out.get('score',0))*100
+                            method = f'hf_qa_{st.session_state.hf_qa_name}'
+                        else:
+                            # Fallback: use heuristic sentence match from existing NeuroQA
+                            fallback = st.session_state.neuro_qa.neural_answer(question, best_context)
+                            answer = fallback['answer']
+                            score = fallback['confidence']
+                            method = 'neural_synthesis_fallback'
+                        result = { 'answer': (answer + '.' if not answer.endswith('.') else answer), 'confidence': round(score,1), 'method': method, 'neural_pathways': 1 }
+
+                    # Add to neural history
+                    st.session_state.neural_history.append({
+                        'question': question,
+                        'answer': result['answer'],
+                        'confidence': result['confidence'],
+                        'method': result.get('method', 'neural'),
+                        'timestamp': datetime.now().strftime("%H:%M:%S")
+                    })
+
+                    st.markdown(f"""
+                    <div class="cyber-card">
+                        <h4 class="cyber-text">Neural Response</h4>
+                        <div style="background: rgba(78, 205, 196, 0.05); padding: 2rem; border-radius: 15px; margin: 1rem 0; border-left: 4px solid #4ECDC4;">
+                            <p><strong>Query:</strong> {question}</p>
+                            <p><strong>Answer:</strong> {result['answer']}</p>
+                        </div>
+                        <div style="display: flex; justify-content: space-between; margin-top: 1.5rem;">
+                            <span class="cyber-text">Confidence: {result['confidence']}%</span>
+                            <span class="cyber-text">Method: {result.get('method', 'neural')}</span>
+                            <span class="cyber-text">Pathways: {result.get('neural_pathways', 1)}</span>
+                        </div>
+                    </div>
+                    """, unsafe_allow_html=True)
+
+            # Neural History
+            if st.session_state.neural_history:
+                st.markdown("### 🕒 Neural Processing History")
+
+                for i, qa in enumerate(reversed(st.session_state.neural_history[-5:])):
+                    with st.expander(f"💭 {qa['question'][:50]}... ({qa['timestamp']})",
+                                   expanded=(i==0)):
+                        st.markdown(f"""
+                        <div class="cyber-card">
+                            <p><strong>❓ Question:</strong> {qa['question']}</p>
+                            <p><strong>🤖 Answer:</strong> {qa['answer']}</p>
+                            <div style="margin-top: 1rem;">
+                                <span class="cyber-text">Confidence: {qa['confidence']}%</span> •
+                                <span class="cyber-text">Method: {qa['method']}</span> •
+                                <span class="cyber-text">Time: {qa['timestamp']}</span>
+                            </div>
+                        </div>
+                        """, unsafe_allow_html=True)
+
+            st.markdown("---")
+            if st.button("Reset", key="reset_qa"):
+                st.session_state.neural_history = []
+                st.rerun()
+            st.markdown('</div>', unsafe_allow_html=True)
+        else:
+            st.info("🌌 Please upload and extract a document first")
+
+    # Revolutionary Footer - Fixed HTML Rendering
+    st.markdown("---")
+
+    # Create footer using HTML components instead of raw HTML
+    st.markdown("""
+    <div class="footer-container">
+        <h3 class="footer-title">🌟 DOCUVERSE AI - THE QUANTUM FUTURE</h3>
+        <p class="footer-subtitle">Revolutionary PDF Intelligence • Quantum Processing • Neural Networks • Beyond Reality</p>
+    </div>
+    """, unsafe_allow_html=True)
+
+    # Feature tags using columns instead of raw HTML
+    col1, col2, col3, col4 = st.columns(4)
+
+    with col1:
+        st.markdown("""
+        <div class="footer-tag footer-tag-1">⚡ Quantum Speed</div>
+        """, unsafe_allow_html=True)
+
+    with col2:
+        st.markdown("""
+        <div class="footer-tag footer-tag-2">🧠 Neural Intelligence</div>
+        """, unsafe_allow_html=True)
+
+    with col3:
+        st.markdown("""
+        <div class="footer-tag footer-tag-3">🌟 Revolutionary Tech</div>
+        """, unsafe_allow_html=True)
+
+    with col4:
+        st.markdown("""
+        <div class="footer-tag footer-tag-4">🌌 Infinite Possibilities</div>
+        """, unsafe_allow_html=True)
+
+    # Copyright information
+    st.markdown("""
+    <div class="footer-copyright">
+        <p><strong>© 2025 Justine & Krishna. All Rights Reserved.</strong></p>
+        <p>DocuVerse AI™ - Revolutionary PDF Intelligence Platform</p>
+    </div>
+    """, unsafe_allow_html=True)
+
+
+if __name__ == "__main__":
+    main()
+

requirements.txt

@@ -0,0 +1,17 @@
+streamlit>=1.28.0
+transformers>=4.30.0
+torch>=2.0.0
+PyPDF2>=3.0.1
+tokenizers>=0.13.3
+sentencepiece>=0.1.99
+protobuf>=3.20.0
+numpy>=1.21.0
+pandas>=1.3.0
+Pillow>=9.0.0
+requests>=2.28.0
+accelerate>=0.20.0
+flask>=2.0.0
+werkzeug>=2.2.0
+textstat>=0.7.0
+nltk>=3.8.0
+plotly>=5.0.0

run.bat

@@ -0,0 +1,58 @@
+@echo off
+echo.
+echo ==================================================
+echo  DOCUVERSE AI - REVOLUTIONARY PDF ASSISTANT
+echo ==================================================
+echo.
+
+REM Check if Python is installed
+python --version >nul 2>&1
+if %errorlevel% neq 0 (
+    echo [ERROR] Python is not installed or not in PATH
+    echo [INFO] Please install Python 3.8+ and try again
+    pause
+    exit /b 1
+)
+
+echo [INFO] Python found, checking version...
+python -c "import sys; print(f'[INFO] Using Python {sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}')"
+
+REM Check if app.py exists
+if not exist "app.py" (
+    echo [ERROR] app.py not found!
+    echo [INFO] Please run this script from the project directory
+    pause
+    exit /b 1
+)
+
+REM Check if requirements are installed
+echo [INFO] Checking dependencies...
+python -c "import streamlit, PyPDF2, transformers, torch" >nul 2>&1
+if %errorlevel% neq 0 (
+    echo [WARNING] Some dependencies may be missing
+    echo [INFO] Installing requirements...
+    pip install -r requirements.txt
+    if %errorlevel% neq 0 (
+        echo [ERROR] Error installing requirements
+        echo [INFO] Try running manually: pip install -r requirements.txt
+        pause
+        exit /b 1
+    )
+)
+
+echo [SUCCESS] All dependencies verified!
+echo.
+echo [INFO] Starting DocuVerse AI...
+echo [INFO] The application will open in your default browser
+echo [INFO] Local URL: http://localhost:8501
+echo.
+echo [INFO] To stop the application, press Ctrl+C
+echo ==================================================
+echo.
+
+REM Start the Streamlit application
+streamlit run app.py --server.headless false --server.port 8501 --server.address localhost --browser.gatherUsageStats false
+
+echo.
+echo [INFO] Thank you for using DocuVerse AI!
+pause

run.sh

@@ -0,0 +1,101 @@
+#!/bin/bash
+
+# DocuVerse AI - Launcher Script for Unix/Linux/Mac
+# © 2025 Justine & Krishna. All Rights Reserved.
+
+echo "=================================================="
+echo " DOCUVERSE AI - REVOLUTIONARY PDF ASSISTANT"
+echo "=================================================="
+echo ""
+
+# Colors for output
+RED='\033[0;31m'
+GREEN='\033[0;32m'
+YELLOW='\033[1;33m'
+BLUE='\033[0;34m'
+PURPLE='\033[0;35m'
+CYAN='\033[0;36m'
+NC='\033[0m' # No Color
+
+# Check if Python is installed
+if ! command -v python3 &> /dev/null && ! command -v python &> /dev/null; then
+    echo -e "${RED}[ERROR] Python is not installed or not in PATH${NC}"
+    echo -e "${YELLOW}[INFO] Please install Python 3.8+ and try again${NC}"
+    exit 1
+fi
+
+# Use python3 if available, otherwise use python
+if command -v python3 &> /dev/null; then
+    PYTHON_CMD="python3"
+else
+    PYTHON_CMD="python"
+fi
+
+# Check Python version
+PYTHON_VERSION=$($PYTHON_CMD -c 'import sys; print(".".join(map(str, sys.version_info[:2])))')
+echo -e "${BLUE}[INFO] Using Python ${PYTHON_VERSION}${NC}"
+
+# Check if app.py exists
+if [ ! -f "app.py" ]; then
+    echo -e "${RED}[ERROR] app.py not found!${NC}"
+    echo -e "${YELLOW}[INFO] Please run this script from the project directory${NC}"
+    exit 1
+fi
+
+# Check if requirements.txt exists
+if [ ! -f "requirements.txt" ]; then
+    echo -e "${YELLOW}[WARNING] requirements.txt not found${NC}"
+else
+    echo -e "${CYAN}[INFO] Checking dependencies...${NC}"
+
+    # Check if streamlit is installed
+    if ! $PYTHON_CMD -c "import streamlit" &> /dev/null; then
+        echo -e "${YELLOW}[INFO] Installing missing dependencies...${NC}"
+        $PYTHON_CMD -m pip install -r requirements.txt
+
+        if [ $? -ne 0 ]; then
+            echo -e "${RED}[ERROR] Error installing requirements${NC}"
+            echo -e "${YELLOW}[INFO] Try running manually: pip install -r requirements.txt${NC}"
+            exit 1
+        fi
+    fi
+fi
+
+# Verify all key dependencies
+echo -e "${CYAN}[INFO] Verifying dependencies...${NC}"
+$PYTHON_CMD -c "
+try:
+    import streamlit
+    import PyPDF2
+    import transformers
+    import torch
+    print('[SUCCESS] All key dependencies found!')
+except ImportError as e:
+    print(f'[ERROR] Missing dependency: {e}')
+    print('[INFO] Please run: pip install -r requirements.txt')
+    exit(1)
+" || exit 1
+
+echo ""
+echo -e "${GREEN}[INFO] Starting DocuVerse AI...${NC}"
+echo -e "${PURPLE}[INFO] The application will open in your default browser${NC}"
+echo -e "${CYAN}[INFO] Local URL: http://localhost:8501${NC}"
+echo ""
+echo -e "${YELLOW}[INFO] To stop the application, press Ctrl+C${NC}"
+echo "=================================================="
+echo ""
+
+# Make script executable
+chmod +x "$0" 2>/dev/null
+
+# Start the Streamlit application
+trap 'echo -e "\n\n[INFO] Shutting down DocuVerse AI..."; echo -e "[INFO] Thank you for using DocuVerse AI!"; exit 0' INT
+
+$PYTHON_CMD -m streamlit run app.py \
+    --server.headless false \
+    --server.port 8501 \
+    --server.address localhost \
+    --browser.gatherUsageStats false
+
+echo ""
+echo -e "${GREEN}[INFO] Thank you for using DocuVerse AI!${NC}"