Spaces:
Sleeping
Sleeping
File size: 55,345 Bytes
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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, 0.5);
border-radius: 15px;
color: #E2E8F0;
font-family: 'Rajdhani', sans-serif;
font-size: 1.1rem;
padding: 1rem;
transition: all 0.3s ease;
caret-color: #4ECDC4;
}
.stTextArea > div > div > textarea:focus {
border-color: #4ECDC4;
box-shadow: 0 0 20px rgba(78, 205, 196, 0.4);
background: linear-gradient(135deg, rgba(30, 30, 30, 0.9), rgba(50, 50, 50, 0.98));
}
/* 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()
# 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'
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 Seconds</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: parameters left (stack first on mobile), content right
col_params, col_content = st.columns([1, 2])
with col_params:
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 col_content:
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"
)
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()
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