#!/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("""
""", 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('DOCUVERSE AI
', unsafe_allow_html=True)
st.markdown('Revolutionary PDF Intelligence Platform
', 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('', unsafe_allow_html=True)
st.markdown('
Document Upload
', 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"""
File Detected
Filename: {uploaded_file.name}
Size: {file_size:.1f} MB
Type: {uploaded_file.type}
Status: Ready for processing
""", 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"""
""", 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('
', unsafe_allow_html=True)
st.markdown('
Text Input
', unsafe_allow_html=True)
st.markdown("""
Direct Text Input
Paste your text directly here for immediate processing and summarization.
""", 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"""
""", unsafe_allow_html=True)
st.markdown("---")
if st.button("Reset", key="reset_text"):
st.session_state.document_text = ""
st.rerun()
st.markdown('
', unsafe_allow_html=True)
st.markdown('
Neural Document Analysis
', unsafe_allow_html=True)
# Quantum metrics
stats = st.session_state.pdf_processor.get_advanced_stats(st.session_state.document_text)
st.markdown('
', unsafe_allow_html=True)
col1, col2, col3, col4 = st.columns(4)
with col1:
st.markdown(f"""
{stats['words']:,}
Quantum Words
""", unsafe_allow_html=True)
with col2:
st.markdown(f"""
{stats['sentences']:,}
Neural Sentences
""", unsafe_allow_html=True)
with col3:
st.markdown(f"""
{stats['complexity']:.1f}%
Complexity Index
""", unsafe_allow_html=True)
with col4:
st.markdown(f"""
{stats['reading_time']}
Neural Seconds
""", unsafe_allow_html=True)
st.markdown('
', 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'
{word} ({freq})'
st.markdown(f'
{keyword_html}
',
unsafe_allow_html=True)
st.markdown("---")
if st.button("Reset", key="reset_analysis"):
st.session_state.document_text = ""
st.rerun()
st.markdown('
', unsafe_allow_html=True)
st.markdown('
Advanced Summarization Engine
', 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("""
Parameters
""", 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"""
{st.session_state.quantum_summarizer.summary_types[summary_type]}
Confidence: {result['confidence']}%
Method: {result['method']}
Type: {result['type']}
""", 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('
', unsafe_allow_html=True)
st.markdown('
Neuro Question & Answer
', 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"""
Neural Response
Query: {question}
Answer: {result['answer']}
Confidence: {result['confidence']}%
Method: {result.get('method', 'neural')}
Pathways: {result.get('neural_pathways', 1)}
""", 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"""
❓ Question: {qa['question']}
🤖 Answer: {qa['answer']}
Confidence: {qa['confidence']}% •
Method: {qa['method']} •
Time: {qa['timestamp']}
""", unsafe_allow_html=True)
st.markdown("---")
if st.button("Reset", key="reset_qa"):
st.session_state.neural_history = []
st.rerun()
st.markdown('