app.py

import os
import nltk
from nltk.tokenize import sent_tokenize, word_tokenize
from nltk.corpus import stopwords
from nltk.probability import FreqDist
from flask import Flask, request, jsonify, render_template
import PyPDF2
import docx
import re
import heapq

# Download necessary NLTK data
nltk.download('punkt')
nltk.download('stopwords')

app = Flask(__name__)

class SimpleDocumentAgent:
    def __init__(self):
        """Initialize a simple document processing agent using free libraries."""
        self.current_document_text = ""
        self.document_name = ""
        self.stop_words = set(stopwords.words('english'))
    
    def load_document(self, file_path):
        """Load document from PDF or DOCX file."""
        try:
            if file_path.endswith('.pdf'):
                self.document_name = os.path.basename(file_path)
                with open(file_path, 'rb') as file:
                    pdf_reader = PyPDF2.PdfReader(file)
                    self.current_document_text = ""
                    for page_num in range(len(pdf_reader.pages)):
                        page = pdf_reader.pages[page_num]
                        self.current_document_text += page.extract_text()
            
            elif file_path.endswith('.docx'):
                self.document_name = os.path.basename(file_path)
                doc = docx.Document(file_path)
                self.current_document_text = "\n".join([para.text for para in doc.paragraphs])
            
            else:
                return "Unsupported file format. Please use PDF or DOCX."
            
            return f"Successfully loaded {self.document_name}"
        
        except Exception as e:
            return f"Error loading document: {str(e)}"
    
    def summarize_document(self, sentences_count=5):
        """Generate a summary using frequency-based extraction."""
        if not self.current_document_text:
            return "No document loaded. Please load a document first."
        
        # Tokenize the text into sentences
        sentences = sent_tokenize(self.current_document_text)
        
        # Calculate word frequencies
        words = word_tokenize(self.current_document_text.lower())
        words = [word for word in words if word.isalnum() and word not in self.stop_words]
        
        freq_dist = FreqDist(words)
        
        # Calculate sentence scores based on word frequencies
        sentence_scores = {}
        for i, sentence in enumerate(sentences):
            for word in word_tokenize(sentence.lower()):
                if word in freq_dist:
                    if i in sentence_scores:
                        sentence_scores[i] += freq_dist[word]
                    else:
                        sentence_scores[i] = freq_dist[word]
        
        # Get top sentences
        summary_sentences_indices = heapq.nlargest(sentences_count, 
                                                   sentence_scores, 
                                                   key=sentence_scores.get)
        
        # Sort the indices to preserve original order
        summary_sentences_indices.sort()
        
        # Create the summary
        summary = [sentences[i] for i in summary_sentences_indices]
        
        return " ".join(summary)
    
    def extract_information(self, info_type):
        """Extract specific information like dates, emails, or phone numbers."""
        if not self.current_document_text:
            return "No document loaded. Please load a document first."
        
        results = []
        
        if info_type.lower() == "email" or info_type.lower() == "emails":
            # Pattern for emails
            email_pattern = r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b'
            results = re.findall(email_pattern, self.current_document_text)
            
        elif info_type.lower() == "phone" or info_type.lower() == "phones" or info_type.lower() == "phone numbers":
            # Pattern for phone numbers
            phone_pattern = r'\b(\+\d{1,2}\s)?\(?\d{3}\)?[\s.-]?\d{3}[\s.-]?\d{4}\b'
            results = re.findall(phone_pattern, self.current_document_text)
            
        elif info_type.lower() == "date" or info_type.lower() == "dates":
            # Pattern for dates (simple pattern, can be improved)
            date_pattern = r'\b\d{1,2}[/.-]\d{1,2}[/.-]\d{2,4}\b'
            results = re.findall(date_pattern, self.current_document_text)
            
        elif info_type.lower() == "url" or info_type.lower() == "urls" or info_type.lower() == "website" or info_type.lower() == "websites":
            # Pattern for URLs
            url_pattern = r'https?://[^\s]+'
            results = re.findall(url_pattern, self.current_document_text)
        
        else:
            # If not a specific pattern, search for occurrences of the term
            results = [sentence for sentence in sent_tokenize(self.current_document_text) 
                      if info_type.lower() in sentence.lower()]
        
        if not results:
            return f"No {info_type} found in the document."
            
        return results
    
    def answer_question(self, question):
        """Attempt to answer questions about the document using keyword matching."""
        if not self.current_document_text:
            return "No document loaded. Please load a document first."
        
        # Tokenize the question and remove stop words
        question_words = [w.lower() for w in word_tokenize(question) 
                         if w.lower() not in self.stop_words and w.isalnum()]
        
        # Tokenize the document into sentences
        sentences = sent_tokenize(self.current_document_text)
        
        # Score sentences based on the question words they contain
        sentence_scores = {}
        for i, sentence in enumerate(sentences):
            words = [w.lower() for w in word_tokenize(sentence)]
            score = sum(1 for word in question_words if word in words)
            if score > 0:
                sentence_scores[i] = score
        
        # If no matches found
        if not sentence_scores:
            return "I couldn't find information related to your question in the document."
        
        # Get the top 3 most relevant sentences
        top_indices = heapq.nlargest(3, sentence_scores, key=sentence_scores.get)
        relevant_sentences = [sentences[i] for i in sorted(top_indices)]
        
        return " ".join(relevant_sentences)

# Set up Flask routes
@app.route('/')
def home():
    return render_template('index.html')

@app.route('/upload', methods=['POST'])
def upload_file():
    # Check if the post request has the file part
    if 'file' not in request.files:
        return jsonify({"error": "No file part"})
    
    file = request.files['file']
    if file.filename == '':
        return jsonify({"error": "No selected file"})
    
    if file:
        # Save the file temporarily
        file_path = os.path.join("temp", file.filename)
        os.makedirs("temp", exist_ok=True)
        file.save(file_path)
        
        # Process the file
        result = agent.load_document(file_path)
        
        # Remove the temporary file
        os.remove(file_path)
        
        return jsonify({"message": result})

@app.route('/summarize', methods=['POST'])
def summarize():
    sentences = request.json.get('sentences', 5)
    result = agent.summarize_document(sentences)
    return jsonify({"summary": result})

@app.route('/extract', methods=['POST'])
def extract():
    info_type = request.json.get('info_type', '')
    result = agent.extract_information(info_type)
    return jsonify({"extracted": result})

@app.route('/question', methods=['POST'])
def question():
    query = request.json.get('question', '')
    result = agent.answer_question(query)
    return jsonify({"answer": result})

# Initialize the agent
agent = SimpleDocumentAgent()

# Create a basic HTML template
@app.route('/get_index_template')
def get_index_template():
    html_content = """
    <!DOCTYPE html>
    <html>
    <head>
        <title>Document Processing Agent</title>
        <style>
            body { font-family: Arial, sans-serif; margin: 0; padding: 20px; line-height: 1.6; }
            h1 { color: #333; }
            .container { max-width: 800px; margin: 0 auto; }
            .section { margin-bottom: 20px; padding: 15px; border: 1px solid #ddd; border-radius: 5px; }
            button { background-color: #4CAF50; color: white; padding: 10px 15px; border: none; border-radius: 4px; cursor: pointer; }
            button:hover { background-color: #45a049; }
            input, select { padding: 8px; margin: 10px 0; width: 100%; }
            textarea { width: 100%; height: 150px; }
            .result { background-color: #f9f9f9; padding: 10px; border-radius: 5px; margin-top: 10px; }
        </style>
    </head>
    <body>
        <div class="container">
            <h1>Document Processing Agent</h1>
            
            <div class="section">
                <h2>Upload Document</h2>
                <form id="uploadForm">
                    <input type="file" id="documentFile" accept=".pdf,.docx">
                    <button type="submit">Upload</button>
                </form>
                <div id="uploadResult" class="result"></div>
            </div>
            
            <div class="section">
                <h2>Summarize Document</h2>
                <label for="sentenceCount">Number of sentences:</label>
                <input type="number" id="sentenceCount" value="5" min="1" max="20">
                <button onclick="summarizeDocument()">Generate Summary</button>
                <div id="summaryResult" class="result"></div>
            </div>
            
            <div class="section">
                <h2>Extract Information</h2>
                <select id="infoType">
                    <option value="email">Emails</option>
                    <option value="phone">Phone Numbers</option>
                    <option value="date">Dates</option>
                    <option value="url">URLs</option>
                </select>
                <button onclick="extractInfo()">Extract</button>
                <div id="extractResult" class="result"></div>
            </div>
            
            <div class="section">
                <h2>Ask Questions</h2>
                <input type="text" id="question" placeholder="Enter your question about the document">
                <button onclick="askQuestion()">Ask</button>
                <div id="questionResult" class="result"></div>
            </div>
        </div>

        <script>
            // Upload document
            document.getElementById('uploadForm').addEventListener('submit', function(event) {
                event.preventDefault();
                const fileInput = document.getElementById('documentFile');
                const file = fileInput.files[0];
                if (!file) {
                    alert('Please select a file to upload');
                    return;
                }
                
                const formData = new FormData();
                formData.append('file', file);
                
                fetch('/upload', {
                    method: 'POST',
                    body: formData
                })
                .then(response => response.json())
                .then(data => {
                    document.getElementById('uploadResult').textContent = data.message;
                })
                .catch(error => {
                    console.error('Error:', error);
                    document.getElementById('uploadResult').textContent = 'Error uploading file';
                });
            });
            
            // Summarize
            function summarizeDocument() {
                const sentences = document.getElementById('sentenceCount').value;
                
                fetch('/summarize', {
                    method: 'POST',
                    headers: {
                        'Content-Type': 'application/json',
                    },
                    body: JSON.stringify({ sentences: parseInt(sentences) })
                })
                .then(response => response.json())
                .then(data => {
                    document.getElementById('summaryResult').textContent = data.summary;
                })
                .catch(error => {
                    console.error('Error:', error);
                    document.getElementById('summaryResult').textContent = 'Error generating summary';
                });
            }
            
            // Extract info
            function extractInfo() {
                const infoType = document.getElementById('infoType').value;
                
                fetch('/extract', {
                    method: 'POST',
                    headers: {
                        'Content-Type': 'application/json',
                    },
                    body: JSON.stringify({ info_type: infoType })
                })
                .then(response => response.json())
                .then(data => {
                    if (Array.isArray(data.extracted)) {
                        document.getElementById('extractResult').textContent = data.extracted.join('\\n');
                    } else {
                        document.getElementById('extractResult').textContent = data.extracted;
                    }
                })
                .catch(error => {
                    console.error('Error:', error);
                    document.getElementById('extractResult').textContent = 'Error extracting information';
                });
            }
            
            // Ask question
            function askQuestion() {
                const question = document.getElementById('question').value;
                
                fetch('/question', {
                    method: 'POST',
                    headers: {
                        'Content-Type': 'application/json',
                    },
                    body: JSON.stringify({ question: question })
                })
                .then(response => response.json())
                .then(data => {
                    document.getElementById('questionResult').textContent = data.answer;
                })
                .catch(error => {
                    console.error('Error:', error);
                    document.getElementById('questionResult').textContent = 'Error processing question';
                });
            }
        </script>
    </body>
    </html>
    """
    return html_content

if __name__ == "__main__":
    # Create a templates folder and index.html
    os.makedirs("templates", exist_ok=True)
    with open("templates/index.html", "w") as f:
        f.write(get_index_template())
    
    # Run the app
    app.run(debug=True)