app.py

import numpy as np
from sentence_transformers import SentenceTransformer
import faiss
import re
import gradio as gr

def preprocess_text(text):
    """
    Preprocess the text into structured question-answer pairs
    """
    # Split text into sections by questions
    sections = []
    current_section = []
    
    for line in text.split('\n'):
        line = line.strip()
        if line.startswith('Question'):
            if current_section:
                sections.append(' '.join(current_section))
            current_section = [line]
        elif line:
            current_section.append(line)
    
    if current_section:
        sections.append(' '.join(current_section))
    
    # Create a structured format
    structured_sections = []
    for section in sections:
        # Remove page numbers and other irrelevant text
        section = re.sub(r'\d+\s*$', '', section)
        section = re.sub(r'TRAPS:|BEST ANSWER:|PASSABLE ANSWER:', ' ', section)
        structured_sections.append(section.strip())
    
    return structured_sections

def query_qa_system(question, model, index, text_chunks, similarity_threshold=0.4):
    """
    Query the QA system with improved matching
    """
    # Encode and normalize the question
    question_embedding = model.encode([question])
    faiss.normalize_L2(question_embedding)
    
    # Search for the most similar chunks
    k = 1  # Get only the best match
    similarities, indices = index.search(question_embedding, k)
    
    best_idx = indices[0][0]
    similarity_score = similarities[0][0]  # Cosine similarity score
    
    if similarity_score >= similarity_threshold:
        matched_text = text_chunks[best_idx]
        # Extract just the question number for reference
        question_num = re.search(r'Question \d+:', matched_text)
        question_num = question_num.group(0) if question_num else "Matching section"
        
        return {
            'question': question_num,
            'full_text': matched_text,
            'confidence': float(similarity_score),
            'found_answer': True
        }
    else:
        return {
            'question': None,
            'full_text': "I couldn't find a sufficiently relevant answer to your question in the provided document.",
            'confidence': float(similarity_score),
            'found_answer': False
        }

# Function to handle PDF file upload and initialization
def initialize_qa_system(pdf_file):
    # Read the uploaded PDF
    try:
        from PyPDF2 import PdfReader
        pdf_reader = PdfReader(pdf_file.name)
        pdf_text = ""
        for page in pdf_reader.pages:
            text = page.extract_text()
            if text:
                pdf_text += text + "\n"
        
        # Process text and create embeddings
        text_chunks = preprocess_text(pdf_text)
        model = SentenceTransformer("all-MiniLM-L6-v2")
        embeddings = model.encode(text_chunks)
        
        # Create index
        dimension = embeddings.shape[1]
        faiss.normalize_L2(embeddings)
        index = faiss.IndexFlatIP(dimension)
        index.add(embeddings)
        
        return {
            'model': model,
            'index': index,
            'text_chunks': text_chunks,
            'status': f"System initialized with {len(text_chunks)} text chunks from your PDF!"
        }
    except Exception as e:
        return {
            'model': None,
            'index': None,
            'text_chunks': None,
            'status': f"Error: {str(e)}"
        }

# Global variables to store our QA system components
qa_system = {'model': None, 'index': None, 'text_chunks': None}

# Function to handle file upload
def upload_file(pdf_file):
    global qa_system
    result = initialize_qa_system(pdf_file)
    qa_system = result
    return result['status']

# Function to handle questions
def answer_question(question):
    global qa_system
    
    if not qa_system['model'] or not qa_system['index'] or not qa_system['text_chunks']:
        return "Please upload a PDF file first."
    
    result = query_qa_system(question, qa_system['model'], qa_system['index'], qa_system['text_chunks'])
    answer_start = result['full_text'].find('Answer:') + len('Answer:')
    answer = result['full_text'][answer_start:].strip()

    
    if result['found_answer']:
        return f"Match (confidence: {result['confidence']:.2f}):\n\n{answer}"
    else:
        return f"{answer}\nBest match confidence: {result['confidence']:.2f}"

# Create the Gradio interface
with gr.Blocks(title="Interview Q&A Assistant") as demo:
    gr.Markdown("# Interview Q&A Assistant")
    gr.Markdown("Upload your interview questions PDF and ask questions to get the most relevant sections.")
    
    with gr.Row():
        with gr.Column():
            pdf_upload = gr.File(label="Upload PDF File")
            upload_button = gr.Button("Initialize Q&A System")
            status_text = gr.Textbox(label="Status", value="Upload a PDF to begin")
        
    with gr.Row():
        with gr.Column():
            question_input = gr.Textbox(label="Ask a question about interview preparation")
            submit_button = gr.Button("Get Answer")
        
    with gr.Row():
        answer_output = gr.Textbox(label="Answer", lines=10)
    
    # Set up events
    upload_button.click(upload_file, inputs=pdf_upload, outputs=status_text)
    submit_button.click(answer_question, inputs=question_input, outputs=answer_output)

# Launch the app
if __name__ == "__main__":
    demo.launch(share=True)