app.py

# Import necessary libraries
import gradio as gr
import time
import logging
import os
import re
from datetime import datetime
import numpy as np
import pandas as pd
from sentence_transformers import SentenceTransformer, util
import faiss
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import PyPDF2
import io

# Set up logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s',
    handlers=[logging.StreamHandler()]
)
logger = logging.getLogger('Vision2030Assistant')

# Check for GPU availability
has_gpu = torch.cuda.is_available()
logger.info(f"GPU available: {has_gpu}")

# Define the Vision2030Assistant class
class Vision2030Assistant:
    def __init__(self):
        """Initialize the Vision 2030 Assistant with models, knowledge base, and indices."""
        logger.info("Initializing Vision 2030 Assistant...")
        self.load_embedding_models()
        self.load_language_model()
        self._create_knowledge_base()
        self._create_indices()
        self._create_sample_eval_data()
        self.metrics = {"response_times": [], "user_ratings": [], "factual_accuracy": []}
        self.session_history = {}  # Dictionary to store session history
        self.has_pdf_content = False  # Flag to indicate if PDF content is available
        logger.info("Assistant initialized successfully")

    def load_embedding_models(self):
        """Load Arabic and English embedding models with fallback mechanism."""
        try:
            self.arabic_embedder = SentenceTransformer('CAMeL-Lab/bert-base-arabic-camelbert-ca')
            self.english_embedder = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
            if has_gpu:
                self.arabic_embedder = self.arabic_embedder.to('cuda')
                self.english_embedder = self.english_embedder.to('cuda')
            logger.info("Embedding models loaded successfully")
        except Exception as e:
            logger.error(f"Failed to load embedding models: {e}")
            self._fallback_embedding()

    def _fallback_embedding(self):
        """Fallback method for embedding models using a simple random vector approach."""
        logger.warning("Using fallback embedding method")
        class SimpleEmbedder:
            def encode(self, text):
                import hashlib
                hash_obj = hashlib.md5(text.encode())
                np.random.seed(int(hash_obj.hexdigest(), 16) % 2**32)
                return np.random.randn(384).astype(np.float32)
        self.arabic_embedder = SimpleEmbedder()
        self.english_embedder = SimpleEmbedder()

    def load_language_model(self):
        """Load the DistilGPT-2 language model for response generation."""
        try:
            self.tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
            self.model = AutoModelForCausalLM.from_pretrained("distilgpt2")
            if has_gpu:
                self.model = self.model.to('cuda')
            self.generator = pipeline(
                'text-generation', 
                model=self.model, 
                tokenizer=self.tokenizer, 
                device=0 if has_gpu else -1
            )
            logger.info("Language model loaded successfully")
        except Exception as e:
            logger.error(f"Failed to load language model: {e}")
            self.generator = None

    def _create_knowledge_base(self):
        """Initialize the knowledge base with basic Vision 2030 information."""
        self.english_texts = [
            "Vision 2030 is Saudi Arabia's strategic framework to reduce dependence on oil, diversify the economy, and develop public sectors.",
            "The key pillars of Vision 2030 are a vibrant society, a thriving economy, and an ambitious nation.",
            "NEOM is a planned smart city in Tabuk Province, a key Vision 2030 project."
        ]
        self.arabic_texts = [
            "رؤية 2030 هي إطار استراتيجي لتقليل الاعتماد على النفط وتنويع الاقتصاد.",
            "الركائز الرئيسية لرؤية 2030 هي مجتمع حيوي، واقتصاد مزدهر، ووطن طموح.",
            "نيوم مدينة ذكية مخططة في تبوك، مشروع رئيسي لرؤية 2030."
        ]
        self.pdf_english_texts = []
        self.pdf_arabic_texts = []

    def _create_indices(self):
        """Create FAISS indices for the initial knowledge base."""
        try:
            # English index
            english_vectors = [self.english_embedder.encode(text) for text in self.english_texts]
            dim = len(english_vectors[0])
            nlist = max(1, len(english_vectors) // 10)
            quantizer = faiss.IndexFlatL2(dim)
            self.english_index = faiss.IndexIVFFlat(quantizer, dim, nlist)
            self.english_index.train(np.array(english_vectors))
            self.english_index.add(np.array(english_vectors))

            # Arabic index
            arabic_vectors = [self.arabic_embedder.encode(text) for text in self.arabic_texts]
            self.arabic_index = faiss.IndexIVFFlat(quantizer, dim, nlist)
            self.arabic_index.train(np.array(arabic_vectors))
            self.arabic_index.add(np.array(arabic_vectors))
            logger.info("FAISS indices created successfully")
        except Exception as e:
            logger.error(f"Error creating indices: {e}")

    def _create_sample_eval_data(self):
        """Create sample evaluation data for testing factual accuracy."""
        self.eval_data = [
            {"question": "What are the key pillars of Vision 2030?", 
             "lang": "en", 
             "reference": "The key pillars of Vision 2030 are a vibrant society, a thriving economy, and an ambitious nation."},
            {"question": "ما هي الركائز الرئيسية لرؤية 2030؟", 
             "lang": "ar", 
             "reference": "الركائز الرئيسية لرؤية 2030 هي مجتمع حيوي، واقتصاد مزدهر، ووطن طموح."}
        ]

    def retrieve_context(self, query, lang, session_id):
        """Retrieve relevant context based on the query and session history."""
        try:
            history = self.session_history.get(session_id, [])
            history_context = " ".join([f"Q: {q} A: {a}" for q, a in history[-2:]])
            embedder = self.arabic_embedder if lang == "ar" else self.english_embedder
            query_vec = embedder.encode(query)

            if lang == "ar":
                if self.has_pdf_content and self.pdf_arabic_texts:
                    index = self.pdf_arabic_index
                    texts = self.pdf_arabic_texts
                else:
                    index = self.arabic_index
                    texts = self.arabic_texts
            else:
                if self.has_pdf_content and self.pdf_english_texts:
                    index = self.pdf_english_index
                    texts = self.pdf_english_texts
                else:
                    index = self.english_index
                    texts = self.english_texts

            D, I = index.search(np.array([query_vec]), k=2)
            context = "\n".join([texts[i] for i in I[0] if i >= 0]) + f"\nHistory: {history_context}"
            return context if context.strip() else "No relevant information found."
        except Exception as e:
            logger.error(f"Retrieval error: {e}")
            return "Error retrieving context."

    def generate_response(self, query, session_id):
        """Generate a response to the user's query using context and session history."""
        if not query.strip():
            return "Please enter a valid question."
        
        start_time = time.time()
        try:
            lang = "ar" if any('\u0600' <= c <= '\u06FF' for c in query) else "en"
            context = self.retrieve_context(query, lang, session_id)
            
            if "Error" in context or "No relevant" in context:
                reply = context
            elif self.generator:
                prompt = f"Context: {context}\nQuestion: {query}\nAnswer:"
                response = self.generator(prompt, max_length=150, num_return_sequences=1, do_sample=True, temperature=0.7)
                reply = response[0]['generated_text'].split("Answer:")[-1].strip()
            else:
                reply = context
            
            self.session_history.setdefault(session_id, []).append((query, reply))
            self.metrics["response_times"].append(time.time() - start_time)
            return reply
        except Exception as e:
            logger.error(f"Response generation error: {e}")
            return "Sorry, an error occurred. Please try again."

    def evaluate_factual_accuracy(self, response, reference):
        """Evaluate the factual accuracy of a response using semantic similarity."""
        try:
            embedder = self.english_embedder  # Assuming reference is in English for simplicity
            response_vec = embedder.encode(response)
            reference_vec = embedder.encode(reference)
            similarity = util.cos_sim(response_vec, reference_vec).item()
            return similarity
        except Exception as e:
            logger.error(f"Evaluation error: {e}")
            return 0.0

    def process_pdf(self, file):
        """Process an uploaded PDF file and update the knowledge base."""
        if not file:
            return "Please upload a PDF file."
        
        try:
            pdf_reader = PyPDF2.PdfReader(io.BytesIO(file))
            text = "".join([page.extract_text() or "" for page in pdf_reader.pages])
            if not text.strip():
                return "No extractable text found in PDF."
            
            # Split text into chunks
            chunks = [text[i:i+300] for i in range(0, len(text), 300)]
            self.pdf_english_texts = [c for c in chunks if not any('\u0600' <= char <= '\u06FF' for char in c)]
            self.pdf_arabic_texts = [c for c in chunks if any('\u0600' <= char <= '\u06FF' for char in c)]

            # Create indices for PDF content
            if self.pdf_english_texts:
                english_vectors = [self.english_embedder.encode(text) for text in self.pdf_english_texts]
                dim = len(english_vectors[0])
                nlist = max(1, len(english_vectors) // 10)
                quantizer = faiss.IndexFlatL2(dim)
                self.pdf_english_index = faiss.IndexIVFFlat(quantizer, dim, nlist)
                self.pdf_english_index.train(np.array(english_vectors))
                self.pdf_english_index.add(np.array(english_vectors))

            if self.pdf_arabic_texts:
                arabic_vectors = [self.arabic_embedder.encode(text) for text in self.pdf_arabic_texts]
                dim = len(arabic_vectors[0])
                nlist = max(1, len(arabic_vectors) // 10)
                quantizer = faiss.IndexFlatL2(dim)
                self.pdf_arabic_index = faiss.IndexIVFFlat(quantizer, dim, nlist)
                self.pdf_arabic_index.train(np.array(arabic_vectors))
                self.pdf_arabic_index.add(np.array(arabic_vectors))

            self.has_pdf_content = True
            return f"PDF processed: {len(self.pdf_english_texts)} English, {len(self.pdf_arabic_texts)} Arabic chunks."
        except Exception as e:
            logger.error(f"PDF processing error: {e}")
            return f"Error processing PDF: {e}"

# Create the Gradio interface
def create_interface():
    """Set up the Gradio interface for chatting and PDF uploading."""
    assistant = Vision2030Assistant()

    def chat(query, history, session_id):
        reply = assistant.generate_response(query, session_id)
        history.append((query, reply))
        return history, ""

    with gr.Blocks() as demo:
        gr.Markdown("# Vision 2030 Virtual Assistant")
        session_id = gr.State(value="user1")  # Fixed session ID for simplicity
        chatbot = gr.Chatbot()
        msg = gr.Textbox(label="Ask a question")
        submit = gr.Button("Submit")
        pdf_upload = gr.File(label="Upload PDF", type="binary")
        upload_status = gr.Textbox(label="Upload Status")

        submit.click(chat, [msg, chatbot, session_id], [chatbot, msg])
        pdf_upload.upload(assistant.process_pdf, pdf_upload, upload_status)

    return demo

# Launch the interface
if __name__ == "__main__":
    demo = create_interface()
    demo.launch()