import pandas as pd
import fitz  # PyMuPDF for PDF extraction
import spacy
from langchain.vectorstores import FAISS
import torch
from transformers import AutoTokenizer, AutoModel
import gradio as gr

# Load and preprocess PDF text
def extract_text_from_pdf(pdf_path):
    text = ""
    with fitz.open(pdf_path) as pdf_document:
        for page_num in range(len(pdf_document)):
            page = pdf_document.load_page(page_num)
            text += page.get_text()
    return text

# Extract text from the PDF
pdf_text = extract_text_from_pdf('Getting_Started_with_Ubuntu_16.04.pdf')  # Replace with your PDF path

# Convert the text to a DataFrame
df = pd.DataFrame({'text': [pdf_text]})

# Define your custom embedding model
class CustomEmbeddingModel:
    def __init__(self, model_name):
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModel.from_pretrained(model_name)

    def embed_text(self, text):
        inputs = self.tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
        with torch.no_grad():
            embeddings = self.model(**inputs).last_hidden_state.mean(dim=1)
        return embeddings[0].numpy()

embedding_model = CustomEmbeddingModel('distilbert-base-uncased')  # Replace with your model name

# Load Spacy model for preprocessing
nlp = spacy.load("en_core_web_sm")

def preprocess_text(text):
    doc = nlp(text)
    tokens = [token.lemma_.lower() for token in doc if token.text.lower() not in stopwords.words('english') and token.is_alpha]
    return ' '.join(tokens)

# Apply preprocessing and embedding
df['text'] = df['text'].apply(preprocess_text)
df['text_embeddings'] = df['text'].apply(lambda x: embedding_model.embed_text(x))

# Create FAISS vector store
documents = df['text'].tolist()
embeddings = df['text_embeddings'].tolist()
vector_store = FAISS.from_documents(documents, embeddings)

# Function to generate a response
def generate_response(query):
    preprocessed_query = preprocess_text(query)
    query_embedding = embedding_model.embed_text(preprocessed_query)
    # Find the closest document in the vector store
    distances, indices = vector_store.search(query_embedding, k=1)  # k=1 for the closest document
    if indices:
        response = documents[indices[0]]
    else:
        response = "No relevant information found."
    return response

# Gradio interface
iface = gr.Interface(
    fn=generate_response,
    inputs=gr.Textbox(label="Enter your query", placeholder="Ask about Ubuntu..."),
    outputs=gr.Textbox(label="Response"),
    title="Ubuntu Manual Chatbot",
    description="Ask questions about the Ubuntu manual."
)

if __name__ == "__main__":
    iface.launch()