import os
import gradio as gr
import fitz  # PyMuPDF
from sentence_transformers import SentenceTransformer
import numpy as np
import faiss
from typing import List, Tuple, Dict

# Placeholder for the app's state
class MyApp:
    def __init__(self) -> None:
        self.documents = []
        self.embeddings = None
        self.index = None
        self.model = SentenceTransformer('all-MiniLM-L6-v2')

    def load_pdfs(self, files: List[gr.File]) -> str:
        """Extracts text from multiple PDF files and stores them."""
        self.documents = []
        for file in files:
            doc = fitz.open(stream=file.read(), filetype="pdf")
            for page_num in range(len(doc)):
                page = doc[page_num]
                text = page.get_text()
                self.documents.append({
                    "file_name": file.name,
                    "page": page_num + 1,
                    "content": text
                })
        return f"Processed {len(files)} PDFs successfully!"

    def build_vector_db(self) -> str:
        """Builds a vector database using the content of the PDFs."""
        if not self.documents:
            return "No documents to process."
        contents = [doc["content"] for doc in self.documents]
        self.embeddings = self.model.encode(contents, show_progress_bar=True)
        self.index = faiss.IndexFlatL2(self.embeddings.shape[1])
        self.index.add(np.array(self.embeddings))
        return "Vector database built successfully!"

    def search_documents(self, query: str, k: int = 3) -> List[Dict]:
        """Searches for relevant document snippets using vector similarity."""
        if not self.index:
            return [{"content": "Vector database is not built."}]
        query_embedding = self.model.encode([query], show_progress_bar=False)
        D, I = self.index.search(np.array(query_embedding), k)
        results = [self.documents[i] for i in I[0]]
        return results if results else [{"content": "No relevant documents found."}]

app = MyApp()

def upload_files(files: List[gr.File]) -> str:
    return app.load_pdfs(files)

def build_vector_db() -> str:
    return app.build_vector_db()

def respond(message: str, history: List[Tuple[str, str]]) -> Tuple[List[Tuple[str, str]], str]:
    # Retrieve relevant documents
    retrieved_docs = app.search_documents(message)
    context = "\n".join(
        [f"File: {doc['file_name']}, Page: {doc['page']}\n{doc['content']}" for doc in retrieved_docs]
    )

    # Generate response (Placeholder for actual model inference)
    response_content = f"Simulated response based on the following context:\n{context}"

    # Append the message and generated response to the chat history
    history.append((message, response_content))
    return history, ""

with gr.Blocks() as demo:
    gr.Markdown("# PDF Chatbot")
    gr.Markdown("Upload your PDFs, build a vector database, and start querying your documents.")

    with gr.Row():
        with gr.Column():
            upload_btn = gr.File(label="Upload PDFs", file_types=[".pdf"], file_count="multiple")
            upload_message = gr.Textbox(label="Upload Status", lines=2)
            build_db_btn = gr.Button("Build Vector Database")
            db_message = gr.Textbox(label="DB Build Status", lines=2)

            upload_btn.change(upload_files, inputs=[upload_btn], outputs=[upload_message])
            build_db_btn.click(build_vector_db, inputs=[], outputs=[db_message])

        with gr.Column():
            chatbot = gr.Chatbot(label="Chat Responses")
            query_input = gr.Textbox(label="Enter your query here")
            submit_btn = gr.Button("Submit")
            submit_btn.click(respond, inputs=[query_input, chatbot], outputs=[chatbot, query_input])

demo.launch()