import gradio as gr
import pandas as pd
import pixeltable as pxt
from pixeltable.iterators import DocumentSplitter
import numpy as np
from pixeltable.functions.huggingface import sentence_transformer
from pixeltable.functions import openai
import os

"""## Store OpenAI API Key"""

if 'OPENAI_API_KEY' not in os.environ:
    os.environ['OPENAI_API_KEY'] = getpass.getpass('Enter your OpenAI API key:')

"""Pixeltable Set up"""

# Ensure a clean slate for the demo
pxt.drop_dir('rag_demo', force=True)
pxt.create_dir('rag_demo')

# Set up embedding function
@pxt.expr_udf
def e5_embed(text: str) -> np.ndarray:
    return sentence_transformer(text, model_id='intfloat/e5-large-v2')

# Create prompt function
@pxt.udf
def create_prompt(top_k_list: list[dict], question: str) -> str:
    concat_top_k = '\n\n'.join(
        elt['text'] for elt in reversed(top_k_list)
    )
    return f'''
    PASSAGES:

    {concat_top_k}

    QUESTION:

    {question}'''

# Gradio Application
def process_files(ground_truth_file, pdf_files):
    # Ensure a clean slate for the demo by removing and recreating the 'rag_demo' directory
    pxt.drop_dir('rag_demo', force=True)
    pxt.create_dir('rag_demo')

    # Process the ground truth file, which contains questions and correct answers
    # Import as CSV or Excel depending on the file extension
    if ground_truth_file.name.endswith('.csv'):
        queries_t = pxt.io.import_csv('rag_demo.queries', ground_truth_file.name)
    else:
        queries_t = pxt.io.import_excel('rag_demo.queries', ground_truth_file.name)

    # Create a table to store the uploaded PDF documents
    documents_t = pxt.create_table(
        'rag_demo.documents',
        {'document': pxt.DocumentType()}
    )
    
    # Insert the PDF files into the documents table
    documents_t.insert({'document': file.name} for file in pdf_files if file.name.endswith('.pdf'))

    # Create a view that splits the documents into smaller chunks
    chunks_t = pxt.create_view(
        'rag_demo.chunks',
        documents_t,
        iterator=DocumentSplitter.create(
            document=documents_t.document,
            separators='token_limit',
            limit=300
        )
    )

    # Add an embedding index to the chunks for similarity search
    chunks_t.add_embedding_index('text', string_embed=e5_embed)

    # Define a query function to retrieve the top-k most similar chunks for a given question
    @chunks_t.query
    def top_k(query_text: str):
      sim = chunks_t.text.similarity(query_text)
      return (
          chunks_t.order_by(sim, asc=False)
              .select(chunks_t.text, sim=sim)
              .limit(5)
      )

    # Add computed columns to the queries table for context retrieval and prompt creation
    queries_t['question_context'] = chunks_t.top_k(queries_t.Question)
    queries_t['prompt'] = create_prompt(
        queries_t.question_context, queries_t.Question
    )

    # Prepare messages for the OpenAI API, including system instructions and user prompt
    messages = [
        {
            'role': 'system',
            'content': 'Please read the following passages and answer the question based on their contents.'
        },
        {
            'role': 'user',
            'content': queries_t.prompt
        }
    ]

     # Add OpenAI response column
    queries_t['response'] = openai.chat_completions(
        model='gpt-4o-mini-2024-07-18', messages=messages
    )
    
    # Extract the answer text from the API response
    queries_t['answer'] = queries_t.response.choices[0].message.content.astype(pxt.StringType())

    # Prepare the output dataframe with questions, correct answers, and model-generated answers
    df_output = queries_t.select(queries_t.Question, queries_t.correct_answer, queries_t.answer).collect().to_pandas()

    try:
        # Return the output dataframe for display
        return df_output
    except Exception as e:
        return f"An error occurred: {str(e)}", None

# Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# RAG Demo App")

    # File upload components for ground truth and PDF documents
    with gr.Row():
        ground_truth_file = gr.File(label="Upload Ground Truth (CSV or XLSX)", file_count="single")
        pdf_files = gr.File(label="Upload PDF Documents", file_count="multiple")

    # Button to trigger file processing
    process_button = gr.Button("Process Files and Generate Outputs")

    # Output component to display the results
    df_output = gr.DataFrame(label="Pixeltable Table")

    process_button.click(process_files, inputs=[ground_truth_file, pdf_files], outputs=df_output)
    #question_input = gr.Textbox(label="Enter your question")
    #query_button = gr.Button("Query LLM")
    
    #query_button.click(query_llm, inputs=question_input, outputs=output_dataframe)

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