import gradio as gr
import os
import PyPDF2
import pandas as pd
import openai
import zipfile
from io import BytesIO
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_community.llms import OpenAI
from langchain.text_splitter import RecursiveCharacterTextSplitter


def detect_language(text):
    """Detects the language of the input text using OpenAI."""
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=[
            {"role": "system", "content": "Detect the language of this text."},
            {"role": "user", "content": text}
        ]
    )
    return response["choices"][0]["message"]["content"].strip()

# Set up OpenAI API key (replace with your key)
openai.api_key = "YOUR_OPENAI_API_KEY"

def extract_files_from_zip(zip_path):
    """Extracts PDF, TXT, and CSV files from a ZIP archive, including subfolders."""
    extracted_files = {"pdf": [], "txt": [], "csv": []}
    
    with zipfile.ZipFile(zip_path, 'r') as zip_ref:
        for file_name in zip_ref.namelist():
            if file_name.endswith(('.pdf', '.txt', '.csv')):
                with zip_ref.open(file_name) as file:
                    content = file.read()
                    if file_name.endswith(".pdf"):
                        extracted_files["pdf"].append(BytesIO(content))
                    elif file_name.endswith(".txt"):
                        extracted_files["txt"].append(BytesIO(content))
                    elif file_name.endswith(".csv"):
                        extracted_files["csv"].append(BytesIO(content))
    return extracted_files

def analyze_text(text):
    """Uses OpenAI to analyze notes, links, and complementary information in the text."""
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=[
            {"role": "system", "content": "Analyze this document and extract key points, links, and complementary information."},
            {"role": "user", "content": text}
        ]
    )
    return response["choices"][0]["message"]["content"].strip()

def get_text_from_pdf(pdf_files):
    text = ""
    for pdf in pdf_files:
        reader = PyPDF2.PdfReader(pdf)
        for page in reader.pages:
            text += page.extract_text() + "\n"
    return text

def get_text_from_txt(txt_files):
    text = ""
    for txt in txt_files:
        text += txt.read().decode("utf-8") + "\n"
    return text

def get_text_from_csv(csv_files):
    text = ""
    for csv in csv_files:
        df = pd.read_csv(csv)
        text += df.to_string() + "\n"
    return text

def create_vector_database(text):
    splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
    texts = splitter.split_text(text)
    embeddings = OpenAIEmbeddings()
    vector_db = FAISS.from_texts(texts, embeddings)
    return vector_db

def get_answer(question, vector_db, analysis):
    retriever = vector_db.as_retriever()
    docs = retriever.get_relevant_documents(question)
    
    if not docs:
        return "I could not find the answer in the documents. Do you want me to search external sources?"
    
    context = "\n".join([doc.page_content for doc in docs])
    language = detect_language(question)
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=[
            {"role": "system", "content": f"You are a Data Analytics assistant. Answer in {language}. Use the documents and their analyses to answer questions."},
            {"role": "user", "content": question + "\n\nBased on the following document content:\n" + context + "\n\nAdditional insights:\n" + analysis}
        ]
    )
    return response["choices"][0]["message"]["content"]

def chatbot_interface(zip_file_path, question):
    if not zip_file_path:
        return "Please upload a ZIP file before asking a question."
    
    extracted_files = extract_files_from_zip(zip_file_path)
    text = get_text_from_pdf(extracted_files["pdf"]) + get_text_from_txt(extracted_files["txt"]) + get_text_from_csv(extracted_files["csv"])
    
    if not text:
        return "The ZIP file does not contain valid PDF, TXT, or CSV files. Please upload supported file types."
    
    analysis = analyze_text(text)
    vector_db = create_vector_database(text)
    return get_answer(question, vector_db, analysis)

# Gradio interface
demo = gr.Interface(
    fn=chatbot_interface,
    inputs=[gr.File(label="Upload ZIP File"),
            gr.Textbox(label="Ask a question", placeholder="Type your question here...")],
    outputs=gr.Textbox(label="Answer")
)

demo.launch()