import gradio as gr
import os
import PyPDF2
import pandas as pd
import openai
import docx
import requests
import json
from docx import Document
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_folder(folder_path):
    """Scans a folder and its subfolders for PDF, TXT, CSV, DOCX, and IPYNB files."""
    extracted_files = {"pdf": [], "txt": [], "csv": [], "docx": [], "ipynb": []}
    
    print(f"Scanning folder: {folder_path}")
    for root, subdirs, files in os.walk(folder_path):
        print(f"Checking folder: {root}")  # Debugging log for subfolders
        for file_name in files:
            file_path = os.path.join(root, file_name)
            print(f"Found file: {file_path}")
            if file_name.endswith(".pdf"):
                extracted_files["pdf"].append(file_path)
            elif file_name.endswith(".txt"):
                extracted_files["txt"].append(file_path)
            elif file_name.endswith(".csv"):
                extracted_files["csv"].append(file_path)
            elif file_name.endswith(".docx"):
                extracted_files["docx"].append(file_path)
            elif file_name.endswith(".ipynb"):
                extracted_files["ipynb"].append(file_path)
    
    print("Files found:", extracted_files)  # Debugging log
    return extracted_files

def get_text_from_pdf(pdf_files):
    """Extracts text from PDF files."""
    text = ""
    for pdf_path in pdf_files:
        with open(pdf_path, "rb") as pdf_file:
            reader = PyPDF2.PdfReader(pdf_file)
            for page in reader.pages:
                text += page.extract_text() + "\n"
    return text

def read_text_from_files(file_paths):
    """Reads text content from TXT files."""
    text = ""
    for file_path in file_paths:
        with open(file_path, "r", encoding="utf-8", errors="ignore") as file:
            text += file.read() + "\n"
    return text

def get_text_from_csv(csv_files):
    """Extracts text from CSV files."""
    text = ""
    for csv_path in csv_files:
        df = pd.read_csv(csv_path)
        text += df.to_string() + "\n"
    return text

def get_text_from_docx(docx_files):
    """Extracts text from DOCX files."""
    text = ""
    for docx_path in docx_files:
        doc = Document(docx_path)
        for para in doc.paragraphs:
            text += para.text + "\n"
    return text

def get_text_from_ipynb(ipynb_files):
    """Extracts text from Jupyter Notebook (.ipynb) files."""
    text = ""
    for ipynb_path in ipynb_files:
        with open(ipynb_path, "r", encoding="utf-8", errors="ignore") as file:
            content = json.load(file)
            for cell in content.get("cells", []):
                if cell.get("cell_type") == "markdown" or cell.get("cell_type") == "code":
                    text += "\n".join(cell.get("source", [])) + "\n"
    return text

def combine_text_from_files(extracted_files):
    """Combines text from all extracted files."""
    text = (
        get_text_from_pdf(extracted_files["pdf"]) +
        read_text_from_files(extracted_files["txt"]) +
        get_text_from_csv(extracted_files["csv"]) +
        get_text_from_docx(extracted_files["docx"]) +
        get_text_from_ipynb(extracted_files["ipynb"])
    )
    return text

def generate_response(question, text):
    """Uses OpenAI to answer a question based on extracted text."""
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=[
            {"role": "system", "content": "You are a data analytics assistant. Answer the question based on the provided document content."},
            {"role": "user", "content": f"{question}\n\nBased on the following document content:\n{text[:3000]}"}  # Limit to 3000 characters to avoid excessive token usage
        ]
    )
    return response["choices"][0]["message"]["content"].strip()

def chatbot_interface(question):
    folder_path = "New_Data_Analytics/"
    extracted_files = extract_files_from_folder(folder_path)
    
    text = combine_text_from_files(extracted_files)
    
    print("Final extracted text for chatbot processing:", text[:500])  # Debugging log (First 500 chars)
    
    if not text.strip():
        return "The folder does not contain valid PDF, TXT, CSV, DOCX, or IPYNB files. Please upload supported file types."
    
    return generate_response(question, text)

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

demo.launch()