import mimetypes
import pandas as pd
import PyPDF2
import json
import re
import spacy
import os
from dotenv import load_dotenv
import openai
import numpy as np

# Load environment variables
load_dotenv()

# Set OpenAI API Key

openai.api_key = os.getenv("OPENAI_API_KEY")

# Load SpaCy model
# nlp = spacy.load("en_core_web_sm")


import spacy
from spacy.cli import download

# Ensure the model is available
try:
    nlp = spacy.load("en_core_web_sm")
except OSError:
    print("Downloading SpaCy 'en_core_web_sm' model...")
    download("en_core_web_sm")
    nlp = spacy.load("en_core_web_sm")


# Detect file type
def detect_file_type(file_path):
    file_type = mimetypes.guess_type(file_path)[0]
    if file_type in ["application/pdf"]:
        return "pdf"
    elif file_type in ["text/csv", "application/vnd.ms-excel"]:
        return "csv"
    elif file_type == "application/json":
        return "json"
    else:
        raise ValueError(f"Unsupported file format: {file_type}")

# Extract text from CSV
def extract_text_from_csv(file_path):
    df = pd.read_csv(file_path)
    text = " ".join(df.astype(str).stack())
    return text

# Extract text from PDF
def extract_text_from_pdf(file_path):
    pdf_reader = PyPDF2.PdfReader(file_path)
    text = ""
    for page in pdf_reader.pages:
        text += page.extract_text()
    return text

# Extract text from JSON
def extract_text_from_json(file_path):
    def recursive_text_extraction(data):
        if isinstance(data, dict):
            return " ".join(recursive_text_extraction(value) for value in data.values())
        elif isinstance(data, list):
            return " ".join(recursive_text_extraction(item) for item in data)
        else:
            return str(data)

    with open(file_path, 'r') as f:
        data = json.load(f)
    return recursive_text_extraction(data)

# Generalized text extraction
def extract_text(file_path):
    file_type = detect_file_type(file_path)
    if file_type == "csv":
        return extract_text_from_csv(file_path)
    elif file_type == "pdf":
        return extract_text_from_pdf(file_path)
    elif file_type == "json":
        return extract_text_from_json(file_path)
    else:
        raise ValueError("Unsupported file format")

# Preprocess text
def preprocess_text_generalized(text):
    text = re.sub(r"http\S+|www\S+|https\S+", "", text)  # Remove URLs
    text = re.sub(r"[^\x20-\x7E]", "", text)  # Remove non-ASCII characters
    text = re.sub(r"\s+", " ", text)  # Normalize whitespace
    chunk_size = 100000  # Maximum chunk size
    chunks = [text[i:i + chunk_size] for i in range(0, len(text), chunk_size)]
    processed_chunks = []
    for chunk in chunks:
        doc = nlp(chunk.lower())
        tokens = [
            token.lemma_
            for token in doc
            if not token.is_stop and token.is_alpha
        ]
        processed_chunks.append(" ".join(tokens))
    processed_text = " ".join(processed_chunks)
    return processed_text

# Generate embeddings using OpenAI API
def get_openai_embeddings(text, model="text-embedding-ada-002"):
    """
    Generate embeddings for a given text using OpenAI API.
    """
    try:
        response = openai.Embedding.create(input=text, model=model)
        embeddings = response["data"][0]["embedding"]
        return np.array(embeddings)  # Convert to NumPy array for compatibility
    except Exception as e:
        print(f"Error generating embeddings: {e}")
        return None

# Example usage
if __name__ == "__main__":
    # Example file path
    file_path = "example.pdf"

    # Extract and preprocess text
    raw_text = extract_text(file_path)
    preprocessed_text = preprocess_text_generalized(raw_text)

    # Generate embeddings using OpenAI API
    embeddings = get_openai_embeddings(preprocessed_text)
    if embeddings is not None:
        print(f"Embeddings generated successfully. Shape: {embeddings.shape}")
    else:
        print("Failed to generate embeddings.")