import os
import PyPDF2
from sentence_transformers import SentenceTransformer
import warnings

warnings.filterwarnings(
    "ignore",
    category=FutureWarning,
    message="`clean_up_tokenization_spaces` was not set.*"
)

model = SentenceTransformer('all-MiniLM-L6-v2')

def parse_pdf(filepath):
    text = ""
    with open(filepath, 'rb') as f:
        reader = PyPDF2.PdfReader(f)
        for page in reader.pages:
            text += page.extract_text() + "\n"
    return text

def parse_audio(filepath):
    try:
        import whisper
        model = whisper.load_model("base")
        result = model.transcribe(filepath)
        return result['text']
    except Exception as e:
        raise RuntimeError(f"Audio parsing failed — likely missing ffmpeg. Error: {e}")

def parse_text(filepath):
    with open(filepath, 'r') as f:
        return f.read()

def parse_file(file_obj):
    filename = file_obj.name.lower()

    if filename.endswith(".pdf"):
        reader = PyPDF2.PdfReader(file_obj)
        text = ""
        for page in reader.pages:
            text += page.extract_text()
        return text

    elif filename.endswith(".txt"):
        return file_obj.read().decode("utf-8")

    else:
        raise ValueError("Unsupported file type.")

def chunk_text(text, chunk_size=300):
    words = text.split()
    return [' '.join(words[i:i+chunk_size]) for i in range(0, len(words), chunk_size)]

def chunk_and_embed(text):
    chunks = chunk_text(text)
    embeddings = model.encode(chunks).tolist()
    return list(zip(chunks, embeddings))