import gradio as gr
from transformers import pipeline, AutoModelForSeq2SeqLM, AutoTokenizer
import pdfplumber
from docx import Document
import textract
from langdetect import detect

# Function to detect language from the text
def detect_language(text):
    try:
        return detect(text)
    except Exception as e:
        print(f"Error detecting language: {e}")
        return None

# Function to read document and extract text
def read_document(file_path):
    text = ""
    try:
        if file_path.endswith('.pdf'):
            with pdfplumber.open(file_path) as pdf:
                for page in pdf.pages:
                    text += page.extract_text()
        elif file_path.endswith('.docx'):
            doc = Document(file_path)
            for para in doc.paragraphs:
                text += para.text
        else:
            text = textract.process(file_path).decode()
    except Exception as e:
        print(f"An error occurred: {e}")
    return text

# Function to get summarization model
def get_summarizer(language):
    model_name = "facebook/bart-large-cnn" if language == "en" else "facebook/mbart-large-50"
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
    return pipeline("summarization", model=model, tokenizer=tokenizer)

# Gradio interface function to summarize the document
def summarize_document(file_info):
    try:
        # Read the uploaded file and extract text
        file_path = file_info["name"]
        text = read_document(file_path)
        
        if not text.strip():
            return "The document is empty or could not be read."
    
        # Detect the language of the text
        language = detect_language(text)
        if not language:
            return "Language detection failed."
        
        # Get the appropriate summarizer model
        summarizer = get_summarizer(language)
        
        # Generate summary
        summary = summarizer(text, max_length=130, min_length=30, truncation=True)
        return summary[0]['summary_text']
    except Exception as e:
        # This will print the error message and the traceback
        print(f"An error occurred: {e}")
        traceback.print_exc()
        return str(e)  # Return the error message as output to the user
    
# Gradio app interface
iface = gr.Interface(
    fn=summarize_document,
    inputs=gr.File(label="Upload your document (PDF, DOCX, or TXT)", type="binary"),
    outputs="text",
    title="Document Summarizer",
    description="Upload your document and get a summarized version of its content. Currently supports English and French."
)


# Run the Gradio app
iface.launch()