import streamlit as st
from transformers import pipeline, VisionEncoderDecoderModel, ViTImageProcessor
from PIL import Image
import fitz 
import logging
from concurrent.futures import ThreadPoolExecutor
import torch

# Setup logging
def setup_logging():
    logging.basicConfig(
        level=logging.INFO,
        format="%(asctime)s - %(levelname)s - %(message)s",
    )

# Load models globally for faster performance
@st.cache_resource
def load_models():
    logging.info("Loading Hugging Face models...")
    # Load image-to-text model from Hugging Face
    processor = ViTImageProcessor.from_pretrained("microsoft/vision-transformation-transformer")
    model = VisionEncoderDecoderModel.from_pretrained("microsoft/vision-transformation-transformer")
    
    # Load translation models
    translator_hi = pipeline("translation", model="Helsinki-NLP/opus-mt-en-hi")
    translator_ur = pipeline("translation", model="Helsinki-NLP/opus-mt-en-ur")
    
    # Summarization model
    summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
    
    return processor, model, translator_hi, translator_ur, summarizer

# Function to extract text from images using Hugging Face model
def extract_text_from_image(image, processor, model):
    logging.info("Extracting text from image...")
    # Preprocess image
    inputs = processor(images=image, return_tensors="pt")
    # Use the model to generate captions
    out = model.generate(**inputs)
    return processor.decode(out[0], skip_special_tokens=True)

# Function to extract text from PDFs
def extract_text_from_pdf(pdf_file):
    logging.info("Extracting text from PDF...")
    doc = fitz.open(pdf_file)
    text = ""
    for page in doc:
        text += page.get_text()
    return text

# Function to process text in chunks for better performance
def process_chunks(text, model, chunk_size=500):
    chunks = [text[i:i + chunk_size] for i in range(0, len(text), chunk_size)]
    results = []
    with ThreadPoolExecutor() as executor:
        results = list(executor.map(lambda chunk: model(chunk, max_length=200), chunks))
    return " ".join([result[0]["translation_text"] for result in results])

# Main app logic
def main():
    setup_logging()
    st.title("Lab Report Analyzer")
    st.write("Upload a file (Image, PDF, or Text) to analyze and summarize the lab report in English, Hindi, and Urdu.")
    
    # Load models
    processor, model, translator_hi, translator_ur, summarizer = load_models()
    
    file = st.file_uploader("Upload a file (Image, PDF, or Text):", type=["jpg", "png", "jpeg", "pdf", "txt"])
    if file:
        text = ""
        try:
            if file.type in ["image/jpeg", "image/png", "image/jpg"]:
                image = Image.open(file)
                text = extract_text_from_image(image, processor, model)
            elif file.type == "application/pdf":
                text = extract_text_from_pdf(file)
            elif file.type == "text/plain":
                text = file.read().decode("utf-8")

            if text:
                with st.spinner("Analyzing the report..."):
                    # Generate summary
                    summary = summarizer(text, max_length=130, min_length=30)[0]["summary_text"]

                    # Generate translations
                    hindi_translation = process_chunks(text, translator_hi)
                    urdu_translation = process_chunks(text, translator_ur)

                    # Display results
                    st.subheader("Analysis Summary (English):")
                    st.write(summary)

                    st.subheader("Hindi Translation:")
                    st.write(hindi_translation)

                    st.subheader("Urdu Translation:")
                    st.write(urdu_translation)
            else:
                st.warning("No text could be extracted. Please check the file and try again.")
        except Exception as e:
            logging.error(f"Error processing the file: {e}")
            st.error("An error occurred while processing the file. Please try again.")
    else:
        st.info("Please upload a file to begin.")

if __name__ == "__main__":
    main()