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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()
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