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import streamlit as st
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
from transformers import AutoModelForSequenceClassification
from PIL import Image
import pytesseract
import pdfplumber
import torch
# Load BART for zero-shot classification and Bio_ClinicalBERT for text summarization
@st.cache_resource
def load_models():
# Bio_ClinicalBERT for text summarization
tokenizer_bert = AutoTokenizer.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")
model_bert = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large-cnn")
summarizer = pipeline("summarization", model=model_bert, tokenizer=tokenizer_bert, device=0 if torch.cuda.is_available() else -1)
# BART model for zero-shot classification
classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli", device=0 if torch.cuda.is_available() else -1)
return summarizer, classifier
summarizer, classifier = load_models()
# OCR for Image using Tesseract
def extract_text_from_image(image):
return pytesseract.image_to_string(image)
# Extract text from PDF using pdfplumber
def extract_text_from_pdf(pdf_file):
text = ""
with pdfplumber.open(pdf_file) as pdf:
for page in pdf.pages:
text += page.extract_text() or ""
return text
# Analyze and interpret the medical report
def analyze_medical_text(text):
# Summarize the extracted text using ClinicalBERT
summarized_text = summarizer(text, max_length=100, min_length=30, do_sample=False)[0]['summary_text']
# Use BART for classification insights
interpretation = classifier(
summarized_text,
candidate_labels=["normal", "abnormal", "urgent", "needs follow-up", "critical condition"],
multi_label=True
)
recommendations = classifier(
summarized_text,
candidate_labels=["medication", "dietary change", "exercise", "follow-up with a doctor", "lifestyle change"],
multi_label=True
)
return {
"summary": summarized_text,
"interpretation": interpretation['labels'],
"recommendations": recommendations['labels']
}
# Streamlit UI
st.title("Medical Lab Report Analyzer with ClinicalBERT and BART")
st.write("Upload your medical lab report (PDF/Image) to get a summary and actionable insights.")
uploaded_file = st.file_uploader("Choose a PDF/Image file", type=["pdf", "png", "jpg", "jpeg"])
if uploaded_file:
file_type = uploaded_file.type
# Extract text based on file type
if file_type == "application/pdf":
with st.spinner("Extracting text from PDF..."):
extracted_text = extract_text_from_pdf(uploaded_file)
else:
with st.spinner("Extracting text from Image..."):
image = Image.open(uploaded_file)
extracted_text = extract_text_from_image(image)
# Analyze the extracted text
if extracted_text.strip():
with st.spinner("Analyzing report using ClinicalBERT..."):
result = analyze_medical_text(extracted_text)
# Display the results
st.subheader("Summary of the Report")
st.write(result['summary'])
st.subheader("Interpretation of Results")
for label in result['interpretation']:
st.write(f"- {label.capitalize()}")
st.subheader("Actionable Recommendations")
for rec in result['recommendations']:
st.write(f"- {rec.capitalize()}")
else:
st.error("No text could be extracted. Please try with a different file.")
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