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| import streamlit as st | |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline | |
| import torch | |
| import easyocr | |
| from PIL import Image | |
| import fitz # PyMuPDF | |
| # Load EasyOCR Reader | |
| reader = easyocr.Reader(['en']) | |
| # Load ClinicalBERT Model | |
| def load_clinicalbert(): | |
| model_name = "emilyalsentzer/Bio_ClinicalBERT" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForSequenceClassification.from_pretrained(model_name) | |
| return pipeline("text-classification", model=model, tokenizer=tokenizer, device=0 if torch.cuda.is_available() else -1) | |
| clinicalbert_analyzer = load_clinicalbert() | |
| # OCR function using EasyOCR | |
| def extract_text_from_image(image): | |
| image = Image.open(image).convert('RGB') | |
| result = reader.readtext(image, detail=0) | |
| extracted_text = " ".join(result) | |
| return extracted_text | |
| # PDF text extraction using PyMuPDF | |
| def extract_text_from_pdf(pdf_file): | |
| text = "" | |
| with fitz.open(pdf_file) as pdf: | |
| for page in pdf: | |
| text += page.get_text() | |
| return text | |
| # Analysis function using ClinicalBERT | |
| def analyze_report(text): | |
| # Define prompts for analysis | |
| summary_prompt = f"Summarize the following medical report:\n{text}" | |
| interpretation_prompt = f"Interpret the following lab results:\n{text}" | |
| recommendation_prompt = f"Provide actionable recommendations based on this medical report:\n{text}" | |
| # Use ClinicalBERT for text analysis | |
| summary = clinicalbert_analyzer(summary_prompt)[0]['label'] | |
| interpretation = clinicalbert_analyzer(interpretation_prompt)[0]['label'] | |
| recommendations = clinicalbert_analyzer(recommendation_prompt)[0]['label'] | |
| return { | |
| "summary": summary, | |
| "interpretation": interpretation, | |
| "recommendations": recommendations | |
| } | |
| # Streamlit UI | |
| st.title("Clinical Lab Report Analyzer") | |
| st.write("Upload your medical lab report (PDF/Image) to get a summary, interpretation, and actionable recommendations.") | |
| 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..."): | |
| extracted_text = extract_text_from_image(uploaded_file) | |
| # Analyze the extracted text | |
| if extracted_text: | |
| with st.spinner("Analyzing the medical report using ClinicalBERT..."): | |
| result = analyze_report(extracted_text) | |
| # Display the results | |
| st.subheader("Summary") | |
| st.write(result['summary']) | |
| st.subheader("Interpretation of Results") | |
| st.write(result['interpretation']) | |
| st.subheader("Actionable Recommendations") | |
| st.write(result['recommendations']) | |
| else: | |
| st.error("No text could be extracted. Please try with a different file.") | |