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| import streamlit as st | |
| import pdfplumber | |
| import pytesseract | |
| from PIL import Image | |
| from transformers import pipeline | |
| import re | |
| # Ensure Tesseract-OCR is properly configured (Uncomment & update path if needed) | |
| # pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe" | |
| # Load pre-trained Hugging Face models | |
| summarizer = pipeline("summarization", model="t5-small") | |
| medical_qa = pipeline("question-answering", model="deepset/bert-base-cased-squad2") | |
| # Function to extract text from PDF | |
| def extract_text_from_pdf(pdf_file): | |
| with pdfplumber.open(pdf_file) as pdf: | |
| text = "\n".join(page.extract_text() for page in pdf.pages if page.extract_text()) | |
| return text if text else "No text found in PDF." | |
| # Function to extract text from images (JPG, PNG) | |
| def extract_text_from_image(image_file): | |
| image = Image.open(image_file) | |
| text = pytesseract.image_to_string(image) | |
| return text.strip() if text else "No text found in Image." | |
| # Function to summarize medical report | |
| def summarize_report(text): | |
| if len(text) > 500: # Handle long text | |
| text = text[:500] | |
| summary = summarizer(text, max_length=150, min_length=50, do_sample=False) | |
| return summary[0]['summary_text'] | |
| # Function to find medical terms dynamically using regex | |
| def extract_medical_terms(text): | |
| words = re.findall(r'\b[A-Z][a-z]+(?:[ -][A-Z][a-z]+)*\b', text) | |
| return list(set(words)) | |
| # Function to explain medical terms | |
| def explain_term(term): | |
| context = "Hypercholesterolemia is a condition with high cholesterol in the blood. Atherosclerosis refers to artery narrowing due to fat buildup." | |
| response = medical_qa(question=f"What is {term}?", context=context) | |
| return response["answer"] | |
| # Streamlit UI | |
| st.title("🩺 AI Medical Report Analyzer") | |
| st.write("Upload a medical **PDF or Image (JPG, PNG)** to get a summarized report with term explanations.") | |
| uploaded_file = st.file_uploader("Upload a PDF or Image", type=["pdf", "jpg", "png"]) | |
| if uploaded_file: | |
| file_type = uploaded_file.type | |
| if file_type == "application/pdf": | |
| text = extract_text_from_pdf(uploaded_file) | |
| st.subheader("📜 Extracted Text from PDF:") | |
| elif file_type in ["image/png", "image/jpeg"]: | |
| text = extract_text_from_image(uploaded_file) | |
| st.subheader("🖼️ Extracted Text from Image:") | |
| st.text_area("Report Content:", text, height=200) | |
| if st.button("Generate AI Summary"): | |
| summary = summarize_report(text) | |
| st.subheader("📑 AI-Generated Summary:") | |
| st.markdown(f"**{summary}**") | |
| if st.button("Explain Medical Terms"): | |
| terms = extract_medical_terms(text) | |
| if terms: | |
| st.subheader("📖 Medical Term Explanations:") | |
| for term in terms[:5]: # Limit to 5 terms for efficiency | |
| explanation = explain_term(term) | |
| st.markdown(f"**{term}:** {explanation}") | |
| else: | |
| st.write("No medical terms detected.") |