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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.") |