app.py

import streamlit as st
from transformers import pipeline
from PIL import Image
import pytesseract
import PyPDF2
import pdfplumber
import torch

# Load the BART model for summarization and NLI
@st.cache_resource
def load_model():
    return pipeline("zero-shot-classification", model="facebook/bart-large-mnli", device=0 if torch.cuda.is_available() else -1)

classifier = load_model()

# 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()
    return text

# Summarize, interpret and give actionable insights
def analyze_report(text):
    # Provide a summary
    summary = classifier(text, candidate_labels=["summary"], multi_label=False)['labels'][0]

    # Interpretation of results
    interpretation = classifier(text, candidate_labels=["interpretation", "normal", "abnormal"], multi_label=True)

    # Recommendations
    recommendations = classifier(text, candidate_labels=["follow-up", "Holistic/OTC treatment", "dietary change", "medication"], multi_label=True)

    return {
        "summary": summary,
        "interpretation": interpretation['labels'],
        "recommendations": recommendations['labels']
    }

# Streamlit UI
st.title("Medical Lab Report Analyzer")
st.write("Upload your medical lab report (PDF/Image) for 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:
        with st.spinner("Analyzing report..."):
            result = analyze_report(extracted_text)
        
        # Display the results
        st.subheader("Summary")
        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.")