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| import streamlit as st | |
| from PyPDF2 import PdfReader | |
| from transformers import pipeline | |
| from PIL import Image | |
| # Load Hugging Face model | |
| model = pipeline("text-generation", model="Ketak-ZoomRx/Drug_Prompt_Ollama_67k",torch_dtype="auto", trust_remote_code=True, use_fast=True, device_map={"": "cuda:0"},) | |
| def extract_text_from_pdf(pdf_file): | |
| reader = PdfReader(pdf_file) | |
| text = "" | |
| for page in reader.pages: | |
| text += page.extract_text() | |
| return text | |
| def get_lab_report_interpretation(text): | |
| result = model(text) | |
| return result[0]['label'] | |
| def process_image(image_file): | |
| img = Image.open(image_file) | |
| return img | |
| # Streamlit UI | |
| st.title("Medical Lab Report Analyzer") | |
| # Upload image and PDF | |
| uploaded_pdf = st.file_uploader("Upload your PDF Medical Lab Report", type="pdf") | |
| uploaded_image = st.file_uploader("Upload Image of Medical Report", type="jpg png jpeg") | |
| if uploaded_pdf is not None: | |
| st.subheader("Extracted Text from PDF:") | |
| pdf_text = extract_text_from_pdf(uploaded_pdf) | |
| st.write(pdf_text) | |
| st.subheader("Lab Report Interpretation:") | |
| interpretation = get_lab_report_interpretation(pdf_text) | |
| st.write(interpretation) | |
| elif uploaded_image is not None: | |
| st.image(process_image(uploaded_image), caption="Uploaded Image", use_column_width=True) | |
| st.warning("For text extraction from image, please upload a PDF.") | |
| else: | |
| st.info("Please upload a medical PDF or image for analysis.") | |