app.py

import gradio as gr
import pandas as pd
import easyocr
from paddleocr import PaddleOCR
from file_processing import read_file
from entity_recognition import process_text
from utils import safe_dataframe


# Initialize PaddleOCR globally (CPU mode)
reader = PaddleOCR(use_angle_cls=True, lang="en") 

# reader = easyocr.Reader(['en'],download_enabled=True, gpu=True)  # Initialize OCR model





with gr.Blocks() as demo:
    gr.Markdown("# 🏥 Medical Lab Test Report Extracter")

    with gr.Row():
        file_input = gr.File(label="📂 Upload Report")
    submit_btn = gr.Button("Extract",visible=False)

    welcome_msg=gr.Markdown("# Please Upload any lab report file 📂 and the processing will start automatically ")

    def invisible():
      return gr.update(visible=False)
    file_input.upload(invisible,outputs=welcome_msg,api_name=False)




    @gr.render(inputs=file_input,triggers=[file_input.upload])
    def extract(file):
        """Processes the uploaded file and extracts medical data."""
        # welcome_msg.update(visible=False)


        text = read_file(file.name, reader)  # Read the file (implement `read_file`)
        print("Performing NER...")
        output = process_text(text)  # Perform entity recognition (implement `process_text`)


        metadata = output["metadata"]


        metadata_str = f"**Patient Name:** {metadata['patient_name']}\n\n" \
                      f"**Age:** {metadata['age']} \n\n" \
                      f"**Gender:** {metadata['gender']}\n\n" \
                      f"**Lab Name:** {metadata['lab_name']}\n\n" \
                      f"**Report Date:** {metadata['report_date']}"

        print(f"Processed report for {metadata['patient_name']}")
        metadata_md = gr.Markdown(metadata_str)


        for test in output["report"]:
            test_type = test["test_type"]
            lab_tests = safe_dataframe(test,"lab_tests")

            gr.Markdown(f"### 📊 Test : {test_type}")
            gr.Dataframe(lab_tests)

        gr.JSON(output,label="📜 Extracted Report")
        return output
    output_JSON=gr.JSON(visible=False)
    submit_btn.click(extract,inputs=file_input,outputs=output_JSON,api_name="extract_report")

demo.launch(debug=True, share=True)