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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) |