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import gradio as gr
import pandas as pd
# from file_processing import FileProcessorFactory
from file_processing import read_file
from entity_recognition import process_text
from utils import safe_dataframe
def show_to_UI(file):
"""Processes the uploaded file and extracts medical data."""
# processor = FileProcessorFactory.get_processor(file.name) # Get the correct processor
# if processor is None:
# raise ValueError(f"Unsupported file format: {file.name}")
# text = processor.extract_text(file.name) # Extract content
text = read_file(file.name) # Extract content
output = process_text(text) # Perform entity recognition
metadata = output["metadata"]
# Convert extracted data safely
highs = safe_dataframe(output["reds"], "high")
lows = safe_dataframe(output["reds"], "low")
labtests = safe_dataframe(output, "lab_tests")
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']}")
return metadata_str, highs, lows, labtests, output
# ✅ Gradio Interface
with gr.Blocks() as demo:
gr.Markdown("# 🏥 Medical Lab Report Processor")
with gr.Row():
pdf_input = gr.File(label="📂 Upload Report")
submit_btn = gr.Button("Process")
metadata_output = gr.Markdown("**Patient Name: Prashasst Dongre...**")
with gr.Row():
high_output = gr.Dataframe(label="🔺 High Values")
low_output = gr.Dataframe(label="🔻 Low Values")
lab_test_output = gr.Dataframe(label="📊 Lab Test Results")
output_JSON = gr.JSON(label="📜 Extracted Report")
submit_btn.click(show_to_UI, inputs=[pdf_input], outputs=[metadata_output, high_output, low_output, lab_test_output, output_JSON])
demo.launch()
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