app.py

import gradio as gr
import os
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas

# Logic to process the uploaded report and extract tumor details
def process_report(file):
    # Replace this with actual logic to analyze the uploaded file
    # Example output
    tumor_details = {
        "has_tumor": True,
        "tumor_type": "Glioma",
        "size": "1.2 cm",
        "location": "Right occipital lobe",
        "confidence": 0.9,
        "analysis_details": {
            "anomalous_regions": 2,
            "edge_complexity": 151,
            "tumor_score": 1,
            "region_intensities": [
                220.39,
                158.87,
                210.12,
                176.93,
                134.04,
                160.1,
                176.74,
                124.37,
                171.66
            ]
        }
    }
    return tumor_details

def generate_pdf_report(tumor_details):
    # Path to save the tumor analysis PDF report
    pdf_path = "tumor_analysis_report.pdf"

    # Create a PDF canvas
    c = canvas.Canvas(pdf_path, pagesize=letter)
    width, height = letter

    # Title
    c.setFont("Helvetica-Bold", 16)
    c.drawCentredString(width / 2, height - 50, "MRI BRAIN REPORT")

    # Patient and scan details
    c.setFont("Helvetica", 12)
    c.drawString(50, height - 100, "Patient Name: ______________________")
    c.drawString(50, height - 120, "Scan Type: MRI Brain")
    c.drawString(50, height - 140, "Date: _____________________________")

    # Findings
    c.setFont("Helvetica-Bold", 12)
    c.drawString(50, height - 180, "FINDINGS:")
    c.setFont("Helvetica", 12)
    c.drawString(70, height - 200, f"- Tumor Presence: {tumor_details['has_tumor']}")
    c.drawString(70, height - 220, f"- Tumor Type: {tumor_details['tumor_type']}")
    c.drawString(70, height - 240, f"- Location: {tumor_details['location']}")
    c.drawString(70, height - 260, f"- Size: {tumor_details['size']}")
    c.drawString(70, height - 280, f"- Confidence: {tumor_details['confidence']}")

    # Radiological Description
    c.setFont("Helvetica-Bold", 12)
    c.drawString(50, height - 320, "Radiological Description:")
    c.setFont("Helvetica", 12)

    description = []
    if tumor_details['has_tumor']:
        description.append("The tumor appears well-defined with irregular margins.")
        description.append(f"The lesion measures approximately {tumor_details['size']} and is located in the {tumor_details['location']}.")
        if tumor_details['analysis_details']['anomalous_regions'] > 0:
            description.append("There are areas of abnormal signal intensity, suggestive of hyperintense regions.")
        if tumor_details['analysis_details']['edge_complexity'] > 100:
            description.append("The lesion demonstrates heterogeneous signal patterns with irregular margins.")
        description.append("Region intensities indicate varying levels of hyperintensity, consistent with the observed tumor characteristics.")
    else:
        description.append("No evidence of tumor detected.")

    y_position = height - 340
    for line in description:
        c.drawString(70, y_position, f"• {line}")
        y_position -= 20

    # Impression
    c.setFont("Helvetica-Bold", 12)
    c.drawString(50, y_position - 20, "IMPRESSION:")
    c.setFont("Helvetica", 12)
    impression = "Features are suggestive of a low-grade glioma in the right occipital lobe. Follow-up MRI or biopsy is recommended."
    c.drawString(70, y_position - 40, impression)

    # Note
    c.setFont("Times-Italic", 10)
    c.drawString(50, y_position - 80, "NOTE:")
    c.drawString(70, y_position - 100, "This is an AI-assisted preliminary report. Final confirmation should be made by a licensed radiologist.")

    # Save the PDF
    c.save()
    return pdf_path

def generate_prescription_form():
    # Path to save the prescription form PDF
    pdf_path = "prescription_form.pdf"

    # Create a PDF canvas
    c = canvas.Canvas(pdf_path, pagesize=letter)
    width, height = letter

    # Prescription Form Title
    c.setFont("Helvetica-Bold", 16)
    c.drawCentredString(width / 2, height - 50, "PRESCRIPTION FORM")

    # Prescription Details
    c.setFont("Helvetica", 12)
    c.drawString(50, height - 100, "Patient Name: ______________________")
    c.drawString(50, height - 120, "Date: _____________________________")
    c.drawString(50, height - 140, "Prescribed By: _____________________")

    c.drawString(50, height - 180, "Medications:")
    c.drawString(70, height - 200, "1. ______________________________")
    c.drawString(70, height - 220, "2. ______________________________")
    c.drawString(70, height - 240, "3. ______________________________")

    c.drawString(50, height - 280, "Instructions:")
    c.drawString(70, height - 300, "• ______________________________")
    c.drawString(70, height - 320, "• ______________________________")

    c.drawString(50, height - 360, "Follow-up Appointment:")
    c.drawString(70, height - 380, "Date: _____________________________")

    # Save the PDF
    c.save()
    return pdf_path

# Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Tumor Report Scanner")
    gr.Markdown("Upload a scan report to extract detailed tumor analysis and generate separate PDF reports.")

    with gr.Row():
        file_input = gr.File(label="Upload Scan Report")
        analysis_output = gr.JSON(label="Tumor Analysis Results")
        report_download = gr.File(label="Download Tumor Analysis Report")
        prescription_download = gr.File(label="Download Prescription Form")

    submit_btn = gr.Button("Analyze Report")

    def analyze_and_generate(file):
        tumor_details = process_report(file)
        analysis_pdf = generate_pdf_report(tumor_details)
        prescription_pdf = generate_prescription_form()
        return tumor_details, analysis_pdf, prescription_pdf

    submit_btn.click(analyze_and_generate, inputs=file_input, outputs=[analysis_output, report_download, prescription_download])

if __name__ == "__main__":
    demo.launch()