app.py

import os
os.environ['TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD'] = '1'

import numpy as np
import torch
import gradio as gr
from PIL import Image
import fitz  # PyMuPDF
import torchxrayvision as xrv
from torchvision import transforms
import re

# --- Device & Model ---
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
MODEL = xrv.models.get_model("densenet121-res224-all").to(DEVICE).eval()
LABELS = MODEL.pathologies

# --- Extended Medical Information ---
DISEASE_INFO = {
    "Atelectasis": {
        "description":"Collapse of part or all of a lung, reducing oxygen exchange.",
        "cause":"Blocked airway, lung compression, post-surgery.",
        "recommendation":"Deep breathing exercises, possibly bronchoscopy or physiotherapy.",
    },
    "Cardiomegaly": {
        "description":"Enlargement of the heart, seen as a broad silhouette.",
        "cause":"High blood pressure, valve disease, cardiomyopathy.",
        "recommendation":"Echocardiogram, consult a cardiologist.",
    },
    "Consolidation": {
        "description":"Lung region filled with liquid instead of air.",
        "cause":"Often from pneumonia (bacterial or viral).",
        "recommendation":"Consult a physician; likely antibiotics and follow-up chest X-ray.",
    },
    "Edema": {
        "description":"Fluid accumulation in the lungs.",
        "cause":"Heart failure, kidney issues.",
        "recommendation":"Treat underlying cause, may need diuretics, consult cardiology.",
    },
    "Effusion": {
        "description":"Fluid buildup between lung and chest wall.",
        "cause":"Infection, heart failure, cancer.",
        "recommendation":"May need drainage (thoracentesis), see a pulmonologist.",
    },
    "Emphysema": {
        "description":"Damage and enlargement of lung air sacs (alveoli).",
        "cause":"Mainly smoking.",
        "recommendation":"Quit smoking, pulmonary rehab, inhalers.",
    },
    "Fibrosis": {
        "description":"Scarring of lung tissue, making breathing difficult.",
        "cause":"Longstanding inflammation, auto-immune disease, occupational exposure.",
        "recommendation":"Pulmonologist consult, immunosuppression/antifibrotic therapy.",
    },
    "Fracture": {
        "description":"Break/crack in a bone (commonly ribs).",
        "cause":"Trauma, accident, fall.",
        "recommendation":"Pain management, monitor for organ injury, orthopedic consult if severe.",
    },
    "Infiltration": {
        "description":"Something abnormal (cells/fluid) in the lungs.",
        "cause":"Most often infection, sometimes inflammation or cancer.",
        "recommendation":"See physician, further tests to clarify cause.",
    },
    "Mass": {
        "description":"Lump or growth seen on X-ray.",
        "cause":"Could be benign or malignant tumor.",
        "recommendation":"Consult pulmonologist or oncologist, consider CT/biopsy.",
    },
    "Nodule": {
        "description":"Small round or oval spot in the lung.",
        "cause":"Old infection, benign, or early cancer.",
        "recommendation":"May need CT and follow-up scans, discuss with doctor.",
    },
    "Pleural_Thickening": {
        "description":"Thickening of chest lining.",
        "cause":"Old infection, asbestos exposure.",
        "recommendation":"Pulmonology follow-up; rarely needs intervention.",
    },
    "Pneumonia": {
        "description":"Infection causing inflammation in the lungs.",
        "cause":"Bacteria, viruses, or fungus.",
        "recommendation":"Antibiotics/antivirals if needed. Seek prompt medical attention.",
    },
    "Pneumothorax": {
        "description":"Collapsed lung (air leaks into chest cavity).",
        "cause":"Trauma, rupture, sometimes spontaneous.",
        "recommendation":"May need emergency care to remove air; consult ER.",
    },
    # Add more if you want to extend—see LABELS for all possible findings
}

# --- Recommendations for top-line advice ---
ADVICE = {
    "Pneumonia": "Possible infection. Recommend antibiotics and pulmonology consult.",
    "Cardiomegaly": "Enlarged heart. Recommend echocardiography and cardiologist review.",
    "Effusion": "Fluid in lung space. May need thoracentesis.",
    "Fracture": "Possible bone break. Requires orthopedic consultation.",
    "Edema": "Pulmonary fluid overload. Evaluate for heart failure.",
}

def get_advice(label):
    return ADVICE.get(label, "Please consult a radiologist for further evaluation.")

def get_disease_info(label):
    d = DISEASE_INFO.get(label)
    if d:
        return (
            f"<b>{label}</b>: {d['description']}<br>"
            f"<b>Possible Causes:</b> {d['cause']}<br>"
            f"<b>Recommendation:</b> {d['recommendation']}"
        )
    return f"<b>{label}</b>: No extra info available. Please consult a radiologist."

# --- Image Preprocessing ---
def preprocess_image(pil_img: Image.Image) -> torch.Tensor:
    """Convert to grayscale, normalize, and resize for model."""
    if pil_img.mode != "L":
        pil_img = pil_img.convert("L")
    img_array = np.array(pil_img).astype(np.float32)
    img_array = xrv.datasets.normalize(img_array, 255)
    img_array = img_array[None, ...]  # [1, H, W]
    img_array = xrv.datasets.XRayCenterCrop()(img_array)
    img_array = xrv.datasets.XRayResizer(224)(img_array)
    tensor = torch.from_numpy(img_array).unsqueeze(0).to(DEVICE)
    return tensor

# --- X-ray Analysis (No CAM) ---
def analyse_xray(img: Image.Image):
    try:
        if img is None:
            return "Please upload an X-ray image.", None
        x = preprocess_image(img)
        with torch.no_grad():
            output = MODEL(x)
            probs = torch.sigmoid(output)[0] * 100
        topk = torch.topk(probs, 5)

        html = "<h3>🩺 Top 5 Predictions</h3><table border='1'><tr><th>Condition</th><th>Confidence</th><th>Details</th></tr>"
        for idx in topk.indices:
            label = LABELS[idx]
            html += (
                f"<tr><td>{label}</td>"
                f"<td>{probs[idx]:.1f}%</td>"
                f"<td>{get_disease_info(label)}</td></tr>"
            )
        html += "</table>"

        top_label = LABELS[topk.indices[0].item()]
        html += f"<br><b>Recommended Action for '{top_label}':</b> {get_advice(top_label)}"

        return html, img.resize((224, 224))
    except Exception as e:
        return f"Error processing image: {str(e)}", None

# --- Report PDF Analysis ---
def analyse_report(file):
    try:
        if file is None:
            return "Please upload a PDF report."
        doc = fitz.open(file.name)
        text = "\n".join(page.get_text() for page in doc)
        doc.close()
        found = []
        for label in LABELS:
            if re.search(rf"\b{label.lower()}\b", text.lower()):
                found.append(label)
        if found:
            html = "<h3>📃 Findings Detected in Report:</h3><ul>"
            for label in found:
                html += f"<li>{get_disease_info(label)}</li>"
            html += "</ul>"
        else:
            html = "<p>No known conditions detected from report text.</p>"
        return html
    except Exception as e:
        return f"Error processing PDF: {str(e)}"

# --- Gradio UI ---
with gr.Blocks(title="🩻 RadiologyScan AI") as demo:
    gr.Markdown(
        "## 🩻 RadiologyScan AI\n"
        "Perform fast AI-based analysis of Chest X-rays and medical reports\n"
        "<em>This tool provides informative summaries for common radiological findings. Not a substitute for a professional medical opinion.</em>"
    )
    with gr.Tabs():
        with gr.Tab("🔍 X-ray Analysis"):
            x_input = gr.Image(label="Upload Chest X-ray", type="pil")
            x_out_html = gr.HTML()
            x_out_image = gr.Image(label="Resized X-ray (224x224)")
            analyze_btn = gr.Button("Analyze X-ray")
            clear_btn = gr.Button("Clear")
            analyze_btn.click(analyse_xray, inputs=x_input, outputs=[x_out_html, x_out_image])
            clear_btn.click(lambda: (None, "", None), None, [x_input, x_out_html, x_out_image])

        with gr.Tab("📄 PDF Report Analysis"):
            pdf_input = gr.File(file_types=[".pdf"], label="Upload PDF Medical Report")
            pdf_output = gr.HTML()
            analyze_pdf_btn = gr.Button("Analyze Report")
            clear_pdf_btn = gr.Button("Clear")
            analyze_pdf_btn.click(analyse_report, inputs=pdf_input, outputs=pdf_output)
            clear_pdf_btn.click(lambda: (None, ""), None, [pdf_input, pdf_output])

if __name__ == "__main__":
    demo.launch(server_port=int(os.getenv("PORT", 7860)), show_error=True)