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 re
import fitz  # PyMuPDF
from torchvision import transforms

# ==============================
# CONFIG
# ==============================
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Your 23 custom labels
LABELS = [
    "Pneumonia", "Tuberculosis", "Lung Cancer", "Pulmonary Fibrosis",
    "COPD", "COVID-19 lung infection", "Pleural Effusion", "Atelectasis",
    "Cardiomegaly", "Rib Fracture", "Spinal Fracture", "Osteoporosis",
    "Arthritis", "Bone Tumor", "Scoliosis", "Dental Caries",
    "Impacted Tooth", "Jaw Fracture", "Intestinal Obstruction",
    "Kidney Stone", "Gallstone", "Foreign Object"
]

# Disease info (examples, can be extended)
DISEASE_INFO = {
    "Pneumonia": {
        "description": "Infection of the lung causing inflammation.",
        "cause": "Bacterial, viral, or fungal pathogens.",
        "recommendation": "Consult a doctor, antibiotics/antivirals if confirmed."
    },
    "Tuberculosis": {
        "description": "Bacterial infection by Mycobacterium tuberculosis.",
        "cause": "Airborne spread from infected person.",
        "recommendation": "Seek TB specialist, long-term antibiotics."
    },
    # ... add info for all 23 labels ...
}

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."

# ==============================
# MODEL LOADING
# ==============================
# Replace this with your own Hugging Face model ID
MODEL_ID = "your-username/your-xray-multilabel-model"

# Example: assume model is a torch.nn.Module with sigmoid output
# The model should accept [1, 3, 224, 224] tensor and output [1, len(LABELS)]
model = torch.hub.load("pytorch/vision", "resnet18", pretrained=False)
model.fc = torch.nn.Linear(model.fc.in_features, len(LABELS))
model.load_state_dict(torch.load("model_weights.pth", map_location=DEVICE))
model.to(DEVICE).eval()

# ==============================
# IMAGE PREPROCESSING
# ==============================
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.Grayscale(num_output_channels=3),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5]*3, std=[0.5]*3)
])

def preprocess_image(img: Image.Image) -> torch.Tensor:
    return transform(img).unsqueeze(0).to(DEVICE)

# ==============================
# XRAY ANALYSIS
# ==============================
def analyse_xray(img: Image.Image):
    if img is None:
        return "Please upload an X-ray image.", None
    try:
        x = preprocess_image(img)
        with torch.no_grad():
            outputs = model(x)
            probs = torch.sigmoid(outputs)[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>"
        return html, img.resize((224, 224))
    except Exception as e:
        return f"Error processing image: {str(e)}", None

# ==============================
# PDF REPORT ANALYSIS
# ==============================
def analyse_report(file):
    if file is None:
        return "Please upload a PDF report."
    try:
        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="🩻 Multi-Xray AI") as demo:
    gr.Markdown(
        "## 🩻 Multi-Xray AI\n"
        "Detect and classify 23 different medical conditions from various X-ray types."
    )
    with gr.Tabs():
        with gr.Tab("🔍 X-ray Analysis"):
            x_input = gr.Image(label="Upload X-ray", type="pil")
            x_out_html = gr.HTML()
            x_out_image = gr.Image(label="Resized (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)