Create app.py

app.py

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+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)