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| import torch | |
| import torch.nn as nn | |
| from torchvision import transforms, models | |
| import gradio as gr | |
| from PIL import Image | |
| import numpy as np | |
| from huggingface_hub import InferenceClient | |
| import os | |
| # Copyright 2025 Muhammad Zeeshan Burki | |
| # Licensed under the Apache License, Version 2.0... | |
| class KidneyClassifier(nn.Module): | |
| def __init__(self, num_classes=4): | |
| super(KidneyClassifier, self).__init__() | |
| self.backbone = models.resnet50(pretrained=False) | |
| num_features = self.backbone.fc.in_features | |
| self.backbone.fc = nn.Sequential( | |
| nn.Dropout(0.5), | |
| nn.Linear(num_features, 512), | |
| nn.ReLU(), | |
| nn.Dropout(0.3), | |
| nn.Linear(512, num_classes) | |
| ) | |
| def forward(self, x): | |
| return self.backbone(x) | |
| def load_model(): | |
| device = torch.device('cpu') | |
| model = KidneyClassifier(num_classes=4) | |
| model.load_state_dict(torch.load('best_kidney_model.pth', map_location=device)) | |
| model.eval() | |
| return model, device | |
| model, device = load_model() | |
| class_names = ['Normal', 'Cyst', 'Tumor', 'Stone'] | |
| class MedicalAssistant: | |
| def __init__(self): | |
| try: | |
| self.client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") | |
| except: | |
| self.client = None | |
| self.prevention_methods = { | |
| 'Cyst': [ | |
| "Stay hydrated by drinking plenty of water", | |
| "Maintain a balanced diet low in sodium", | |
| "Regular medical check-ups", | |
| "Avoid excessive protein intake", | |
| "Monitor blood pressure regularly" | |
| ], | |
| 'Tumor': [ | |
| "Avoid smoking and tobacco products", | |
| "Maintain a healthy weight", | |
| "Eat a diet rich in fruits and vegetables", | |
| "Limit processed and red meat consumption", | |
| "Regular exercise and physical activity" | |
| ], | |
| 'Stone': [ | |
| "Drink plenty of water (8-10 glasses daily)", | |
| "Limit sodium and oxalate-rich foods", | |
| "Reduce animal protein consumption", | |
| "Maintain adequate calcium intake", | |
| "Avoid excessive vitamin C supplements" | |
| ], | |
| 'Normal': [ | |
| "Continue healthy lifestyle habits", | |
| "Regular hydration", | |
| "Balanced nutrition", | |
| "Regular exercise", | |
| "Routine medical check-ups" | |
| ] | |
| } | |
| def generate_summary(self, prediction, confidence): | |
| if self.client: | |
| prompt = f"""You are a medical AI assistant. A CT kidney scan shows: {prediction} with {confidence:.2%} confidence. | |
| Provide a brief, compassionate explanation and next steps. Keep it under 150 words.""" | |
| try: | |
| response = self.client.text_generation( | |
| prompt, | |
| max_new_tokens=150, | |
| temperature=0.7 | |
| ) | |
| return response | |
| except: | |
| pass | |
| return f"The analysis suggests {prediction}. Please consult with your healthcare provider for proper diagnosis and treatment recommendations." | |
| def get_prevention_methods(self, prediction): | |
| return self.prevention_methods.get(prediction, []) | |
| medical_assistant = MedicalAssistant() | |
| def predict_kidney_condition(image): | |
| if image is None: | |
| return "Please upload an image first.", {} | |
| try: | |
| transform = transforms.Compose([ | |
| transforms.Resize((224, 224)), | |
| transforms.ToTensor(), | |
| transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) | |
| ]) | |
| if isinstance(image, np.ndarray): | |
| image = Image.fromarray(image) | |
| image_tensor = transform(image).unsqueeze(0).to(device) | |
| with torch.no_grad(): | |
| outputs = model(image_tensor) | |
| probabilities = torch.nn.functional.softmax(outputs[0], dim=0) | |
| confidence, predicted = torch.max(probabilities, 0) | |
| predicted_class = class_names[predicted.item()] | |
| confidence_score = confidence.item() | |
| summary = medical_assistant.generate_summary(predicted_class, confidence_score) | |
| prevention_methods = medical_assistant.get_prevention_methods(predicted_class) | |
| prob_dict = {class_names[i]: float(probabilities[i]) for i in range(len(class_names))} | |
| result_text = f"""## 🏥 Analysis Results | |
| **Predicted Condition:** {predicted_class} | |
| **Confidence:** {confidence_score:.1%} | |
| ## 📋 AI Summary | |
| {summary} | |
| ## 🛡️ Prevention & Management Tips | |
| """ | |
| for i, method in enumerate(prevention_methods, 1): | |
| result_text += f"\n{i}. {method}" | |
| result_text += "\n\n⚠️ **Disclaimer:** This is an AI analysis tool for educational purposes only. Always consult healthcare professionals for medical advice." | |
| return result_text, prob_dict | |
| except Exception as e: | |
| return f"Error processing image: {str(e)}", {} | |
| def create_app(): | |
| with gr.Blocks(title="CT Kidney Analysis", theme=gr.themes.Soft()) as demo: | |
| gr.Markdown(""" | |
| # 🫘 CT Kidney Analysis System | |
| Upload a CT kidney scan to get AI-powered analysis with medical insights. | |
| **Detects:** Normal | Cyst | Tumor | Kidney Stone | |
| """) | |
| with gr.Row(): | |
| with gr.Column(): | |
| image_input = gr.Image( | |
| label="📤 Upload CT Kidney Scan", | |
| type="pil", | |
| height=350 | |
| ) | |
| analyze_btn = gr.Button( | |
| "🔍 Analyze Scan", | |
| variant="primary", | |
| size="lg" | |
| ) | |
| gr.Markdown("### 📋 Instructions:") | |
| gr.Markdown(""" | |
| 1. Upload a clear CT kidney scan image | |
| 2. Click 'Analyze Scan' | |
| 3. Review results and recommendations | |
| 4. **Always consult medical professionals** | |
| """) | |
| with gr.Column(): | |
| result_output = gr.Markdown( | |
| value="👆 Upload an image and click 'Analyze Scan' to begin" | |
| ) | |
| confidence_output = gr.Label( | |
| label="📊 Confidence Scores", | |
| num_top_classes=4 | |
| ) | |
| analyze_btn.click( | |
| fn=predict_kidney_condition, | |
| inputs=image_input, | |
| outputs=[result_output, confidence_output] | |
| ) | |
| gr.Markdown(""" | |
| --- | |
| ### ⚕️ Medical Disclaimer | |
| This AI system is for **educational and research purposes only**. It should not replace professional medical advice, diagnosis, or treatment. | |
| ### 🔬 Model Information | |
| - **Architecture:** ResNet-50 with Transfer Learning | |
| - **Training Data:** CT Kidney Dataset (Normal, Cyst, Tumor, Stone) | |
| - **AI Assistant:** HuggingFace Zephyr-7B-Beta for summaries | |
| - **Made By:** Muhammad Zeeshan Burki | |
| """) | |
| return demo | |
| if __name__ == "__main__": | |
| app = create_app() | |
| app.launch() | |