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| import os | |
| os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # Suppress TensorFlow warnings | |
| import gradio as gr | |
| import tensorflow as tf | |
| from tensorflow.keras.models import load_model | |
| import numpy as np | |
| from PIL import Image | |
| import requests | |
| import json | |
| # ================== MODEL LOADING ================== | |
| try: | |
| model = load_model('wound_classifier_model_googlenet.h5') | |
| print("✅ Model loaded successfully") | |
| except Exception as e: | |
| raise RuntimeError(f"❌ Model loading failed: {str(e)}") | |
| # ================== CLASS LABELS ================== | |
| CLASS_LABELS = [ | |
| "Abrasions", "Bruises", "Burns", "Cut", "Diabetic Wounds", "Gingivitis", | |
| "Surgical Wounds", "Venous Wounds", "athlete foot", "cellulitis", | |
| "chickenpox", "cutaneous larva migrans", "impetigo", "nail fungus", | |
| "ringworm", "shingles", "tooth discoloration", "ulcer" | |
| ] | |
| # Verify model compatibility | |
| assert len(CLASS_LABELS) == model.output_shape[-1], \ | |
| f"Class mismatch: Model expects {model.output_shape[-1]} classes, found {len(CLASS_LABELS)}" | |
| # ================== OPENROUTER CONFIG ================== | |
| OPENROUTER_API_KEY = "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" | |
| OPENROUTER_API_URL = "https://openrouter.ai/api/v1/chat/completions" | |
| MODEL_NAME = "mistralai/mistral-7b-instruct" | |
| # ================== IMAGE PROCESSING ================== | |
| def preprocess_image(image, target_size=(224, 224)): | |
| """Process and validate input images""" | |
| try: | |
| if not image: | |
| raise ValueError("🚨 No image provided") | |
| image = image.convert("RGB").resize(target_size) | |
| array = np.array(image) / 255.0 | |
| print(f"🖼️ Image processed: Shape {array.shape}") | |
| return array | |
| except Exception as e: | |
| raise RuntimeError(f"🖼️ Image processing failed: {str(e)}") | |
| # ================== MEDICAL GUIDELINES ================== | |
| def get_medical_guidelines(wound_type): | |
| """Fetch treatment guidelines from OpenRouter API""" | |
| headers = { | |
| "Authorization": f"Bearer {OPENROUTER_API_KEY}", | |
| "Content-Type": "application/json", | |
| "HTTP-Referer": "https://huggingface.co/spaces/MahatirTusher/Wound_Treatment" | |
| } | |
| prompt = f"""You are a medical expert providing guidance for a patient with {wound_type}. | |
| First, briefly introduce what {wound_type} are and their typical characteristics. | |
| Then, provide a comprehensive care guide including: | |
| 1. Immediate First Aid Steps | |
| 2. Home Care Instructions | |
| 3. Prevention Tips | |
| 4. Warning Signs (when to seek emergency care) | |
| Format your response in clear sections with proper spacing. Use simple, | |
| non-technical language that a general audience can understand.""" | |
| try: | |
| print(f"📡 Sending API request for {wound_type}...") | |
| response = requests.post( | |
| OPENROUTER_API_URL, | |
| headers=headers, | |
| json={ | |
| "model": MODEL_NAME, | |
| "messages": [{"role": "user", "content": prompt}], | |
| "temperature": 0.5 | |
| }, | |
| timeout=20 | |
| ) | |
| response.raise_for_status() | |
| result = response.json() | |
| print("🔧 Raw API response:", json.dumps(result, indent=2)) | |
| if not result.get("choices"): | |
| return "⚠️ API response format unexpected" | |
| return result["choices"][0]["message"]["content"] | |
| except Exception as e: | |
| return f"⚠️ Guidelines unavailable: {str(e)}" | |
| # ================== MAIN PREDICTION ================== | |
| def predict(image): | |
| """Complete prediction pipeline""" | |
| try: | |
| # Preprocess image | |
| processed_img = preprocess_image(image) | |
| input_tensor = np.expand_dims(processed_img, axis=0) | |
| # Make prediction | |
| predictions = model.predict(input_tensor)[0] | |
| sorted_indices = np.argsort(predictions)[::-1] # Descending order | |
| # Format results | |
| results = { | |
| CLASS_LABELS[i]: float(predictions[i]) | |
| for i in sorted_indices[:3] # Top 3 predictions | |
| } | |
| top_class = CLASS_LABELS[sorted_indices[0]] | |
| # Get guidelines | |
| guidelines = get_medical_guidelines(top_class) | |
| return results, guidelines | |
| except Exception as e: | |
| return {f"🚨 Error": str(e)}, "" | |
| # ================== CSS STYLES ================== | |
| custom_css = """ | |
| .gradio-container { | |
| font-family: 'Times New Roman', Times, serif !important; | |
| } | |
| .container { | |
| background: rgba(255, 255, 255, 0.9); | |
| border-radius: 20px; | |
| padding: 2rem; | |
| box-shadow: 0 8px 32px 0 rgba(31, 38, 135, 0.37); | |
| backdrop-filter: blur(4px); | |
| border: 1px solid rgba(255, 255, 255, 0.18); | |
| margin: 1rem 0; | |
| } | |
| .title { | |
| color: #2D3748; | |
| text-align: center; | |
| font-size: 2.5rem; | |
| margin-bottom: 1rem; | |
| } | |
| .subtitle { | |
| color: #4A5568; | |
| text-align: center; | |
| font-size: 1.2rem; | |
| margin-bottom: 2rem; | |
| } | |
| .warning { | |
| background-color: #FED7D7; | |
| border-left: 4px solid #F56565; | |
| padding: 1rem; | |
| margin: 1rem 0; | |
| border-radius: 8px; | |
| } | |
| .info-section { | |
| background-color: #E6F6FF; | |
| border-radius: 12px; | |
| padding: 1.5rem; | |
| margin: 1rem 0; | |
| } | |
| .guidelines-box { | |
| background: #F7FAFC; | |
| border-radius: 10px; | |
| padding: 1rem; | |
| margin-top: 1rem; | |
| } | |
| """ | |
| # ================== GRADIO INTERFACE ================== | |
| def create_interface(): | |
| with gr.Blocks(css=custom_css, title="WoundWise AI") as demo: | |
| # Logo and Introduction | |
| with gr.Column(elem_classes="container"): | |
| gr.Image("logo.png", show_label=False, container=False) | |
| gr.Markdown("# Welcome to WoundWise AI") | |
| gr.Markdown(""" | |
| In today's fast-paced world, early detection of medical conditions can be a game-changer. | |
| WoundWise AI is a cutting-edge AI-powered wound classification system developed as part | |
| of EarlyMed, an initiative by students of Vellore Institute of Technology. Our mission | |
| is simple yet impactful: "Early Detection, Smarter Decision." | |
| By leveraging deep learning, this system analyzes wound images to provide accurate | |
| classifications and essential treatment guidelines. Whether it's a minor cut or a | |
| serious skin condition, our AI assists in identifying potential risks, helping users | |
| make informed health decisions at an early stage. | |
| """) | |
| # Main Interface | |
| with gr.Column(elem_classes="container"): | |
| gr.Markdown("## 📸 Upload or Capture Wound Image") | |
| gr.Markdown("Use your device's camera or upload an existing image") | |
| file_input = gr.Image(type="pil", label="Upload Wound Image") | |
| submit_btn = gr.Button("Analyze Now", variant="primary") | |
| # Example Images | |
| gr.Examples( | |
| examples=["abrasion.jpg", "burn.png", "bruise.png", "chicken-pox.png", "cut.png"], | |
| inputs=file_input, | |
| label="Example Images" | |
| ) | |
| output_label = gr.Label(label="Analysis Results", num_top_classes=3) | |
| output_guidelines = gr.Textbox( | |
| label="Medical Guidelines", | |
| lines=12, | |
| elem_classes="guidelines-box" | |
| ) | |
| # Why Care About Wounds | |
| with gr.Column(elem_classes="info-section"): | |
| gr.Markdown(""" | |
| ## ⚠️ Why Wound Care Matters | |
| Ignoring wounds can lead to serious complications: | |
| - Risk of bacterial infection | |
| - Delayed healing and scarring | |
| - Chronic wound development | |
| - Systemic health issues | |
| - Increased medical costs | |
| Early intervention and proper care are crucial for optimal healing and preventing complications. | |
| """) | |
| # How Our Model Works | |
| with gr.Column(elem_classes="info-section"): | |
| gr.Markdown(""" | |
| ## 🔬 Our Technology | |
| WoundWise AI uses a state-of-the-art deep learning model based on the GoogLeNet | |
| architecture, trained on thousands of medical images. The system: | |
| 1. Analyzes wound images using advanced computer vision | |
| 2. Identifies 18 different types of wounds and skin conditions | |
| 3. Provides real-time classification with confidence scores | |
| 4. Generates medical guidelines using advanced AI | |
| Our model achieves high accuracy through extensive training and validation by | |
| medical professionals. | |
| """) | |
| # Disclaimer | |
| with gr.Column(elem_classes="warning"): | |
| gr.Markdown(""" | |
| ## ⚕️ Medical Disclaimer | |
| This AI system is designed to assist in preliminary wound assessment only. It is not | |
| a substitute for professional medical advice, diagnosis, or treatment. Always seek | |
| the advice of qualified healthcare providers with any questions regarding your | |
| medical condition. If you believe your wound is serious or life-threatening, | |
| please seek immediate medical attention. | |
| """) | |
| # Connect input to processing | |
| submit_btn.click( | |
| fn=predict, | |
| inputs=[file_input], | |
| outputs=[output_label, output_guidelines] | |
| ) | |
| return demo | |
| if __name__ == "__main__": | |
| iface = create_interface() | |
| iface.launch( | |
| server_name="0.0.0.0", | |
| server_port=7860, | |
| share=True | |
| ) |