""" Gradio Web UI for Fish Disease Detection Enhanced with Gemini-powered treatment and Grad-CAM explainability """ # Load environment variables from .env file (for local development) try: from dotenv import load_dotenv load_dotenv() print("✅ Environment variables loaded from .env") except ImportError: print("⚠️ python-dotenv not installed (OK for production deployment)") import gradio as gr import google.generativeai as genai from PIL import Image from backend import config as cfg from backend.predictor import FishDiseasePredictor from backend.treatment import TreatmentGenerator from backend.gradcam import generate_gradcam_visualization # ==================== GEMINI SETUP FOR TREATMENT ==================== treatment_gemini_model = None if cfg.GEMINI_API_KEY: try: genai.configure(api_key=cfg.GEMINI_API_KEY) treatment_gemini_model = genai.GenerativeModel(cfg.GEMINI_MODEL_NAME) print("✅ Gemini AI enabled for treatment recommendations") except Exception as e: print(f"❌ Gemini setup failed: {e}") treatment_gemini_model = None else: print("⚠️ Gemini API key not found. Treatment recommendations will be limited.") # Initialize treatment generator treatment_generator = TreatmentGenerator(treatment_gemini_model) # Initialize predictor config_dict = { 'CLASSES': cfg.CLASSES, 'MODEL_PATH': cfg.MODEL_PATH, 'DEVICE': cfg.DEVICE, 'CONFIDENCE_THRESHOLD': cfg.CONFIDENCE_THRESHOLD, 'MAX_FILE_SIZE_MB': cfg.MAX_FILE_SIZE_MB, 'MIN_IMAGE_SIZE_PX': cfg.MIN_IMAGE_SIZE_PX, 'VALID_EXTENSIONS': cfg.VALID_EXTENSIONS } predictor = FishDiseasePredictor(config_dict, gemini_model=None) def predict_fish_disease(image): """ Main prediction function with Grad-CAM visualization Args: image: PIL Image from Gradio Returns: tuple: (result_text, probability_chart, treatment_text, gradcam_image) """ if image is None: return "⚠️ Please upload an image", None, "", None # Run prediction result = predictor.predict_from_image(image) if not result['success']: return f"❌ {result['error']}", None, "", None pred = result['prediction'] disease = pred['disease'].replace('_', ' ') confidence = pred['confidence'] display_confidence = min(confidence, 100.0) # Get sorted probabilities sorted_probs = sorted(pred['probabilities'].items(), key=lambda x: x[1], reverse=True) # GENERATE GRAD-CAM VISUALIZATION gradcam_image = None try: predicted_class_idx = cfg.CLASSES.index(pred['disease']) model = predictor.model_loader.model transform = predictor.model_loader.transform gradcam_image = generate_gradcam_visualization( model, image, predicted_class_idx, transform ) except Exception as e: print(f"⚠️ Grad-CAM generation failed: {e}") gradcam_image = image # Fallback to original # ENHANCED: Better handling for low confidence cases if pred['below_threshold']: result_text = f""" ## 🐟 Prediction Results **Status:** ❓ **Uncertain - Low Confidence Detection** ⚠️ **Below confidence threshold ({cfg.CONFIDENCE_THRESHOLD}%)** The model detected possible disease signs but cannot confidently identify the specific disease. ### 📊 Most Likely Candidates: 1. **{sorted_probs[0][0].replace('_', ' ')}**: {sorted_probs[0][1]:.1f}% 2. **{sorted_probs[1][0].replace('_', ' ')}**: {sorted_probs[1][1]:.1f}% 3. **{sorted_probs[2][0].replace('_', ' ')}**: {sorted_probs[2][1]:.1f}% ### 💡 Recommended Actions: - Upload a **clearer, well-lit** image if available - Capture the fish from **different angles** - **Consult a fish health professional** for accurate diagnosis - **Monitor the fish** closely for symptom changes """ ai_treatment = treatment_generator.get_recommendations(pred['disease'], display_confidence) treatment_text = f"""### ⚠️ Low Confidence Warning ({display_confidence:.1f}%) Due to uncertain diagnosis, these are **general guidelines** for the top candidate: --- {ai_treatment} --- ### 🔴 CRITICAL REMINDER: This diagnosis has **low confidence** and should **NOT** be used for treatment decisions without professional confirmation. **Next Steps:** 1. Get a professional veterinary assessment 2. Document symptoms with photos/video 3. Monitor water quality parameters 4. Isolate affected fish if condition worsens""" else: status_emoji = "✅" if pred['disease'] == 'Healthy_Fish' else "⚠️" status_text = "Healthy" if pred['disease'] == 'Healthy_Fish' else "Diseased" result_text = f""" ## 🐟 Prediction Results **Disease:** {disease} **Confidence:** {display_confidence:.2f}% **Status:** {status_emoji} {status_text} {"✅ High confidence detection - Results are reliable" if confidence >= 80 else ""} """ treatment_text = treatment_generator.get_recommendations(pred['disease'], display_confidence) # Convert probabilities for Gradio prob_data = { disease_name: prob / 100.0 for disease_name, prob in pred['probabilities'].items() } return result_text, prob_data, treatment_text, gradcam_image # ==================== GRADIO INTERFACE ==================== with gr.Blocks(title="Fish Disease Detection", theme=gr.themes.Soft()) as demo: gr.Markdown(""" # 🐟 Fish Disease Detection System ### AI-Powered Fish Health Diagnosis using VGG16 CNN with Explainable AI Upload a fish image to detect diseases and see **how the AI made its decision** with heatmap visualization. """) with gr.Row(): with gr.Column(scale=1): image_input = gr.Image(type="pil", label="Upload Fish Image", height=400) predict_btn = gr.Button("🔬 Analyze Fish", variant="primary", size="lg") gr.Markdown(""" ### 📋 Instructions: 1. Upload a **clear fish image** 2. Click **'Analyze Fish'** 3. View **results, AI explanation, and treatment** **Supported:** JPG, PNG (Max 10MB) ### 💡 Tips for Best Results: - Use **well-lit** images - Show the **whole fish** clearly - Avoid **blurry** or **obstructed** shots """) with gr.Column(scale=1): result_output = gr.Markdown(label="Results") prob_output = gr.Label(label="Disease Probabilities", num_top_classes=8) # Grad-CAM Visualization Row with gr.Row(): gradcam_output = gr.Image( label="🔍 AI Decision Heatmap - Shows which areas the model focused on for diagnosis (Red = High importance)", type="pil", height=400 ) with gr.Row(): treatment_output = gr.Textbox( label="💊 Treatment Recommendations", lines=15, max_lines=25 ) # Example images gr.Examples( examples=[ ["data/merged_dataset-all/test/Healthy_Fish/Healthy_Fish_1__1.jpg"], ["data/merged_dataset-all/test/Bacterial_gill_disease/Bacterial_gill_disease_1__1.jpg"], ], inputs=image_input, label="📸 Example Images" ) gr.Markdown(""" --- ### 📊 Model Information - **Architecture:** VGG16 CNN with Transfer Learning - **Test Accuracy:** 98.65% - **Training Dataset:** 5,000+ annotated images - **Disease Classes:** 8 diseases + Healthy - **Confidence Threshold:** 70% (for reliable diagnosis) - **Inference Time:** ~2-3 seconds - **AI Treatment:** Powered by Google Gemini 2.0 - **Explainability:** Grad-CAM visualization ### 🎯 Confidence Levels: - **≥ 80%**: High confidence - Very reliable - **70-79%**: Good confidence - Reliable - **< 70%**: Low confidence - Requires verification ### 🔬 Understanding the Heatmap: - **🔴 Red areas**: Model focused here (disease symptoms, lesions, abnormalities) - **🟡 Yellow areas**: Moderate importance - **🟢 Green/Blue areas**: Less important for diagnosis The heatmap shows the AI is making decisions based on actual disease features, not random patterns. --- ### ⚠️ Medical Disclaimer This is an **AI diagnostic tool** for preliminary screening only. **Always consult a qualified aquaculture veterinarian for:** - Professional diagnosis confirmation - Treatment plan approval - Medication dosage recommendations - Emergency health situations This tool is intended to **assist**, not **replace** professional veterinary care. """) # Connect button with 4 outputs predict_btn.click( fn=predict_fish_disease, inputs=image_input, outputs=[result_output, prob_output, treatment_output, gradcam_output] ) # Launch if __name__ == "__main__": demo.launch( server_name="0.0.0.0", server_port=7860 )