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| import gradio as gr | |
| import torch | |
| import torch.nn.functional as F | |
| from transformers import ViTImageProcessor, ViTForImageClassification | |
| from PIL import Image | |
| import google.generativeai as genai | |
| import os | |
| import logging | |
| import asyncio | |
| from typing import Tuple, Optional | |
| import time | |
| # Configure logging | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) | |
| # Configure Gemini API | |
| GEMINI_API_KEY = os.getenv('GEMINI_API_KEY') | |
| if GEMINI_API_KEY: | |
| genai.configure(api_key=GEMINI_API_KEY) | |
| gemini_model = genai.GenerativeModel('gemini-2.0-flash-exp') | |
| else: | |
| logger.warning("GEMINI_API_KEY not found. Remedy suggestions will be disabled.") | |
| gemini_model = None | |
| # Global variables for model caching | |
| model = None | |
| processor = None | |
| # Improved crop and disease mapping | |
| CROP_DISEASE_MAPPING = { | |
| # Tomato diseases | |
| "tomato___bacterial_spot": ("Tomato", "Bacterial Spot"), | |
| "tomato___early_blight": ("Tomato", "Early Blight"), | |
| "tomato___late_blight": ("Tomato", "Late Blight"), | |
| "tomato___leaf_mold": ("Tomato", "Leaf Mold"), | |
| "tomato___septoria_leaf_spot": ("Tomato", "Septoria Leaf Spot"), | |
| "tomato___spider_mites_two_spotted_spider_mite": ("Tomato", "Two-Spotted Spider Mite"), | |
| "tomato___target_spot": ("Tomato", "Target Spot"), | |
| "tomato___tomato_yellow_leaf_curl_virus": ("Tomato", "Yellow Leaf Curl Virus"), | |
| "tomato___tomato_mosaic_virus": ("Tomato", "Mosaic Virus"), | |
| "tomato___healthy": ("Tomato", "Healthy"), | |
| # Potato diseases | |
| "potato___early_blight": ("Potato", "Early Blight"), | |
| "potato___late_blight": ("Potato", "Late Blight"), | |
| "potato___healthy": ("Potato", "Healthy"), | |
| # Apple diseases | |
| "apple___apple_scab": ("Apple", "Apple Scab"), | |
| "apple___black_rot": ("Apple", "Black Rot"), | |
| "apple___cedar_apple_rust": ("Apple", "Cedar Apple Rust"), | |
| "apple___healthy": ("Apple", "Healthy"), | |
| # Corn diseases | |
| "corn_(maize)___cercospora_leaf_spot": ("Corn/Maize", "Cercospora Leaf Spot"), | |
| "corn_(maize)___common_rust": ("Corn/Maize", "Common Rust"), | |
| "corn_(maize)___northern_leaf_blight": ("Corn/Maize", "Northern Leaf Blight"), | |
| "corn_(maize)___healthy": ("Corn/Maize", "Healthy"), | |
| # Grape diseases | |
| "grape___black_rot": ("Grape", "Black Rot"), | |
| "grape___esca_(black_measles)": ("Grape", "Esca (Black Measles)"), | |
| "grape___leaf_blight_(isariopsis_leaf_spot)": ("Grape", "Leaf Blight"), | |
| "grape___healthy": ("Grape", "Healthy"), | |
| # Bell Pepper diseases | |
| "pepper,_bell___bacterial_spot": ("Bell Pepper", "Bacterial Spot"), | |
| "pepper,_bell___healthy": ("Bell Pepper", "Healthy"), | |
| # Cherry diseases | |
| "cherry_(including_sour)___powdery_mildew": ("Cherry", "Powdery Mildew"), | |
| "cherry_(including_sour)___healthy": ("Cherry", "Healthy"), | |
| # Peach diseases | |
| "peach___bacterial_spot": ("Peach", "Bacterial Spot"), | |
| "peach___healthy": ("Peach", "Healthy"), | |
| # Strawberry diseases | |
| "strawberry___leaf_scorch": ("Strawberry", "Leaf Scorch"), | |
| "strawberry___healthy": ("Strawberry", "Healthy"), | |
| # Soybean diseases | |
| "soybean___healthy": ("Soybean", "Healthy"), | |
| # Squash diseases | |
| "squash___powdery_mildew": ("Squash", "Powdery Mildew"), | |
| # Orange diseases | |
| "orange___haunglongbing_(citrus_greening)": ("Orange", "Huanglongbing (Citrus Greening)"), | |
| # Raspberry diseases | |
| "raspberry___healthy": ("Raspberry", "Healthy"), | |
| } | |
| def load_model(): | |
| """Load the ViT model for crop disease classification""" | |
| global model, processor | |
| try: | |
| model_name = "cabrel09/crop_leaf_disease_detector" | |
| logger.info(f"Loading model: {model_name}") | |
| processor = ViTImageProcessor.from_pretrained(model_name) | |
| model = ViTForImageClassification.from_pretrained(model_name) | |
| logger.info("Model loaded successfully!") | |
| return True | |
| except Exception as e: | |
| logger.error(f"Error loading model: {str(e)}") | |
| return False | |
| def parse_label(raw_label: str) -> Tuple[str, str]: | |
| """Parse the raw model output to extract crop and disease information""" | |
| label = raw_label.lower().strip() | |
| # Check if we have a direct mapping | |
| if label in CROP_DISEASE_MAPPING: | |
| return CROP_DISEASE_MAPPING[label] | |
| # Try to parse common patterns | |
| if "___" in label: | |
| parts = label.split("___") | |
| if len(parts) >= 2: | |
| crop_part = parts[0].replace("_", " ").title() | |
| disease_part = parts[1].replace("_", " ").title() | |
| # Clean up crop names | |
| crop_part = crop_part.replace("(", "").replace(")", "") | |
| if "Corn Maize" in crop_part: | |
| crop_part = "Corn/Maize" | |
| elif "Pepper Bell" in crop_part: | |
| crop_part = "Bell Pepper" | |
| elif "Cherry Including Sour" in crop_part: | |
| crop_part = "Cherry" | |
| return crop_part, disease_part | |
| # Fallback parsing | |
| label_parts = label.replace("-", " ").replace("_", " ").split() | |
| crop_keywords = { | |
| 'tomato': 'Tomato', 'potato': 'Potato', 'apple': 'Apple', | |
| 'corn': 'Corn', 'maize': 'Corn/Maize', 'grape': 'Grape', | |
| 'pepper': 'Pepper', 'cherry': 'Cherry', 'peach': 'Peach', | |
| 'strawberry': 'Strawberry', 'soybean': 'Soybean', | |
| 'squash': 'Squash', 'orange': 'Orange', 'citrus': 'Citrus', | |
| 'raspberry': 'Raspberry', 'wheat': 'Wheat', 'rice': 'Rice' | |
| } | |
| disease_keywords = { | |
| 'blight': 'Blight', 'rust': 'Rust', 'spot': 'Spot', | |
| 'rot': 'Rot', 'mold': 'Mold', 'virus': 'Virus', | |
| 'bacterial': 'Bacterial', 'fungal': 'Fungal', | |
| 'healthy': 'Healthy', 'scab': 'Scab', 'mildew': 'Mildew' | |
| } | |
| detected_crop = "Unknown Crop" | |
| detected_disease = "Unknown Condition" | |
| for word in label_parts: | |
| for keyword, crop_name in crop_keywords.items(): | |
| if keyword in word: | |
| detected_crop = crop_name | |
| break | |
| for word in label_parts: | |
| for keyword, disease_name in disease_keywords.items(): | |
| if keyword in word: | |
| detected_disease = disease_name | |
| break | |
| if 'early' in label and 'blight' in label: | |
| detected_disease = "Early Blight" | |
| elif 'late' in label and 'blight' in label: | |
| detected_disease = "Late Blight" | |
| elif 'leaf' in label and 'spot' in label: | |
| detected_disease = "Leaf Spot" | |
| elif 'mosaic' in label: | |
| detected_disease = "Mosaic Virus" | |
| return detected_crop, detected_disease | |
| def get_detailed_disease_info(crop: str, disease: str) -> str: | |
| """Get detailed information about the specific crop disease""" | |
| if disease == "Healthy": | |
| return f"✅ **Healthy {crop}** - Your plant appears to be in good condition! Continue with proper care practices." | |
| elif "blight" in disease.lower(): | |
| return f"🍂 **{disease}** detected in **{crop}** - This is a fungal disease that causes tissue death and browning." | |
| elif "spot" in disease.lower(): | |
| return f"⚫ **{disease}** found on **{crop}** - Characterized by dark lesions on plant tissues." | |
| elif "rust" in disease.lower(): | |
| return f"🦠 **{disease}** affecting **{crop}** - Shows as orange/reddish pustules on plant surfaces." | |
| elif "virus" in disease.lower(): | |
| return f"🦠 **{disease}** in **{crop}** - Viral infection causing various symptoms including mottling and distortion." | |
| else: | |
| return f"🔍 **{disease}** detected in **{crop}** - This condition requires attention and proper management." | |
| async def get_enhanced_remedy_suggestions(crop: str, disease: str, confidence: float) -> str: | |
| """Get AI-powered remedy suggestions using Gemini API with crop-specific context""" | |
| if not gemini_model: | |
| return get_offline_remedy_suggestions(crop, disease) | |
| try: | |
| prompt = f""" | |
| As an expert plant pathologist and agricultural consultant, provide comprehensive treatment recommendations for {disease} affecting {crop} plants. | |
| The diagnosis confidence is {confidence:.1%}. | |
| Please provide a detailed response with the following sections: | |
| 1. **Immediate Actions** (2-3 urgent steps to take right now) | |
| 2. **Treatment Options**: | |
| - Organic/biological treatments | |
| - Chemical fungicides/bactericides (if needed) | |
| - Cultural control methods | |
| 3. **Prevention Strategy** (4-5 preventive measures) | |
| 4. **Monitoring & Follow-up** (what to watch for) | |
| 5. **Expected Timeline** (recovery expectations) | |
| Make it specific to {crop} cultivation and {disease} characteristics. Include product names where appropriate and be practical for farmers. | |
| Format with clear headings and bullet points for easy reading. | |
| """ | |
| response = gemini_model.generate_content( | |
| prompt, | |
| generation_config=genai.types.GenerationConfig( | |
| temperature=0.7, | |
| max_output_tokens=1200, | |
| ) | |
| ) | |
| return f"🤖 **AI-Powered Treatment Plan for {crop} - {disease}:**\n\n{response.text}" | |
| except Exception as e: | |
| logger.error(f"Error getting AI remedy suggestions: {str(e)}") | |
| return get_offline_remedy_suggestions(crop, disease) | |
| def get_offline_remedy_suggestions(crop: str, disease: str) -> str: | |
| """Fallback offline remedy suggestions""" | |
| return f""" | |
| **📋 General Treatment Approach for {crop} - {disease}:** | |
| **🚨 Immediate Actions:** | |
| • Identify and remove affected plant parts | |
| • Improve growing conditions (drainage, spacing, air circulation) | |
| • Apply appropriate treatment based on disease type | |
| **🧪 Treatment Options:** | |
| • For fungal diseases: Use fungicides (copper-based for organic) | |
| • For bacterial diseases: Copper compounds or antibiotics | |
| • For viral diseases: Remove infected plants, control insect vectors | |
| **🛡️ Prevention Strategy:** | |
| • Choose resistant varieties when available | |
| • Practice crop rotation | |
| • Maintain proper plant hygiene | |
| • Monitor regularly for early detection | |
| **📞 Recommendation:** Consult local agricultural extension services for region-specific advice. | |
| """ | |
| def classify_disease_enhanced(image: Image.Image) -> Tuple[str, str, float, str]: | |
| """Enhanced disease classification with better parsing""" | |
| if model is None or processor is None: | |
| return "Unknown Crop", "Error", 0.0, "Model not loaded properly." | |
| try: | |
| inputs = processor(image, return_tensors="pt") | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| probabilities = F.softmax(outputs.logits, dim=-1) | |
| confidence = torch.max(probabilities).item() | |
| predicted_class_id = outputs.logits.argmax().item() | |
| predicted_label = model.config.id2label[predicted_class_id] | |
| crop, disease = parse_label(predicted_label) | |
| disease_info = get_detailed_disease_info(crop, disease) | |
| logger.info(f"Raw prediction: {predicted_label}") | |
| logger.info(f"Parsed: Crop={crop}, Disease={disease}, Confidence={confidence:.2%}") | |
| return crop, disease, confidence, disease_info | |
| except Exception as e: | |
| logger.error(f"Error in classification: {str(e)}") | |
| return "Unknown Crop", "Error", 0.0, f"Classification failed: {str(e)}" | |
| async def process_image_enhanced(image: Image.Image) -> Tuple[str, str]: | |
| """Enhanced image processing with better crop and disease identification""" | |
| if image is None: | |
| return "⚠️ No image provided", "" | |
| crop, disease, confidence, disease_info = classify_disease_enhanced(image) | |
| if disease == "Error": | |
| return f"❌ **Classification Error**\n{disease_info}", "" | |
| confidence_emoji = "🎯" if confidence > 0.8 else "📊" if confidence > 0.6 else "⚠️" | |
| main_result = f""" | |
| ## 🔬 **Plant Disease Analysis Results** | |
| ### 🌱 **Identified Crop:** `{crop}` | |
| ### 🦠 **Disease Status:** `{disease}` | |
| ### 📊 **Confidence Score:** {confidence_emoji} `{confidence:.2%}` | |
| --- | |
| ### 📋 **Disease Information:** | |
| {disease_info} | |
| ### 🔍 **Analysis Notes:** | |
| - Higher confidence scores (>80%) indicate more reliable identification | |
| - Multiple factors including image quality affect accuracy | |
| - Consider consulting agricultural experts for critical decisions | |
| --- | |
| """.strip() | |
| remedy_text = await get_enhanced_remedy_suggestions(crop, disease, confidence) | |
| return main_result, remedy_text | |
| # Optimized CSS - Functional and space-efficient | |
| optimized_css = """ | |
| @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600&display=swap'); | |
| :root { | |
| --primary-green: #7FB069; | |
| --light-green: #A7C957; | |
| --dark-green: #6A994E; | |
| --accent-green: #95D5B2; | |
| --bg-dark: #1a2e1a; | |
| --text-light: #F8F9F0; | |
| --glass-bg: rgba(26, 46, 26, 0.25); | |
| } | |
| .gradio-container { | |
| background: linear-gradient(135deg, var(--bg-dark) 0%, #2d4a2d 50%, var(--bg-dark) 100%); | |
| min-height: 100vh; | |
| font-family: 'Inter', sans-serif; | |
| padding: 0 !important; | |
| } | |
| /* COMPACT NAVBAR - Minimal space usage */ | |
| .compact-navbar { | |
| background: var(--glass-bg); | |
| backdrop-filter: blur(15px); | |
| padding: 0.8rem 2rem; | |
| border-bottom: 1px solid rgba(183, 228, 199, 0.15); | |
| display: flex; | |
| justify-content: space-between; | |
| align-items: center; | |
| position: sticky; | |
| top: 0; | |
| z-index: 1000; | |
| height: 60px; | |
| } | |
| .navbar-title { | |
| background: linear-gradient(135deg, var(--accent-green), var(--primary-green)); | |
| -webkit-background-clip: text; | |
| -webkit-text-fill-color: transparent; | |
| font-size: 1.4rem; | |
| font-weight: 600; | |
| margin: 0; | |
| } | |
| .navbar-subtitle { | |
| color: rgba(248, 249, 240, 0.7); | |
| font-size: 0.85rem; | |
| margin: 0; | |
| } | |
| /* MAIN CONTENT - Maximum space utilization */ | |
| .main-container { | |
| display: flex; | |
| height: calc(100vh - 60px); | |
| gap: 1rem; | |
| padding: 1rem; | |
| } | |
| /* LEFT PANEL - Compact upload area */ | |
| .input-panel { | |
| background: var(--glass-bg); | |
| backdrop-filter: blur(15px); | |
| border-radius: 12px; | |
| padding: 1.5rem; | |
| border: 1px solid rgba(183, 228, 199, 0.1); | |
| width: 320px; | |
| min-width: 320px; | |
| display: flex; | |
| flex-direction: column; | |
| } | |
| .input-panel h3 { | |
| color: var(--accent-green); | |
| font-size: 1.1rem; | |
| margin: 0 0 1rem 0; | |
| font-weight: 500; | |
| } | |
| /* COMPACT UPLOAD - Functional size */ | |
| .compact-upload { | |
| border: 2px dashed var(--primary-green) !important; | |
| border-radius: 8px !important; | |
| background: rgba(127, 176, 105, 0.05) !important; | |
| height: 200px !important; | |
| transition: all 0.2s ease !important; | |
| margin-bottom: 1rem !important; | |
| } | |
| .compact-upload:hover { | |
| border-color: var(--light-green) !important; | |
| background: rgba(127, 176, 105, 0.1) !important; | |
| } | |
| /* RIGHT PANEL - MAXIMUM SPACE FOR RESULTS */ | |
| .results-panel { | |
| background: var(--glass-bg); | |
| backdrop-filter: blur(15px); | |
| border-radius: 12px; | |
| border: 1px solid rgba(183, 228, 199, 0.1); | |
| flex: 1; | |
| display: flex; | |
| flex-direction: column; | |
| overflow: hidden; | |
| } | |
| .results-header { | |
| padding: 1rem 1.5rem 0.5rem; | |
| border-bottom: 1px solid rgba(183, 228, 199, 0.1); | |
| background: rgba(127, 176, 105, 0.05); | |
| } | |
| .results-header h3 { | |
| color: var(--accent-green); | |
| font-size: 1.2rem; | |
| margin: 0; | |
| font-weight: 600; | |
| } | |
| /* SCROLLABLE RESULTS - Key improvement */ | |
| .scrollable-results { | |
| flex: 1; | |
| overflow-y: auto; | |
| padding: 0; | |
| max-height: calc(100vh - 200px); | |
| } | |
| .result-content { | |
| padding: 1.5rem; | |
| color: var(--text-light); | |
| line-height: 1.6; | |
| height: 100%; | |
| } | |
| /* Custom scrollbar */ | |
| .scrollable-results::-webkit-scrollbar { | |
| width: 8px; | |
| } | |
| .scrollable-results::-webkit-scrollbar-track { | |
| background: rgba(127, 176, 105, 0.1); | |
| border-radius: 4px; | |
| } | |
| .scrollable-results::-webkit-scrollbar-thumb { | |
| background: var(--primary-green); | |
| border-radius: 4px; | |
| } | |
| .scrollable-results::-webkit-scrollbar-thumb:hover { | |
| background: var(--light-green); | |
| } | |
| /* STATUS INDICATORS */ | |
| .status-indicator { | |
| display: inline-block; | |
| padding: 0.25rem 0.75rem; | |
| border-radius: 20px; | |
| font-size: 0.8rem; | |
| font-weight: 500; | |
| } | |
| .status-analyzing { | |
| background: rgba(167, 201, 87, 0.2); | |
| color: var(--light-green); | |
| } | |
| .status-ready { | |
| background: rgba(149, 213, 178, 0.2); | |
| color: var(--accent-green); | |
| } | |
| .status-error { | |
| background: rgba(255, 99, 99, 0.2); | |
| color: #ff9999; | |
| } | |
| /* MOBILE RESPONSIVE */ | |
| @media (max-width: 768px) { | |
| .main-container { | |
| flex-direction: column; | |
| height: auto; | |
| min-height: calc(100vh - 60px); | |
| } | |
| .input-panel { | |
| width: 100%; | |
| min-width: auto; | |
| } | |
| .compact-upload { | |
| height: 150px !important; | |
| } | |
| .navbar-title { | |
| font-size: 1.2rem; | |
| } | |
| .scrollable-results { | |
| max-height: 400px; | |
| } | |
| } | |
| /* Tab improvements */ | |
| .tab-nav button { | |
| background: rgba(127, 176, 105, 0.1) !important; | |
| border: 1px solid rgba(127, 176, 105, 0.2) !important; | |
| color: var(--text-light) !important; | |
| } | |
| .tab-nav button.selected { | |
| background: var(--primary-green) !important; | |
| color: var(--bg-dark) !important; | |
| } | |
| /* Markdown content improvements */ | |
| .result-content h1, .result-content h2, .result-content h3 { | |
| color: var(--accent-green); | |
| margin-top: 1.5rem; | |
| margin-bottom: 1rem; | |
| } | |
| .result-content h1:first-child, .result-content h2:first-child, .result-content h3:first-child { | |
| margin-top: 0; | |
| } | |
| .result-content code { | |
| background: rgba(127, 176, 105, 0.2); | |
| padding: 0.2rem 0.4rem; | |
| border-radius: 4px; | |
| color: var(--light-green); | |
| } | |
| .result-content strong { | |
| color: var(--accent-green); | |
| } | |
| .result-content ul, .result-content ol { | |
| padding-left: 1.5rem; | |
| } | |
| .result-content li { | |
| margin: 0.5rem 0; | |
| } | |
| """ | |
| # Initialize the model | |
| logger.info("Initializing Enhanced Crop Disease Detector...") | |
| model_loaded = load_model() | |
| def create_optimized_interface(): | |
| """Space-optimized interface focusing on AI results""" | |
| with gr.Blocks(css=optimized_css, theme=gr.themes.Soft(), title="🌱 AgriVision AI") as interface: | |
| # COMPACT NAVBAR instead of large header | |
| gr.HTML(""" | |
| <div class="compact-navbar"> | |
| <div> | |
| <h1 class="navbar-title">🌱 AgriVision AI</h1> | |
| <p class="navbar-subtitle">Advanced Crop Disease Detection</p> | |
| </div> | |
| <div class="status-indicator status-ready">Ready</div> | |
| </div> | |
| """) | |
| if not model_loaded: | |
| gr.Warning("⚠️ Model failed to load. Please refresh the page or contact support.") | |
| return interface | |
| # MAIN CONTAINER - Maximized space | |
| with gr.Row(elem_classes="main-container"): | |
| # LEFT PANEL - Compact input area | |
| with gr.Column(scale=0, min_width=320, elem_classes="input-panel"): | |
| gr.HTML("<h3>📤 Upload Plant Image</h3>") | |
| # COMPACT UPLOAD - Functional size only | |
| image_input = gr.Image( | |
| type="pil", | |
| label="", | |
| elem_classes="compact-upload", | |
| show_label=False, | |
| height=200 | |
| ) | |
| # Quick info - minimal space | |
| gr.HTML(""" | |
| <div style="font-size: 0.85rem; color: rgba(248, 249, 240, 0.7); line-height: 1.4; margin-top: 1rem;"> | |
| 💡 <strong>Tips:</strong><br> | |
| • Clear, well-lit images work best<br> | |
| • Focus on affected plant parts<br> | |
| • Analysis starts automatically on upload<br> | |
| • Supports 38+ crop-disease combinations | |
| </div> | |
| """) | |
| # RIGHT PANEL - MAXIMUM SPACE for results | |
| with gr.Column(scale=1, elem_classes="results-panel"): | |
| # Results header | |
| gr.HTML(""" | |
| <div class="results-header"> | |
| <h3>🔬 AI Analysis Results</h3> | |
| </div> | |
| """) | |
| # TABBED SCROLLABLE RESULTS | |
| with gr.Tabs(): | |
| with gr.TabItem("🎯 Disease Classification", elem_id="classification-tab"): | |
| disease_output = gr.Markdown( | |
| elem_classes=["scrollable-results", "result-content"], | |
| show_label=False | |
| ) | |
| with gr.TabItem("💊 Treatment Plan", elem_id="treatment-tab"): | |
| remedy_output = gr.Markdown( | |
| elem_classes=["scrollable-results", "result-content"], | |
| show_label=False | |
| ) | |
| with gr.TabItem("📊 Model Details", elem_id="details-tab"): | |
| model_details = gr.Markdown( | |
| elem_classes=["scrollable-results", "result-content"], | |
| show_label=False, | |
| value=""" | |
| **🤖 Model Information:** | |
| **Architecture:** Vision Transformer (ViT) | |
| **Model:** cabrel09/crop_leaf_disease_detector | |
| **Dataset:** PlantVillage Dataset | |
| **Classes Supported:** 04 crop-disease combinations | |
| **Input Resolution:** 224x224 pixels | |
| **Processing Time:** ~2-3 seconds | |
| **Supported Crops:** | |
| - Corn/Maize, Rice | |
| - Potato, Wheat | |
| **Disease Categories:** | |
| - Fungal diseases (Blight, Rust, Mold) | |
| - Bacterial infections (Bacterial Spot) | |
| - Viral infections (Mosaic Virus, YLCV) | |
| - Healthy plant detection | |
| **Usage Instructions:** | |
| 1. Upload a clear image of the affected plant | |
| 2. Ensure good lighting and focus on diseased areas | |
| 3. Review the classification results and confidence score | |
| 4. Follow the AI-generated treatment recommendations | |
| 5. Consult agricultural experts for critical decisions | |
| **Note:** Results are based on visual patterns learned from thousands of plant images. Higher confidence scores (>80%) indicate more reliable identification. | |
| """ | |
| ) | |
| # Enhanced analysis function with model details | |
| async def enhanced_analysis(image): | |
| if image is None: | |
| return ( | |
| "⚠️ **No Image Uploaded**\n\nPlease upload a plant image to begin analysis.", | |
| "**🔄 Waiting for Image Upload**\n\nOnce you upload an image, the AI will:\n\n1. Process the image through the Vision Transformer\n2. Classify the crop and disease\n3. Generate treatment recommendations\n4. Provide confidence scores and analysis details", | |
| ) | |
| try: | |
| # Your existing classification logic here | |
| disease_result, remedy_result = await process_image_enhanced(image) | |
| return disease_result, remedy_result | |
| except Exception as e: | |
| error_msg = f"❌ **Analysis Error**\n\n{str(e)}\n\nPlease try uploading a different image or check the image format." | |
| return error_msg, "**Error Details:**\n\nThe analysis could not be completed. Common issues:\n- Unsupported image format\n- Image too small or unclear\n- Network connectivity issues\n\nPlease try again with a clear, well-lit plant image." | |
| # Event handlers | |
| image_input.change( | |
| fn=enhanced_analysis, | |
| inputs=[image_input], | |
| outputs=[disease_output, remedy_output] | |
| ) | |
| return interface | |
| # Launch with optimized settings | |
| if __name__ == "__main__": | |
| interface = create_optimized_interface() | |
| interface.launch( | |
| share=False, | |
| server_name="0.0.0.0", | |
| server_port=7860, | |
| show_error=True, | |
| debug=True | |
| ) | |