adjusted main

disease_guide.json

@@ -1,5 +1,5 @@
 {
-  "Apple___Apple_scab": {
+  "0": {
     "disease_name": "Apple Scab",
     "common_names": ["Apple scab"],
     "crop": "Apple",
@@ -34,7 +34,7 @@
       }
     ]
   },
-  "Apple___Black_rot": {
+  "1": {
     "disease_name": "Black Rot",
     "common_names": ["Apple black rot"],
     "crop": "Apple",
@@ -60,7 +60,7 @@
     "type": "Fungal",
     "external_resources": []
   },
-  "Apple___Cedar_apple_rust": {
+  "2": {
     "disease_name": "Cedar Apple Rust",
     "common_names": ["Cedar rust"],
     "crop": "Apple",
@@ -86,7 +86,7 @@
     "type": "Fungal",
     "external_resources": []
   },
-  "Apple___healthy": {
+  "3": {
     "disease_name": null,
     "common_names": [],
     "crop": "Apple",
@@ -105,7 +105,7 @@
     "type": "None",
     "external_resources": []
   },
-  "Tomato___Late_blight": {
+  "37": {
     "disease_name": "Late Blight",
     "common_names": ["Tomato late blight"],
     "crop": "Tomato",
@@ -130,7 +130,7 @@
     "type": "Fungal",
     "external_resources": []
   },
-  "Blueberry__healthy": {
+  "4": {
     "disease_name": null,
     "common_names": [],
     "crop": "Blueberry",
@@ -149,7 +149,7 @@
     "type": "None",
     "external_resources": []
   },
-  "Cherry_(including_sour)__Powdery_mildew": {
+  "5": {
     "disease_name": "Powdery Mildew",
     "common_names": ["Cherry powdery mildew"],
     "crop": "Cherry (including sour)",
@@ -178,7 +178,7 @@
     "type": "Fungal",
     "external_resources": []
   },
-  "Cherry_(including_sour)__healthy": {
+  "6": {
     "disease_name": null,
     "common_names": [],
     "crop": "Cherry (including sour)",
@@ -197,7 +197,7 @@
     "type": "None",
     "external_resources": []
   },
-  "Corn_(maize)___Cercospora_leaf_spot_Gray_leaf_spot": {
+  "7": {
     "disease_name": "Gray Leaf Spot",
     "common_names": ["Cercospora Leaf Spot", "Gray Leaf Spot"],
     "crop": "Corn (maize)",
@@ -225,7 +225,7 @@
     "type": "Fungal",
     "external_resources": []
   },
-  "Corn_(maize)___Common_rust": {
+  "8": {
     "disease_name": "Common Rust",
     "common_names": ["Corn Rust"],
     "crop": "Corn (maize)",
@@ -250,7 +250,7 @@
     "type": "Fungal",
     "external_resources": []
   },
-  "Corn_(maize)___Northern_Leaf_Blight": {
+  "9": {
     "disease_name": "Northern Leaf Blight",
     "common_names": ["NLB"],
     "crop": "Corn (maize)",
@@ -275,7 +275,7 @@
     "type": "Fungal",
     "external_resources": []
   },
-  "Corn_(maize)___healthy": {
+  "10": {
     "disease_name": null,
     "common_names": [],
     "crop": "Corn (maize)",
@@ -294,7 +294,7 @@
     "type": "None",
     "external_resources": []
   },
-  "Grape___Black_rot": {
+  "11": {
     "disease_name": "Black Rot",
     "common_names": ["Grape Black Rot"],
     "crop": "Grape",
@@ -319,7 +319,7 @@
     "type": "Fungal",
     "external_resources": []
   },
-  "Grape___Esca_(Black_Measles)": {
+  "12": {
     "disease_name": "Esca (Black Measles)",
     "common_names": ["Esca", "Black Measles"],
     "crop": "Grape",
@@ -342,7 +342,7 @@
     "type": "Fungal",
     "external_resources": []
   },
-  "Grape___Leaf_blight_(Isariopsis_Leaf_Spot)": {
+  "13": {
     "disease_name": "Leaf Blight (Isariopsis Leaf Spot)",
     "common_names": ["Isariopsis Leaf Spot"],
     "crop": "Grape",
@@ -367,7 +367,7 @@
     "type": "Fungal",
     "external_resources": []
   },
-  "Grape___healthy": {
+  "14": {
     "disease_name": null,
     "common_names": [],
     "crop": "Grape",
@@ -386,7 +386,7 @@
     "type": "None",
     "external_resources": []
   },
-  "Orange___Haunglongbing_(Citrus_greening)": {
+  "15": {
     "disease_name": "Huanglongbing (Citrus Greening)",
     "common_names": ["Citrus Greening", "HLB"],
     "crop": "Orange",
@@ -417,7 +417,7 @@
       }
     ]
   },
-  "Peach___Bacterial_spot": {
+  "16": {
     "disease_name": "Bacterial Spot",
     "common_names": ["Peach Bacterial Spot"],
     "crop": "Peach",
@@ -443,7 +443,7 @@
     "type": "Bacterial",
     "external_resources": []
   },
-  "Peach___healthy": {
+  "17": {
     "disease_name": null,
     "common_names": [],
     "crop": "Peach",
@@ -462,7 +462,7 @@
     "type": "None",
     "external_resources": []
   },
-  "Pepper,_bell___Bacterial_spot": {
+  "18": {
     "disease_name": "Bacterial Spot",
     "common_names": ["Bell Pepper Bacterial Spot"],
     "crop": "Pepper, bell",
@@ -490,7 +490,7 @@
     "type": "Bacterial",
     "external_resources": []
   },
-  "Pepper,_bell___healthy": {
+  "19": {
     "disease_name": null,
     "common_names": [],
     "crop": "Pepper, bell",
@@ -511,7 +511,7 @@
     "type": "None",
     "external_resources": []
   },
-  "Potato___Early_blight": {
+  "20": {
     "disease_name": "Early Blight",
     "common_names": ["Potato Early Blight"],
     "crop": "Potato",
@@ -536,7 +536,7 @@
     "type": "Fungal",
     "external_resources": []
   },
-  "Potato___Late_blight": {
+  "21": {
     "disease_name": "Late Blight",
     "common_names": ["Potato Late Blight"],
     "crop": "Potato",
@@ -565,7 +565,7 @@
     "type": "Fungal‑like",
     "external_resources": []
   },
-  "Potato___healthy": {
+  "22": {
     "disease_name": null,
     "common_names": [],
     "crop": "Potato",
@@ -584,7 +584,7 @@
     "type": "None",
     "external_resources": []
   },
-  "Raspberry___healthy": {
+  "23": {
     "disease_name": null,
     "common_names": [],
     "crop": "Raspberry",
@@ -605,7 +605,7 @@
     "type": "None",
     "external_resources": []
   },
-  "Soybean___healthy": {
+  "24": {
     "disease_name": null,
     "common_names": [],
     "crop": "Soybean",
@@ -624,7 +624,7 @@
     "type": "None",
     "external_resources": []
   },
-  "Squash___Powdery_mildew": {
+  "25": {
     "disease_name": "Powdery Mildew",
     "common_names": ["Squash powdery mildew"],
     "crop": "Squash",
@@ -652,7 +652,7 @@
     "type": "Fungal",
     "external_resources": []
   },
-  "Strawberry___Leaf_scorch": {
+  "26": {
     "disease_name": "Leaf Scorch",
     "common_names": ["Strawberry leaf scorch"],
     "crop": "Strawberry",
@@ -680,7 +680,7 @@
     "type": "Fungal",
     "external_resources": []
   },
-  "Strawberry___healthy": {
+  "27": {
     "disease_name": null,
     "common_names": [],
     "crop": "Strawberry",
@@ -701,7 +701,7 @@
     "type": "None",
     "external_resources": []
   },
-  "Tomato___Bacterial_spot": {
+  "28": {
     "disease_name": "Bacterial Spot",
     "common_names": ["Tomato Bacterial Spot"],
     "crop": "Tomato",
@@ -729,7 +729,7 @@
     "type": "Bacterial",
     "external_resources": []
   },
-  "Tomato___Early_blight": {
+  "29": {
     "disease_name": "Early Blight",
     "common_names": ["Tomato Early Blight"],
     "crop": "Tomato",
@@ -752,7 +752,7 @@
     "type": "Fungal",
     "external_resources": []
   },
-  "Tomato___Leaf_Mold": {
+  "30": {
     "disease_name": "Leaf Mold",
     "common_names": ["Tomato Leaf Mold"],
     "crop": "Tomato",
@@ -777,7 +777,7 @@
     "type": "Fungal",
     "external_resources": []
   },
-  "Tomato___Septoria_leaf_spot": {
+  "31": {
     "disease_name": "Septoria Leaf Spot",
     "common_names": ["Tomato Septoria Leaf Spot"],
     "crop": "Tomato",
@@ -802,7 +802,7 @@
     "type": "Fungal",
     "external_resources": []
   },
-  "Tomato___Spider_mites_Two-spotted_spider_mite": {
+  "32": {
     "disease_name": "Two‑Spotted Spider Mite Infestation",
     "common_names": ["Spider Mites", "Two‑Spotted Mite"],
     "crop": "Tomato",
@@ -827,7 +827,7 @@
     "type": "Pest",
     "external_resources": []
   },
-  "Tomato___Target_Spot": {
+  "33": {
     "disease_name": "Target Spot",
     "common_names": ["Tomato Target Spot"],
     "crop": "Tomato",
@@ -852,7 +852,7 @@
     "type": "Fungal",
     "external_resources": []
   },
-  "Tomato___Tomato_Yellow_Leaf_Curl_Virus": {
+  "34": {
     "disease_name": "Tomato Yellow Leaf Curl Virus",
     "common_names": ["TYLCV"],
     "crop": "Tomato",
@@ -877,7 +877,7 @@
     "type": "Viral",
     "external_resources": []
   },
-  "Tomato___Tomato_mosaic_virus": {
+  "35": {
     "disease_name": "Tomato Mosaic Virus",
     "common_names": ["ToMV"],
     "crop": "Tomato",
@@ -902,7 +902,7 @@
     "type": "Viral",
     "external_resources": []
   },
-  "Tomato___healthy": {
+  "36": {
     "disease_name": null,
     "common_names": [],
     "crop": "Tomato",

main.py

@@ -2,17 +2,19 @@ import json
 import os
 import logging
 from typing import Dict, Optional, Any, List
+import uuid
 from pydantic import BaseModel, Field, field_validator
 from contextlib import asynccontextmanager
 from rapidfuzz import process, fuzz
 import urllib.parse
-
-from fastapi.responses import JSONResponse
+from slowapi import Limiter
+from slowapi.util import get_remote_address
 from fastapi.middleware.cors import CORSMiddleware
+from bleach import clean
 
 import numpy as np
 import tensorflow as tf
-from fastapi import FastAPI, File, Path, Query, UploadFile, HTTPException, status
+from fastapi import FastAPI, File, Path, Query, Request, UploadFile, HTTPException, status
 from PIL import Image
 import io
 from huggingface_hub import hf_hub_download
@@ -30,8 +32,8 @@ IMAGE_SIZE: tuple = (300, 300)
 MAX_FILE_SIZE_MB: int = 10
 CONFIDENCE_THRESHOLD: float = 0.5
 
-# Plant disease class names
-CLASS_NAMES = ['Apple___Apple_scab', 'Apple___Black_rot', 'Apple___Cedar_apple_rust', 'Apple___healthy', 'Blueberry___healthy', 'Cherry_(including_sour)___Powdery_mildew', 'Cherry_(including_sour)___healthy', 'Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot', 'Corn_(maize)___Common_rust_', 'Corn_(maize)___Northern_Leaf_Blight', 'Corn_(maize)___healthy', 'Grape___Black_rot', 'Grape___Esca_(Black_Measles)', 'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)', 'Grape___healthy', 'Orange___Haunglongbing_(Citrus_greening)', 'Peach___Bacterial_spot', 'Peach___healthy', 'Pepper,_bell___Bacterial_spot', 'Pepper,_bell___healthy', 'Potato___Early_blight', 'Potato___Late_blight', 'Potato___healthy', 'Raspberry___healthy', 'Soybean___healthy', 'Squash___Powdery_mildew', 'Strawberry___Leaf_scorch', 'Strawberry___healthy', 'Tomato___Bacterial_spot', 'Tomato___Early_blight', 'Tomato___Late_blight', 'Tomato___Leaf_Mold', 'Tomato___Septoria_leaf_spot', 'Tomato___Spider_mites Two-spotted_spider_mite', 'Tomato___Target_Spot', 'Tomato___Tomato_Yellow_Leaf_Curl_Virus', 'Tomato___Tomato_mosaic_virus', 'Tomato___healthy']
+# Plant disease class names - these are the actual class indices that the model outputs
+CLASS_NAMES = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37"]
 
 # HTTP Status Messages
 HTTP_MESSAGES = {
@@ -49,6 +51,7 @@ HTTP_MESSAGES = {
 model: Optional[tf.keras.Model] = None
 disease_guide: Dict[str, Dict[str, Any]] = {}
 
+
 # Response models with improved validation
 
 class DiseaseInfo(BaseModel):
@@ -67,6 +70,7 @@ class DiseaseInfo(BaseModel):
     localized_tips: str = ""
     type: str = "Unknown"
     external_resources: List[Dict[str, str]] = []
+    is_healthy: bool = False
 
     @field_validator('external_resources', mode='before')
     @classmethod
@@ -95,13 +99,16 @@ class DiseaseInfo(BaseModel):
             elif field_name in ['common_names', 'symptoms', 'treatment', 'image_urls', 'prevention', 'external_resources']:
                 return []
             elif field_name in ['crop', 'description', 'management_tips', 'risk_level', 'sprayer_intervals', 'localized_tips', 'type']:
-                return info.default
+                return info.default if hasattr(info, 'default') else "Unknown"
+            elif field_name == 'is_healthy':
+                return False
         return v
 
 
 class PredictionResponse(BaseModel):
     success: bool
     predicted_class: str
+    predicted_class_index: int
     clean_class_name: str = Field(description="Human-readable class name")
     confidence: float
     confidence_level: str = Field(description="High/Medium/Low confidence level")
@@ -116,6 +123,7 @@ class HealthResponse(BaseModel):
     model_loaded: bool
     total_classes: int
     available_diseases: int
+    healthy_classes: int
     message: str
 
 class SearchResult(BaseModel):
@@ -155,14 +163,16 @@ def load_disease_guide() -> Dict[str, Dict[str, Any]]:
         logger.error(f"Failed to load disease guide: {str(e)}")
         return {}
 
-def clean_class_name(class_name: str) -> str:
-    """Convert class name to human-readable format"""
-    # Replace underscores with spaces and clean up formatting
-    cleaned = class_name.replace('___', ' - ').replace('_', ' ')
-    # Handle special cases
-    cleaned = cleaned.replace('(including sour)', '(including sour)')
-    cleaned = cleaned.replace('Two-spotted spider mite', 'Two-spotted spider mite')
-    return cleaned.title()
+def clean_class_name(class_index: str, disease_info: Optional[Dict[str, Any]] = None) -> str:
+    """Convert class index to human-readable format"""
+    if disease_info and disease_info.get('disease_name'):
+        # Use the disease name from the JSON if available
+        disease_name = disease_info['disease_name']
+        crop = disease_info.get('crop', 'Unknown')
+        return f"{crop} - {disease_name}"
+    else:
+        # For healthy plants or unknown diseases
+        return f"Class {class_index} (Healthy/Unknown)"
 
 def get_confidence_level(confidence: float) -> str:
     """Categorize confidence level"""
@@ -172,10 +182,24 @@ def get_confidence_level(confidence: float) -> str:
         return "Medium"
     else:
         return "Low"
+    
+def sanitize_search_query(query: str) -> str:
+    """Sanitize search input"""
+    return clean(query.strip(), tags=[], strip=True)[:100]  # Limit length
 
-def safe_create_disease_info(disease_data: Dict[str, Any]) -> DiseaseInfo:
+def safe_create_disease_info(class_index: str, disease_data: Optional[Dict[str, Any]] = None) -> DiseaseInfo:
     """Safely create DiseaseInfo object with proper validation and defaults"""
     try:
+        if not disease_data:
+            # This is likely a healthy plant or unknown disease
+            return DiseaseInfo(
+                disease_name=None,
+                crop="Unknown",
+                description=f"This appears to be a healthy plant or an unrecognized condition for class {class_index}",
+                is_healthy=True,
+                type="Healthy/Unknown"
+            )
+        
         # Create a copy of the data to avoid modifying the original
         safe_data = disease_data.copy()
         
@@ -195,7 +219,8 @@ def safe_create_disease_info(disease_data: Dict[str, Any]) -> DiseaseInfo:
             'sprayer_intervals': safe_data.get('sprayer_intervals', ''),
             'localized_tips': safe_data.get('localized_tips', ''),
             'type': safe_data.get('type', 'Unknown'),
-            'external_resources': safe_data.get('external_resources', [])
+            'external_resources': safe_data.get('external_resources', []),
+            'is_healthy': False
         }
         
         # Validate and clean external_resources
@@ -212,51 +237,90 @@ def safe_create_disease_info(disease_data: Dict[str, Any]) -> DiseaseInfo:
         return DiseaseInfo(**defaults)
         
     except Exception as e:
-        logger.error(f"Error creating DiseaseInfo: {str(e)}")
+        logger.error(f"Error creating DiseaseInfo for class {class_index}: {str(e)}")
         logger.error(f"Data causing error: {disease_data}")
         
         # Return a minimal valid DiseaseInfo object
         return DiseaseInfo(
             disease_name="Unknown",
             crop="Unknown",
-            description="Error loading disease information",
-            cause="Unknown"
+            description=f"Error loading disease information for class {class_index}",
+            cause="Unknown",
+            is_healthy=False
         )
 
-def get_recommendations(predicted_class: str, confidence: float, disease_info: DiseaseInfo) -> List[str]:
-    """Generate actionable recommendations based on prediction"""
+def get_recommendations(class_index: str, confidence: float, disease_info: DiseaseInfo) -> List[str]:
+    """Generate actionable recommendations based on prediction using treatment and prevention from JSON"""
     recommendations = []
     
+    # Add confidence-based recommendations first
     if confidence < CONFIDENCE_THRESHOLD:
-        recommendations.append("Consider taking a clearer, well-lit photo for better accuracy")
-        recommendations.append("Ensure the leaf fills most of the frame")
+        recommendations.extend([
+            "⚠️ Low confidence prediction - consider taking a clearer, well-lit photo",
+            "📸 Ensure the leaf/plant fills most of the frame and is in focus",
+            "💡 Try taking photos in natural light for better results"
+        ])
     
-    if disease_info.disease_name and disease_info.disease_name != "Unknown":
-        # Disease detected
-        treatments = disease_info.treatment if disease_info.treatment else ["Consult agricultural expert"]
+    if disease_info.is_healthy or not disease_info.disease_name:
+        # Healthy plant recommendations
         recommendations.extend([
-            f"Immediate action: {treatments[0]}",
-            "Isolate affected plants to prevent spread",
-            "Monitor other plants for similar symptoms"
+            "✅ Plant appears healthy - continue current care routine",
+            "👀 Monitor regularly for any changes in leaf color, spots, or wilting",
+            "💧 Maintain proper watering schedule - avoid overwatering",
+            "🌱 Ensure adequate fertilization and soil drainage",
+            "🛡️ Consider preventive measures during disease-prone seasons",
+            "🌿 Keep the growing area clean and remove fallen debris"
         ])
-        
+    else:
+        # Disease detected - use treatment and prevention from JSON
         if disease_info.risk_level == "High":
-            recommendations.insert(0, "⚠️ HIGH RISK: Take immediate action to prevent crop loss")
-            
+            recommendations.insert(0, "🚨 HIGH RISK DISEASE: Take immediate action to prevent crop loss")
+        elif disease_info.risk_level == "Medium":
+            recommendations.insert(0, "⚠️ MEDIUM RISK DISEASE: Prompt treatment recommended")
+        
+        # Add disease identification
+        recommendations.append(f"🔬 Disease identified: {disease_info.disease_name}")
+        
+        # Add treatments from JSON
+        if disease_info.treatment:
+            recommendations.append("💊 **TREATMENT RECOMMENDATIONS:**")
+            for i, treatment in enumerate(disease_info.treatment, 1):
+                recommendations.append(f"   {i}. {treatment}")
+        else:
+            recommendations.append("💊 Consult agricultural expert for proper treatment")
+        
+        # Add prevention measures from JSON
+        if disease_info.prevention:
+            recommendations.append("🛡️ **PREVENTION MEASURES:**")
+            for i, prevention in enumerate(disease_info.prevention, 1):
+                recommendations.append(f"   {i}. {prevention}")
+        
         # Add management tips if available
         if disease_info.management_tips:
-            recommendations.append(f"Management tip: {disease_info.management_tips}")
+            recommendations.append(f"💡 **MANAGEMENT TIP:** {disease_info.management_tips}")
             
         # Add sprayer intervals if available
         if disease_info.sprayer_intervals:
-            recommendations.append(f"Spraying schedule: {disease_info.sprayer_intervals}")
-    else:
-        # Healthy plant
+            recommendations.append(f"🚿 **SPRAYING SCHEDULE:** {disease_info.sprayer_intervals}")
+            
+        # Add localized tips if available
+        if disease_info.localized_tips:
+            recommendations.append(f"🎯 **LOCALIZED TIP:** {disease_info.localized_tips}")
+        
+        # General disease management recommendations
         recommendations.extend([
-            "Plant appears healthy - continue current care routine",
-            "Monitor regularly for any changes",
-            "Maintain preventive measures"
+            "🔒 Isolate affected plants to prevent spread to healthy plants",
+            "👀 Monitor other plants regularly for similar symptoms",
+            "🗑️ Remove and destroy infected plant material properly",
+            "🧼 Sanitize tools and hands after handling infected plants"
         ])
+        
+        # Add external resources if available
+        if disease_info.external_resources:
+            recommendations.append("📚 **ADDITIONAL RESOURCES:**")
+            for resource in disease_info.external_resources:
+                if resource.get('title') and resource.get('url'):
+                    recommendations.append(f"   • {resource['title']}: {resource['url']}")
     
     return recommendations
 
@@ -352,35 +416,43 @@ def predict_image(image_bytes: bytes) -> PredictionResponse:
         predicted_class_idx = np.argmax(predictions[0])
         confidence = float(predictions[0][predicted_class_idx])
         
-        # Get predicted class
-        predicted_class = CLASS_NAMES[predicted_class_idx]
-        clean_name = clean_class_name(predicted_class)
+        # Get predicted class as string (this matches the JSON keys)
+        predicted_class = str(predicted_class_idx)
+        
+        # Get disease information from the JSON using the string key
+        disease_data = disease_guide.get(predicted_class, None)
+        disease_info = safe_create_disease_info(predicted_class, disease_data)
+        
+        # Create clean class name
+        clean_name = clean_class_name(predicted_class, disease_data)
         confidence_level = get_confidence_level(confidence)
         class_id = create_class_id(predicted_class)
         
-        # Get top 5 predictions
+        # Get top 5 predictions with their disease info
         top_indices = np.argsort(predictions[0])[-5:][::-1]
-        all_predictions = {
-            clean_class_name(CLASS_NAMES[idx]): float(predictions[0][idx])
-            for idx in top_indices
-        }
+        all_predictions = {}
         
-        # Get disease information with safe creation
-        disease_data = disease_guide.get(predicted_class, {})
-        disease_info = safe_create_disease_info(disease_data)
+        for idx in top_indices:
+            class_str = str(idx)
+            class_disease_data = disease_guide.get(class_str, None)
+            readable_name = clean_class_name(class_str, class_disease_data)
+            all_predictions[readable_name] = float(predictions[0][idx])
         
-        # Generate recommendations
+        # Generate recommendations using the updated function
         recommendations = get_recommendations(predicted_class, confidence, disease_info)
         
-        # Determine message based on confidence
+        # Determine message based on confidence and disease status
         if confidence < CONFIDENCE_THRESHOLD:
             message = HTTP_MESSAGES["LOW_CONFIDENCE"]
+        elif disease_info.is_healthy:
+            message = "Plant appears healthy! Continue with regular care."
         else:
-            message = "Prediction completed successfully"
+            message = f"Disease detected: {disease_info.disease_name or 'Unknown condition'}"
         
         return PredictionResponse(
             success=True,
             predicted_class=predicted_class,
+            predicted_class_index=predicted_class_idx,
             clean_class_name=clean_name,
             confidence=confidence,
             confidence_level=confidence_level,
@@ -407,6 +479,7 @@ def is_image_file(filename: str) -> bool:
     image_extensions = {'.jpg', '.jpeg', '.png', '.bmp', '.gif', '.tiff', '.webp'}
     return any(filename.lower().endswith(ext) for ext in image_extensions)
 
+
 @asynccontextmanager
 async def lifespan(app: FastAPI):
     """Handle startup and shutdown events"""
@@ -439,7 +512,7 @@ async def lifespan(app: FastAPI):
 app = FastAPI(
     title="Plant Disease Prediction API",
     description="API for predicting plant diseases from leaf images using deep learning",
-    version="2.1.0",
+    version="2.2.0",
     lifespan=lifespan,
     docs_url="/docs",
     redoc_url="/redoc"
@@ -454,29 +527,51 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
+@app.middleware("http")
+async def add_request_id(request: Request, call_next):
+    request_id = str(uuid.uuid4())
+    request.state.request_id = request_id
+    
+    with logger.contextvars(request_id=request_id):
+        response = await call_next(request)
+    
+    return response
+
+limiter = Limiter(key_func=get_remote_address)
+app.state.limiter = limiter
+
 @app.get("/", response_model=HealthResponse)
 async def root():
     """Root endpoint with API information"""
+    disease_count = len([d for d in disease_guide.values() if d.get("disease_name")])
+    healthy_count = len(CLASS_NAMES) - disease_count
+    
     return HealthResponse(
         status="running",
         model_loaded=model is not None,
         total_classes=len(CLASS_NAMES),
-        available_diseases=len([d for d in disease_guide.values() if d.get("disease_name")]),
+        available_diseases=disease_count,
+        healthy_classes=healthy_count,
         message="Plant Disease Prediction API is running"
     )
 
 @app.get("/health", response_model=HealthResponse)
 async def health_check():
     """Health check endpoint"""
+    disease_count = len([d for d in disease_guide.values() if d.get("disease_name")])
+    healthy_count = len(CLASS_NAMES) - disease_count
+    
     return HealthResponse(
         status="healthy" if model is not None else "unhealthy",
         model_loaded=model is not None,
         total_classes=len(CLASS_NAMES),
-        available_diseases=len([d for d in disease_guide.values() if d.get("disease_name")]),
+        available_diseases=disease_count,
+        healthy_classes=healthy_count,
         message=HTTP_MESSAGES["MODEL_LOAD_SUCCESS"] if model is not None else HTTP_MESSAGES["MODEL_NOT_LOADED"]
     )
 
 @app.post("/predict", response_model=PredictionResponse)
+@limiter.limit("10/minute") 
 async def predict_plant_disease(file: UploadFile = File(...)):
     """
     Predict plant disease from uploaded image
@@ -515,12 +610,17 @@ async def predict_plant_disease(file: UploadFile = File(...)):
             status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
             detail=HTTP_MESSAGES["IMAGE_PROCESSING_FAILED"].format(error=str(e))
         )
+    finally:
+        # Explicit cleanup for large files
+        if image_bytes:
+            del image_bytes
 
 @app.get("/diseases", response_model=List[SearchResult])
 async def get_all_plant_diseases(
     crop: Optional[str] = Query(None, description="Filter by crop name (e.g. Apple, Tomato)"),
     disease_type: Optional[str] = Query(None, description="Filter by disease type (Fungal, Bacterial, Viral)"),
-    risk_level: Optional[str] = Query(None, description="Filter by risk level (High, Medium, Low)")
+    risk_level: Optional[str] = Query(None, description="Filter by risk level (High, Medium, Low)"),
+    include_healthy: bool = Query(False, description="Include healthy/unknown classes")
 ):
     """
     Get all plant diseases with optional filtering
@@ -528,21 +628,23 @@ async def get_all_plant_diseases(
     diseases = []
     
     for class_name, info in disease_guide.items():
-        if not info.get("disease_name"):
-            continue  # Skip healthy entries
-        
-        # Apply filters
-        if crop and info.get("crop", "").lower() != crop.lower():
-            continue
-        if disease_type and info.get("type", "").lower() != disease_type.lower():
-            continue
-        if risk_level and info.get("risk_level", "").lower() != risk_level.lower():
+        # Skip healthy classes unless specifically requested
+        if not include_healthy and not info.get("disease_name"):
             continue
         
+        # Apply filters (only for disease entries)
+        if info.get("disease_name"):  # Only apply filters to actual diseases
+            if crop and info.get("crop", "").lower() != crop.lower():
+                continue
+            if disease_type and info.get("type", "").lower() != disease_type.lower():
+                continue
+            if risk_level and info.get("risk_level", "").lower() != risk_level.lower():
+                continue
+        
         diseases.append(SearchResult(
             class_name=class_name,
             class_id=create_class_id(class_name),
-            disease_info=safe_create_disease_info(info)
+            disease_info=safe_create_disease_info(class_name, info if info.get("disease_name") else None)
         ))
     
     return diseases
@@ -550,36 +652,54 @@ async def get_all_plant_diseases(
 @app.get("/search", response_model=SearchResponse)
 async def search_diseases(
     query: str = Query(..., min_length=1, description="Search term"),
-    limit: int = Query(10, ge=1, le=50, description="Maximum number of results")
+    limit: int = Query(10, ge=1, le=50, description="Maximum number of results"),
+    include_healthy: bool = Query(False, description="Include healthy/unknown classes in search")
 ):
     """
     Search plant diseases with fuzzy matching and relevance scoring
     """
-    query_lower = query.lower()
+    cleaned_query = sanitize_search_query(query)
+    if not cleaned_query:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="Search query cannot be empty"
+        )
+    if len(cleaned_query) < 2:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="Search query must be at least 2 characters long"
+        )
+    
+    query_lower = cleaned_query.lower()
     exact_matches = []
     fuzzy_candidates = []
     
     for class_name, info in disease_guide.items():
-        if not info.get("disease_name"):
+        # Skip healthy classes unless specifically requested
+        if not include_healthy and not info.get("disease_name"):
             continue
         
         # Build searchable text
-        searchable_text = " ".join([
-            class_name,
-            info.get("disease_name", ""),
-            info.get("description", ""),
-            info.get("crop", ""),
-            info.get("type", ""),
-            " ".join(info.get("symptoms", [])),
-            " ".join(info.get("common_names", []))
-        ]).lower()
+        searchable_text_parts = [class_name]
+        
+        if info.get("disease_name"):
+            searchable_text_parts.extend([
+                info.get("disease_name", ""),
+                info.get("description", ""),
+                info.get("crop", ""),
+                info.get("type", ""),
+                " ".join(info.get("symptoms", [])),
+                " ".join(info.get("common_names", []))
+            ])
+        
+        searchable_text = " ".join(searchable_text_parts).lower()
         
         # Check for exact substring matches
         if query_lower in searchable_text:
             exact_matches.append(SearchResult(
                 class_name=class_name,
                 class_id=create_class_id(class_name),
-                disease_info=safe_create_disease_info(info)
+                disease_info=safe_create_disease_info(class_name, info if info.get("disease_name") else None)
             ))
         else:
             fuzzy_candidates.append((class_name, info, searchable_text))
@@ -594,26 +714,34 @@ async def search_diseases(
     
     # Fuzzy search on candidates
     search_texts = [text for _, _, text in fuzzy_candidates]
-    fuzzy_matches = process.extract(
-        query, search_texts, scorer=fuzz.token_sort_ratio, limit=limit
-    )
-    
-    suggestions = []
-    for match_text, score, idx in fuzzy_matches:
-        if score > 60:  # Minimum relevance threshold
-            class_name, info, _ = fuzzy_candidates[idx]
-            suggestions.append(SearchResult(
-                class_name=class_name,
-                class_id=create_class_id(class_name),
-                disease_info=safe_create_disease_info(info),
-                relevance_score=score
-            ))
+    if search_texts:
+        fuzzy_matches = process.extract(
+            query, search_texts, scorer=fuzz.token_sort_ratio, limit=limit
+        )
+        
+        suggestions = []
+        for match_text, score, idx in fuzzy_matches:
+            if score > 60:  # Minimum relevance threshold
+                class_name, info, _ = fuzzy_candidates[idx]
+                suggestions.append(SearchResult(
+                    class_name=class_name,
+                    class_id=create_class_id(class_name),
+                    disease_info=safe_create_disease_info(class_name, info if info.get("disease_name") else None),
+                    relevance_score=score
+                ))
+        
+        return SearchResponse(
+            results=[],
+            suggestions=suggestions,
+            total_results=len(suggestions),
+            message="No exact matches found. Showing relevant suggestions." if suggestions else "No matches found."
+        )
     
     return SearchResponse(
         results=[],
-        suggestions=suggestions,
-        total_results=len(suggestions),
-        message="No exact matches found. Showing relevant suggestions." if suggestions else "No matches found."
+        suggestions=[],
+        total_results=0,
+        message="No matches found."
     )
 
 @app.get("/diseases/{class_id}", response_model=SearchResult)
@@ -627,18 +755,19 @@ async def get_disease_by_class_id(
         # Decode the class_id back to class_name
         class_name = decode_class_id(class_id)
         
-        disease_data = disease_guide.get(class_name)
-        
-        if not disease_data:
+        # Validate that the class exists in our CLASS_NAMES
+        if class_name not in CLASS_NAMES:
             raise HTTPException(
                 status_code=status.HTTP_404_NOT_FOUND,
-                detail=f"Disease with class ID '{class_id}' not found."
+                detail=f"Class with ID '{class_id}' not found in supported classes."
             )
         
+        disease_data = disease_guide.get(class_name, None)
+        
         return SearchResult(
             class_name=class_name,
             class_id=class_id,
-            disease_info=safe_create_disease_info(disease_data)
+            disease_info=safe_create_disease_info(class_name, disease_data)
         )
         
     except UnicodeDecodeError:
@@ -649,24 +778,24 @@ async def get_disease_by_class_id(
 
 @app.get("/diseases/by-name/{class_name}", response_model=SearchResult)
 async def get_disease_by_class_name(
-    class_name: str = Path(..., description="Exact class name, e.g. Apple___Apple_scab")
+    class_name: str = Path(..., description="Exact class name (string number), e.g. '0', '1', '2'")
 ):
     """
     Retrieve detailed information for a specific disease class by exact class name
     (Alternative endpoint for direct class name access)
     """
-    disease_data = disease_guide.get(class_name)
-    
-    if not disease_data:
+    # Validate that the class exists in our CLASS_NAMES
+    if class_name not in CLASS_NAMES:
         raise HTTPException(
             status_code=status.HTTP_404_NOT_FOUND,
-            detail=f"Disease with class name '{class_name}' not found."
+            detail=f"Class '{class_name}' not found in supported classes. Supported classes: {', '.join(CLASS_NAMES[:10])}..."
         )
     
+    disease_data = disease_guide.get(class_name, None)
     return SearchResult(
         class_name=class_name,
         class_id=create_class_id(class_name),
-        disease_info=safe_create_disease_info(disease_data)
+        disease_info=safe_create_disease_info(class_name, disease_data)
     )
 
 @app.get("/stats")