"Update space"

Signed-off-by: Emmanuel Frimpong Asante <[email protected]>

services/disease_detection.py

@@ -4,11 +4,20 @@ import cv2
 import numpy as np
 import tensorflow as tf
 from keras.models import load_model
+from services.llama_service import llama2_response
 
 # Check your model optimizer configuration
 optimizer = tf.keras.optimizers.Adam()  # Default optimizer without weight_decay
-# Load the model (ensure the model path is correct)
-my_model = load_model('models/Final_Chicken_disease_model.h5')
+
+# Load TensorFlow/Keras models with GPU support if available, otherwise use CPU
+try:
+    device_name = '/GPU:0' if len(tf.config.list_physical_devices('GPU')) > 0 else '/CPU:0'
+    with tf.device(device_name):
+        my_model = load_model('models/Final_Chicken_disease_model.h5', compile=True)
+        auth_model = load_model('models/auth_model.h5', compile=True)
+        print(f"Models loaded successfully on {device_name}.")
+except Exception as e:
+    print(f"Error loading models: {e}")
 
 # Mapping of disease labels to disease names, health status, and treatment recommendations
 name_disease = {0: 'Coccidiosis', 1: 'Healthy', 2: 'New Castle Disease', 3: 'Salmonella'}
@@ -20,7 +29,6 @@ recommend = {
     3: 'Ponston'  # Treatment for Salmonella
 }
 
-
 class PoultryFarmBot:
     def __init__(self, db):
         """
@@ -64,10 +72,54 @@ class PoultryFarmBot:
 
             # Get the disease name, status, and recommended treatment
             name = name_disease.get(indx, "Unknown disease")
-            status = result.get(indx, "unknown condition")
-            recom = recommend.get(indx, "no recommendation available")
+            status = result.get(indx, "Unknown condition")
+            recom = recommend.get(indx, "No recommendation available")
 
             return name, status, recom
         except Exception as e:
             print(f"Error during disease prediction: {e}")
             return "Prediction failed.", None, None, None
+
+    # Generate a detailed response using Llama 2 for disease information and recommendations
+    def generate_disease_response(self, disease_name, status, recommendation):
+        """
+        Generates a detailed response using Llama 2 model for disease information and recommendations.
+
+        :param disease_name: The name of the detected disease.
+        :param status: The health status of the poultry.
+        :param recommendation: The treatment recommendation.
+        :return: A detailed response with disease information.
+        """
+        try:
+            prompt = (
+                f"The disease detected is {disease_name}, classified as {status}. "
+                f"Recommended action: {recommendation}. "
+                f"Here is some information about {disease_name}: causes, symptoms, and treatment methods "
+                "to effectively manage this condition on a poultry farm."
+            )
+            response = llama2_response(prompt)
+            # Post-process to remove the prompt if accidentally included in the response
+            return response.replace(prompt, "").strip()
+        except Exception as e:
+            print(f"Error generating detailed response: {e}")
+            return "Unable to generate detailed response."
+
+    # Diagnose Disease Using Fecal Image
+    def diagnose_disease(self, image):
+        """
+        Diagnose a disease using a fecal image.
+
+        :param image: The image of poultry feces to detect a disease.
+        :return: Tuple of diagnosis result and detailed response if applicable.
+        """
+        if image is not None and image.size > 0:  # Ensure the image is valid and has elements
+            disease_name, status, recommendation = self.predict(image)
+            if disease_name and status and recommendation:
+                detailed_response = self.generate_disease_response(disease_name, status, recommendation)
+                return {
+                    "disease": disease_name,
+                    "status": status,
+                    "recommendation": recommendation,
+                    "detailed_response": detailed_response
+                }
+        return "Please provide an image of poultry fecal matter for disease detection.", None, None, None