update space

README.md

@@ -54,10 +54,10 @@ This project integrates both a **Poultry Farming Assistance System** and a **Pou
 - [x] Implement session management and token validation.
 
 ### 3. Poultry Farming Assistance System
-- [ ] Integrate poultry disease detection model (`Final_Chicken_disease_model.h5`).
-- [ ] Create an image preprocessing pipeline for disease detection.
-- [ ] Build routes to upload and analyze poultry fecal images.
-- [ ] Develop health-related notifications and treatment suggestions.
+- [x] Integrate poultry disease detection model (`Final_Chicken_disease_model.h5`).
+- [x] Create an image preprocessing pipeline for disease detection.
+- [x] Build routes to upload and analyze poultry fecal images.
+- [x] Develop health-related notifications and treatment suggestions.
 - [ ] Implement real-time health monitoring and alert system for farmers.
 
 ### 4. To-Do List Management

services/disease_detection_service.py

@@ -16,20 +16,14 @@ logger = logging.getLogger(__name__)
 
 # Load environment variables
 MONGO_URI = os.getenv("MONGO_URI")
-HF_TOKEN = os.getenv('HF_Token')
 db_client = MongoClient(MONGO_URI)
 db = db_client.poultry_farm  # MongoDB for record-keeping
 
-# Hugging Face login for Llama 3.2
-if HF_TOKEN:
-    login(token=HF_TOKEN, add_to_git_credential=True)
-else:
-    logger.warning("Hugging Face token not found in environment variables.")
 
 # Mixed precision and GPU configuration
-gpus = tf.config.list_physical_devices('GPU')
-if gpus:
-    for gpu in gpus:
+gpu_devices = tf.config.list_physical_devices('GPU')
+if gpu_devices:
+    for gpu in gpu_devices:
         tf.config.experimental.set_memory_growth(gpu, True)
     from tensorflow.keras import mixed_precision
     policy = mixed_precision.Policy('mixed_float16')
@@ -38,7 +32,6 @@ if gpus:
 else:
     logger.info("Using CPU without mixed precision.")
 
-
 # Model loading functions
 def load_disease_model():
     """Load the disease detection model."""
@@ -53,7 +46,6 @@ def load_disease_model():
         logger.error(f"Error loading disease model: {e}")
         raise RuntimeError("Failed to load disease detection model")
 
-
 def load_llama_model():
     """Load the Llama 3.2 model for text generation."""
     try:
@@ -71,6 +63,9 @@ def load_llama_model():
         logger.error(f"Error loading Llama model: {e}")
         raise RuntimeError("Failed to load Llama model")
 
+# Load models at startup
+disease_model = load_disease_model()
+llama_model, llama_tokenizer = load_llama_model()
 
 # Disease mapping and treatment guidelines
 name_disease = {0: 'Coccidiosis', 1: 'Healthy', 2: 'New Castle Disease', 3: 'Salmonella'}
@@ -133,9 +128,9 @@ class PoultryFarmBot:
     def llama_response(self, prompt):
         """Generate a response from the Llama 3.2 model based on a given prompt."""
         try:
-            inputs = tokenizer(prompt, return_tensors="pt", max_length=150, truncation=True, padding=True)
+            inputs = llama_tokenizer(prompt, return_tensors="pt", max_length=150, truncation=True, padding=True)
             outputs = llama_model.generate(inputs["input_ids"], max_length=150, do_sample=True, temperature=0.7)
-            return tokenizer.decode(outputs[0], skip_special_tokens=True)
+            return llama_tokenizer.decode(outputs[0], skip_special_tokens=True)
         except Exception as e:
             logger.error(f"Llama model response error: {e}")
             return "Error generating detailed response."