"Update space"

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

app.py

@@ -7,6 +7,9 @@ import gradio as gr
 import cv2
 import numpy as np
 from huggingface_hub import login
+from datetime import datetime, timedelta
+import json
+import requests
 
 # Ensure the HF token is set
 tok = os.getenv('HF_Token')
@@ -22,27 +25,32 @@ print("TensorFlow version:", tf.__version__)
 print("Eager execution:", tf.executing_eagerly())
 print("TensorFlow GPU Available:", tf.config.list_physical_devices('GPU'))
 
-# Set TensorFlow to use mixed precision to leverage the T4 GPU's capabilities
+# Set TensorFlow to use mixed precision to leverage the T4 GPU's capabilities when available
 from tensorflow.keras import mixed_precision
 
-policy = mixed_precision.Policy('mixed_float16')
-mixed_precision.set_global_policy(policy)
+if len(tf.config.list_physical_devices('GPU')) > 0:
+    policy = mixed_precision.Policy('mixed_float16')
+    mixed_precision.set_global_policy(policy)
+    print("Using mixed precision with GPU")
+else:
+    print("Using CPU without mixed precision")
 
-# Load TensorFlow/Keras models with GPU support if available
+# Load TensorFlow/Keras models with GPU support if available, otherwise use CPU
 try:
-    with tf.device('/GPU:0'):  # Use GPU if available
+    device_name = '/GPU:0' if len(tf.config.list_physical_devices('GPU')) > 0 else '/CPU:0'
+    with tf.device(device_name):  # Use GPU if available, otherwise CPU
         my_model = load_model('models/Final_Chicken_disease_model.h5', compile=True)
         auth_model = load_model('models/auth_model.h5', compile=True)
-        print("Models loaded successfully.")
+        print(f"Models loaded successfully on {device_name}.")
 except Exception as e:
     print(f"Error loading models: {e}")
     raise
 
-# Set PyTorch device
+# Set PyTorch device to GPU if available, otherwise CPU
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 print(f"Using device: {device}")
 
-# Load the tokenizer and LLaMA model, ensuring they run on the GPU
+# Load the tokenizer and LLaMA model, ensuring they run on the correct device
 llama_tokenizer = AutoTokenizer.from_pretrained('meta-llama/Meta-Llama-3-8B-Instruct')
 llama_model = AutoModelForCausalLM.from_pretrained(
     'meta-llama/Meta-Llama-3-8B-Instruct',
@@ -59,79 +67,159 @@ result = {0: 'Critical', 1: 'No issue', 2: 'Critical', 3: 'Critical'}
 recommend = {0: 'Panadol', 1: 'You have no need of Medicine', 2: 'Paracetamol', 3: 'Ponston'}
 
 
-def preprocess_image(image):
-    try:
-        image_check = cv2.resize(image, (224, 224))
-        image_check = np.expand_dims(image_check, axis=0)  # Add batch dimension
-        return image_check
-    except Exception as e:
-        print(f"Error in image preprocessing: {e}")
-        return None
+class PoultryFarmBot:
+    def __init__(self):
+        self.feed_inventory = 1000  # kg
+        self.medicine_inventory = {"Panadol": 100, "Paracetamol": 50}
+        self.chicken_health = {}
+        self.reports = []
+
+    # Health Monitoring and Disease Diagnosis
+    def preprocess_image(self, image):
+        try:
+            image_check = cv2.resize(image, (224, 224))
+            image_check = np.expand_dims(image_check, axis=0)  # Add batch dimension
+            return image_check
+        except Exception as e:
+            print(f"Error in image preprocessing: {e}")
+            return None
+
+    def predict(self, image):
+        image_check = self.preprocess_image(image)
+        if image_check is None:
+            return "Image preprocessing failed.", None, None, None
+
+        indx = auth_model.predict(image_check).argmax()
+
+        if indx == 0:  # If the image is recognized as a chicken disease image
+            indx = my_model.predict(image_check).argmax()
+            name = name_disease.get(indx)
+            status = result.get(indx)
+            recom = recommend.get(indx)
+
+            diagnosis = f"The chicken is in a {status} condition, diagnosed with {name}. The recommended medication is {recom}."
+            return diagnosis, name, status, recom
+        else:  # If the image is not recognized as a chicken disease image
+            return (
+                "The uploaded image is not recognized as a chicken or does not appear to be related to any known chicken diseases. "
+                "Please ensure the image is clear and shows a chicken or its symptoms to receive a proper diagnosis."
+            ), None, None, None
+
+    def diagnose_disease(self, image=None, symptoms=None):
+        if image:
+            return self.predict(image)
+        elif symptoms:
+            # Simulate symptom-based diagnosis
+            return "Based on symptoms, the chicken might have Newcastle Disease."
+        return "Please provide an image or describe the symptoms."
+
+    # Inventory Management
+    def track_inventory(self, item, usage):
+        if item in self.medicine_inventory:
+            self.medicine_inventory[item] -= usage
+            if self.medicine_inventory[item] < 10:
+                return f"{item} inventory is low, please reorder."
+            return f"{item} inventory updated. Current inventory: {self.medicine_inventory[item]} units."
+        elif item == "feed":
+            self.feed_inventory -= usage
+            if self.feed_inventory < 100:
+                return "Feed inventory is low, please reorder."
+            return f"Feed inventory updated. Current inventory: {self.feed_inventory} kg."
+        return "Item not recognized in inventory."
+
+    # Reporting and Analytics
+    def generate_report(self):
+        report = {
+            "date": str(datetime.now()),
+            "feed_inventory": self.feed_inventory,
+            "medicine_inventory": self.medicine_inventory,
+            "chickens_monitored": len(self.chicken_health),
+            "health_reports": self.chicken_health
+        }
+        self.reports.append(report)
+        return json.dumps(report, indent=4)
+
+    # IoT Device Integration (Temperature and Humidity Monitoring)
+    def monitor_environment(self, temperature, humidity):
+        if temperature > 30:
+            return "Temperature too high, increase ventilation."
+        if humidity < 40:
+            return "Humidity too low, consider using a humidifier."
+        return "Environmental conditions are optimal."
+
+    # Integration with External Systems
+    def integrate_with_external_system(self, system_url, data):
+        try:
+            response = requests.post(system_url, json=data)
+            if response.status_code == 200:
+                return "Data successfully sent to external system."
+            else:
+                return f"Failed to send data. Status code: {response.status_code}"
+        except Exception as e:
+            return f"Integration failed with error: {str(e)}"
+
+    # Emergency Handling
+    def handle_emergency(self, emergency_type):
+        if emergency_type == "disease_outbreak":
+            return "Disease outbreak detected. Isolate affected chickens and contact a veterinarian immediately."
+        elif emergency_type == "equipment_failure":
+            return "Equipment failure detected. Check the equipment immediately and perform necessary repairs."
+        else:
+            return "Unknown emergency type."
 
 
-def predict(image):
-    image_check = preprocess_image(image)
-    if image_check is None:
-        return "Image preprocessing failed.", None, None, None
+# Example usage of the chatbot with integrated Health Monitoring and Disease Diagnosis
+bot = PoultryFarmBot()
 
-    indx = auth_model.predict(image_check).argmax()
+# Health Monitoring and Disease Diagnosis
+image = None  # Replace with actual image input
+symptoms = "coughing and sneezing"
+diagnosis_result = bot.diagnose_disease(image=image, symptoms=symptoms)
+print(diagnosis_result)
 
-    if indx == 0:  # If the image is recognized as a chicken disease image
-        indx = my_model.predict(image_check).argmax()
-        name = name_disease.get(indx)
-        status = result.get(indx)
-        recom = recommend.get(indx)
+# Inventory Management
+print(bot.track_inventory("feed", 50))
+print(bot.track_inventory("Panadol", 10))
 
-        diagnosis = f"The chicken is in a {status} condition, diagnosed with {name}. The recommended medication is {recom}."
-        return diagnosis, name, status, recom
-    else:  # If the image is not recognized as a chicken disease image
-        return (
-            "The uploaded image is not recognized as a chicken or does not appear to be related to any known chicken diseases. "
-            "Please ensure the image is clear and shows a chicken or its symptoms to receive a proper diagnosis."
-        ), None, None, None
+# Reporting and Analytics
+print(bot.generate_report())
 
+# IoT Device Integration (Temperature and Humidity Monitoring)
+print(bot.monitor_environment(32, 35))
 
-def generate_combined_response(image, text):
-    if image is not None:
-        diagnosis, name, status, recom = predict(image)
-
-        if name and status and recom:  # If the disease is recognized
-            context = f"The chicken is in a {status} condition, diagnosed with {name}. The recommended medication is {recom}. "
-            if text:
-                context += f"Additionally, the user asked: '{text}'"
-            inputs = llama_tokenizer(context, return_tensors='pt', padding=True).to(device)
-            outputs = llama_model.generate(
-                inputs['input_ids'],
-                attention_mask=inputs['attention_mask'],  # Pass attention mask
-                max_length=500,
-                do_sample=True
-            )
-            advice = llama_tokenizer.decode(outputs[0], skip_special_tokens=True)
-            return diagnosis + "\n\nAdditional Advice: " + advice
-        else:
-            return diagnosis
-    elif text:
-        return chat_response(text)
-    else:
-        return "Please provide an image or ask a question."
+# Integration with External Systems
+data_to_send = {"temperature": 32, "humidity": 35}
+print(bot.integrate_with_external_system("https://api.external-system.com/data", data_to_send))
+
+# Emergency Handling
+print(bot.handle_emergency("disease_outbreak"))
 
 
-def chat_response(user_input):
-    inputs = llama_tokenizer(user_input, return_tensors='pt', padding=True).to(device)
-    outputs = llama_model.generate(
-        inputs['input_ids'],
-        attention_mask=inputs['attention_mask'],  # Pass attention mask
-        max_length=500,
-        do_sample=True
-    )
-    response = llama_tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return response
+# Gradio Interface for Health Monitoring
+def generate_combined_response(image, text):
+    diagnosis, name, status, recom = bot.diagnose_disease(image=image, symptoms=text)
+
+    if name and status and recom:  # If the disease is recognized
+        context = f"The chicken is in a {status} condition, diagnosed with {name}. The recommended medication is {recom}. "
+        if text:
+            context += f"Additionally, the user asked: '{text}'"
+        inputs = llama_tokenizer(context, return_tensors='pt', padding=True).to(device)
+        outputs = llama_model.generate(
+            inputs['input_ids'],
+            attention_mask=inputs['attention_mask'],  # Pass attention mask
+            max_length=500,
+            do_sample=True
+        )
+        advice = llama_tokenizer.decode(outputs[0], skip_special_tokens=True)
+        return diagnosis + "\n\nAdditional Advice: " + advice
+    else:
+        return diagnosis
 
 
 # Gradio Interface
 interface = gr.Interface(
     fn=generate_combined_response,
-    inputs=[gr.Image(label='Upload Image'), gr.Textbox(label='Ask a question')],
+    inputs=[gr.Image(label='Upload Image'), gr.Textbox(label='Describe symptoms or ask a question')],
     outputs=gr.Textbox(label="Response")
 )