"Update space"

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

app.py

@@ -1,7 +1,7 @@
 import os
+import openai  # Import the OpenAI library
 import tensorflow as tf
 import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
 from keras.models import load_model
 import gradio as gr
 import cv2
@@ -18,9 +18,10 @@ if tok:
 else:
     print("Warning: Hugging Face token not found in environment variables.")
 
-# Check GPU availability for both TensorFlow and PyTorch
-print("Torch GPU available:", torch.cuda.is_available())
-print("Number of GPUs:", torch.cuda.device_count())
+# Set your OpenAI API key
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+# Check GPU availability for TensorFlow
 print("TensorFlow version:", tf.__version__)
 print("Eager execution:", tf.executing_eagerly())
 print("TensorFlow GPU Available:", tf.config.list_physical_devices('GPU'))
@@ -46,21 +47,6 @@ except Exception as e:
     print(f"Error loading models: {e}")
     raise
 
-# 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 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',
-    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32  # Use mixed precision if on GPU
-).to(device)
-
-# Explicitly set the pad token if not set
-if llama_tokenizer.pad_token_id is None:
-    llama_tokenizer.pad_token = llama_tokenizer.eos_token
-
 # Dictionaries for disease names, results, and recommendations
 name_disease = {0: 'Coccidiosis', 1: 'Healthy', 2: 'New Castle Disease', 3: 'Salmonella'}
 result = {0: 'Critical', 1: 'No issue', 2: 'Critical', 3: 'Critical'}
@@ -201,7 +187,7 @@ print(bot.integrate_with_external_system("https://api.external-system.com/data",
 print(bot.handle_emergency("disease_outbreak"))
 
 
-# Gradio Interface for Health Monitoring
+# Function to generate a response using OpenAI's GPT model
 def generate_combined_response(image, text):
     diagnosis, name, status, recom = bot.diagnose_disease(image=image, symptoms=text)
 
@@ -209,14 +195,14 @@ def generate_combined_response(image, text):
         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
+
+        # Use OpenAI's GPT model to generate additional advice
+        response = openai.Completion.create(
+            model="gpt-3.5-turbo",  # Use GPT-4 or gpt-3.5-turbo based on your API access
+            prompt=context,
+            max_tokens=150
         )
-        advice = llama_tokenizer.decode(outputs[0], skip_special_tokens=True)
+        advice = response.choices[0].text.strip()
         return diagnosis + "\n\nAdditional Advice: " + advice
     else:
         return diagnosis

requirements.txt

@@ -6,4 +6,4 @@ transformers~=4.43.3
 numpy~=1.23.5
 torchvision
 accelerate
-
+openai