Update app.py

app.py

@@ -1,53 +1,3 @@
-import gradio as gr
-import spaces
-import torch
-import keras
-import keras_nlp
 import tensorflow as tf
 
-gpus = tf.config.experimental.list_physical_devices('GPU')
-if gpus:
-    try:
-        # Restrict TensorFlow to only allocate memory as needed
-        for gpu in gpus:
-            tf.config.experimental.set_memory_growth(gpu, True)
-    except RuntimeError as e:
-        print(e)
-
-keras.utils.set_random_seed(42)
-
-def create_model():
-    gemma_lm = keras_nlp.models.CausalLM.from_preset("hf://soufyane/gemma_2b_instruct_FT_DATA_SCIENCE_lora36_1")
-    return gemma_lm
-
-gemma_lm = create_model()
-    
-@spaces.GPU
-def generate_answer(history, question):
-    # Replace this with the actual code to generate the answer using your model
-    answer = gemma_lm.generate(f"You are an AI Agent specialized to answer to questions about Data Science and be greatfull and nice and helpfull\n\nQuestion:\n{question}\n\nAnswer:\n", max_length=1024)
-    history.append((question, answer))
-    return history
-
-# Gradio interface
-with gr.Blocks() as demo:
-    gr.Markdown("# Chatbot")
-    chatbot = gr.Chatbot()
-    with gr.Row():
-        txt = gr.Textbox(show_label=False, placeholder="Enter your question here...")
-        txt.submit(generate_answer, [chatbot, txt], chatbot)
-
-# Launch the interface
-demo.launch()
-
-###################################
-# zero = torch.Tensor([0]).cuda()
-# print(zero.device) # <-- 'cpu' 🤔
-
-# @spaces.GPU
-# def greet(n):
-#     print(zero.device) # <-- 'cuda:0' 🤗
-#     return f"Hello {zero + n} Tensor"
-
-# demo = gr.Interface(fn=greet, inputs=gr.Number(), outputs=gr.Text())
-# demo.launch()
+print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))