Update app.py

app.py

@@ -1,22 +1,18 @@
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
+from transformers import pipeline
 
-# Load the tokenizer and model
-tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom-560m")
-model = AutoModelForCausalLM.from_pretrained("bigscience/bloom-560m")
+# Load the pipeline
+pipe = pipeline("text-generation", model="bigscience/bloom-560m")
 
-# Define a function to generate text
+# Define a function to generate text using the pipeline
 def generate_text(prompt):
-    input_ids = tokenizer.encode(prompt, return_tensors="pt")
-    output = model.generate(input_ids, max_length=100, num_return_sequences=1)
-    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+    generated_text = pipe(prompt, max_length=100)[0]['generated_text']
     return generated_text
 
 # Create a Gradio interface
 interface = gr.Interface(
     fn=generate_text,
-    inputs=gr.Textbox("text", label="Digite seu texto aqui:", lines=5),  # Increase lines for easier text input.
+    inputs=gr.Textbox("text", label="Digite seu texto aqui:", lines=5),
     outputs=gr.Textbox("text", label="Texto Gerado:")
 )