Replaced direct model load with pipeline

app.py

@@ -10,21 +10,22 @@ if text:
     out = pipe(text)
     st.json(out)
 '''
-
-from transformers import AutoTokenizer, AutoModelForCausalLM
+from transformers import pipeline
 import torch
 
 model_name = "abacusai/Smaug-72B-v0.1"
 
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
+pipe = pipeline("text-generation", model=model_name)
 
-# Encode some input text
-input_text = "Who are you?"
-input_ids = tokenizer.encode(input_text, return_tensors='pt')
+# Prompting the user for input text
+input_text = input("Enter your prompt: ")
 
-# Generate text using the model
-output = model.generate(input_ids, max_length=50)
+# Generating text based on the input
+# Adjust parameters like max_length according to your needs
+generated_texts = pipe(input_text, max_length=50, num_return_sequences=1)
 
-# Decode and print the output
-print("Decoded output: " + tokenizer.decode(output[0], skip_special_tokens=True))
+# Displaying the generated text
+# Assuming we only want the first generated sequence for simplicity
+print("Generated text:")
+for generated_text in generated_texts:
+    print(generated_text['generated_text'])