Update app.py

app.py

@@ -1,22 +1,26 @@
-from transformers import AutoTokenizer, T5ForConditionalGeneration
+import gradio as gr
 
-# Load your model and tokenizer
-tokenizer = AutoTokenizer.from_pretrained("t5-base")
-model = T5ForConditionalGeneration.from_pretrained("t5-base")
+# Define the sidebar items
+sidebar_items = [
+    {"prompt": "Hello world!", "description": "A simple greeting."},
+    {"prompt": "What is your name?", "description": "Ask a user for their name."},
+    # Add more prompt examples and descriptions here...
+]
 
-# Modify the prompt to focus on blockchain security and secure devops
-prompt = "I am a neural network trained on the task of generating advice for improving blockchain security in secure development environments. Here are my recommendations: "
+def greet(name):
+    return f"Hello {name}!"
 
-# Increase max tokens for longer outputs
-max_length = 1024
-
-# Generate output with modified prompt
-generated_output = model.generate(
-    prompt,
-    max_length=max_length,
-    num_return_sequences=1,
-    return_dict_in_generate=True,
-    temperature=0.7
+# Create the interface with left sidebar
+iface = gr.Interface(
+    fn=greet,
+    inputs="text",
+    outputs="text",
+    title="Greeting App",
+    description="Ask a user for their name and greet them."
 )
 
-print(generated_output)
+# Add the left sidebar with prompt examples
+iface.sidebar(sidebar_items)
+
+# Launch the Gradio app
+iface.launch()