Update app.py

app.py

@@ -1,27 +1,18 @@
-from transformers import GPTJForCausalLM, GPTJTokenizer
 import gradio as gr
 
-# Load GPT-J model and tokenizer
-model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")
-tokenizer = GPTJTokenizer.from_pretrained("EleutherAI/gpt-j-6B")
+def greet(name, excitement_level=1.0, formality_level=0.5):
+    greeting = "Hello " + name + "!!"
+    modified_greeting = greeting + f" Excitement level: {excitement_level}, Formality level: {formality_level}"
+    return modified_greeting
 
-def generate_response(prompt, temperature=1.0, max_length=50):
-    # Tokenize prompt and generate response
-    input_ids = tokenizer(prompt, return_tensors="pt").input_ids
-    output_ids = model.generate(input_ids, max_length=max_length, temperature=temperature)
-    response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
-    return response
-
-# Set up Gradio interface
 iface = gr.Interface(
-    fn=generate_response,
-    inputs=[gr.Textbox(placeholder="Enter your prompt..."),
-            gr.Slider(minimum=0.1, maximum=2.0,  label="Temperature"),
-            gr.Slider(minimum=1, maximum=100,  label="Max Length")],
+    fn=greet,
+    inputs=[gr.Textbox(placeholder="Enter your name..."),
+            gr.Slider(minimum=0.1, maximum=2.0,  label="Excitement Level"),
+            gr.Slider(minimum=0.1, maximum=1.0,  label="Formality Level")],
     outputs=gr.Textbox(),
-    title="GPT-J Prompt Generator",
-    description="Generate responses based on a prompt with adjustable variables."
+    title="Greeting Generator",
+    description="Adjust excitement and formality levels."
 )
 
 iface.launch()
-