edit

app.py

@@ -1,25 +1,16 @@
 import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
 
-# Load pre-trained GPT-3.5 model and tokenizer (you can replace this with your model)
-model_name = "EleutherAI/gpt-neo-2.7B"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
-
-def generate_text(input_text, max_length=50):
-    input_ids = tokenizer.encode(input_text, return_tensors="pt")
-    output = model.generate(input_ids, max_length=max_length, num_return_sequences=1)
-    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
-    return generated_text
+# Define a function that echoes the input text
+def echo_text(input_text):
+    return input_text
 
 # Create a Gradio interface
 iface = gr.Interface(
-    fn=generate_text,  # Your text generation function
+    fn=echo_text,  # Echo text function
     inputs=gr.Textbox(text="Enter text here..."),  # Text input field
-    outputs=gr.Textbox(),  # Display generated text
+    outputs=gr.Textbox(),  # Display echoed text
     live=True  # Real-time updates
 )
 
-# Launch the interface
+# Launch the Gradio interface
 iface.launch()