Update app.py

app.py

@@ -1,30 +1,12 @@
 import gradio as gr
-import os
-import requests
-import random
-import time
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
 from PIL import Image
-from transformers import pipeline
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
-
-# Load the pipeline for text generation
-text_generator = pipeline(
-    "text-generation",
-    model="Ar4ikov/gpt2-650k-stable-diffusion-prompt-generator",
-    tokenizer="gpt2"
-)
 
-# Load tokenizer and model for image generation
+# Load tokenizer and model
 tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B")
 model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neo-2.7B")
 
-# Function to generate text based on input prompt
-def generate_text(prompt):
-    return text_generator(prompt, max_length=77)[0]["generated_text"]
-
-# Function to generate image based on input text
 def generate_image(text):
     # Tokenize input text
     input_ids = tokenizer.encode(text, return_tensors="pt")
@@ -42,13 +24,13 @@ def generate_image(text):
 
 # Create Gradio interface
 iface = gr.Interface(
-    fn=[generate_text, generate_image],
-    inputs=["textbox", "textbox"],
-    outputs=["textbox", "image"],
-    title="AI Art Prompt Generator",
-    description="Art Prompt Generator is a user-friendly interface designed to optimize input for AI Art Generator or Creator. For faster generation speeds, it's recommended to load the model locally with GPUs, as the online demo at Hugging Face Spaces utilizes CPU, resulting in slower processing times.",
+    fn=generate_image,
+    inputs=gr.inputs.Textbox(lines=3, label="Input Text"),
+    outputs="image",
+    title="Text-to-Image Generator",
+    description="Generate images from text using Hugging Face's GPT-Neo model.",
     theme="huggingface"
 )
 
-# Launch the interface
+# Launch Gradio interface
 iface.launch()