Update app.py

app.py

@@ -1,12 +1,23 @@
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 from PIL import Image
 
-# Load tokenizer and model
-tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B")
-model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neo-2.7B")
+# 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
+tokenizer = AutoTokenizer.from_pretrained("stablediffusionapi/juggernaut-xl-v8")
+model = AutoModelForCausalLM.from_pretrained("stablediffusionapi/juggernaut-xl-v8")
+
+# 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")
@@ -24,13 +35,13 @@ def generate_image(text):
 
 # Create Gradio interface
 iface = gr.Interface(
-    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.",
+    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.",
     theme="huggingface"
 )
 
-# Launch Gradio interface
-iface.launch()
+# Launch the interface
+iface.launch()