Update app.py

app.py

@@ -7,37 +7,24 @@ 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
-pipe = pipeline(
+text_generator = pipeline(
     "text-generation",
     model="Ar4ikov/gpt2-650k-stable-diffusion-prompt-generator",
     tokenizer="gpt2"
 )
 
-# Initialize a list to store the history of generated prompts
-history = []
-
-# Function to generate text based on input prompt and record the history
-def generate_text(prompt):
-    generated_text = pipe(prompt, max_length=77)[0]["generated_text"]
-    # Append the generated prompt and its result to the history list
-    history.append({"prompt": prompt, "generated_text": generated_text})
-    return generated_text
-
-# Create a Gradio interface with history recording
-iface = gr.Interface(
-    fn=generate_text,
-    inputs=gr.Textbox(lines=5, label="Prompt"),
-    outputs=gr.Textbox(label="Output", show_copy_button=True),
-    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.",
-    api_name="predict"
-)
-# Load tokenizer and model
+# 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")
@@ -55,13 +42,13 @@ 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 the interface
-iface.launch(show_api=True)
+iface.launch()