Update app.py

app.py

@@ -1,47 +1,32 @@
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
-from PIL import Image
+from transformers import pipeline
 
 # Load the pipeline for text generation
-text_generator = pipeline(
+pipe = 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")
+# Initialize a list to store the history of generated prompts
+history = []
 
-# Function to generate text based on input prompt
+# Function to generate text based on input prompt and record the history
 def generate_text(prompt):
-    return text_generator(prompt, max_length=77)[0]["generated_text"]
+    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
 
-# Function to generate image based on input text
-def generate_image(text):
-    # Tokenize input text
-    input_ids = tokenizer.encode(text, return_tensors="pt")
-
-    # Generate image conditioned on input text
-    output = model.generate(input_ids, do_sample=True, max_length=128, num_return_sequences=1)
-
-    # Decode generated image tokens to get image
-    image_bytes = tokenizer.decode(output[0], skip_special_tokens=True)
-
-    # Convert image bytes to PIL image
-    image = Image.open(image_bytes)
-
-    return image
-
-# Create Gradio interface
+# Create a Gradio interface with history recording
 iface = gr.Interface(
-    fn=[generate_text, generate_image],
-    inputs=["textbox", "textbox"],
-    outputs=["textbox", "image"],
+    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.",
-    theme="huggingface"
+    api_name="predict"
 )
 
 # Launch the interface
-iface.launch()
+iface.launch(show_api=True)