Update app.py

app.py

@@ -1,30 +1,27 @@
 import gradio as gr
-from diffusers import StableDiffusionPipeline
-import torch
+from transformers import pipeline
 
-# Load a lightweight stable diffusion model
-model_id = "CompVis/stable-diffusion-v1-4"
-pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
-pipe = pipe.to("cuda")  # Use GPU for faster generation, or .to("cpu") if no GPU is available
+# Load a text generation model (GPT-2 for simplicity)
+generator = pipeline('text-generation', model='gpt2')
 
-def generate_character(name, gender, trait, superpower):
-    description = f"A {gender} character named {name}, who is {trait}. Their superpower is {superpower}."
-    # Generate an image based on the description
-    image = pipe(description).images[0]
-    return image
+def generate_character_description(name, gender, trait, superpower):
+    prompt = f"Generate a detailed description of a character named {name}. They are {gender}, {trait}, and their superpower is {superpower}. Provide a short story or background for this character."
+    # Generate the description based on the prompt
+    description = generator(prompt, max_length=200)[0]['generated_text']
+    return description
 
 # Gradio interface
 iface = gr.Interface(
-    fn=generate_character,
+    fn=generate_character_description,
     inputs=[
         gr.Textbox(label="Name", placeholder="Enter the character's name"),
         gr.Radio(label="Gender", choices=["male", "female", "non-binary"]),
         gr.Textbox(label="Personality Trait", placeholder="e.g., brave, clever, kind"),
         gr.Textbox(label="Superpower", placeholder="e.g., flying, invisibility, super strength")
     ],
-    outputs="image",
-    title="Lightweight Character Generator",
-    description="Enter the details of your character and generate an image!"
+    outputs="text",
+    title="Character Description Generator",
+    description="Enter details about your character to generate a description or background story!"
 )
 
 iface.launch()