Update app.py

app.py

@@ -1,33 +1,22 @@
-import torch
-from diffusers import FluxPipeline
 import gradio as gr
+from transformers import Flux1
 
-# Load the model and set to CPU
-pipe = FluxPipeline.from_pretrained("Shakker-Labs/AWPortrait-FL", torch_dtype=torch.float32)
-pipe.to("cpu")  # Set to CPU
+# Load the model
+model = Flux1.from_pretrained("black-forest-labs/flux-1-dev")
 
-# Define the function to generate an image from a given prompt
 def generate_image(prompt):
-    image = pipe(prompt, 
-                 num_inference_steps=24, 
-                 guidance_scale=3.5,
-                 width=768, height=1024).images[0]
-    image.save(f"example.png")
+    # Generate an image using the model
+    image = model(prompt)
     return image
 
-# Create a Gradio interface
-def inference(prompt):
-    image = generate_image(prompt)
-    return image
-
-# Gradio Interface
-interface = gr.Interface(
-    fn=inference, 
-    inputs=gr.Textbox(lines=2, placeholder="Enter your image description here..."), 
-    outputs="image",
-    title="Text-to-Image Generator",
-    description="Enter a text prompt to generate an image using AWPortrait-FL."
+# Create the Gradio Interface
+demo = gr.Interface(
+    fn=generate_image, 
+    inputs=gr.Textbox(label="Prompt"), 
+    outputs=gr.Image(label="Generated Image"),
+    title="FLUX.1-dev Image Generation Bot",
+    description="Enter a prompt and generate an image using the FLUX.1-dev model."
 )
 
-# Launch the Gradio interface
-interface.launch()
+# Launch the Gradio Interface
+demo.launch()