Update app.py

app.py

@@ -1,11 +1,18 @@
 import gradio as gr
 import replicate
 
-DEPLOYMENT_URI = "dd-ds-ai/lora-test-01-deployment-test"
 
+DEPLOYMENT_URIS = {
+    "Lora 500": "dd-ds-ai/lora-test-01-deployment-test",
+    "Lora 1000": "dd-ds-ai/lora-test-01-deployment-test",
+    "Lora 2000": "dd-ds-ai/lora-test-01-deployment-test"
+}
 
-def generate_image(lora_scale, guidance_scale, prompt_strength, num_steps, prompt):
-    deployment = replicate.deployments.get(DEPLOYMENT_URI)
+
+def generate_image(model_selection, lora_scale, guidance_scale, prompt_strength, num_steps, prompt):
+    deployment_uri = DEPLOYMENT_URIS[model_selection]
+    deployment = replicate.deployments.get(deployment_uri)
+    
     prediction = deployment.predictions.create(
         input={
             "model": "dev",
@@ -21,6 +28,7 @@ def generate_image(lora_scale, guidance_scale, prompt_strength, num_steps, promp
             "prompt": prompt
         }
     )
+    
     prediction.wait()
     output = prediction.output
     image_url = output[0] if output else None
@@ -29,23 +37,22 @@ def generate_image(lora_scale, guidance_scale, prompt_strength, num_steps, promp
 
 # Gradio-Interface erstellen
 def create_gradio_interface():
+    model_selection = gr.Radio(choices=["Lora 500", "Lora 1000", "Lora 2000"], label="Model Selection", value="Lora 1000")
+    
     lora_scale = gr.Slider(0, 2, value=1, step=0.1, label="Lora Scale")
     guidance_scale = gr.Slider(1, 10, value=3.5, step=0.1, label="Guidance Scale")
     prompt_strength = gr.Slider(0, 1, value=0.8, step=0.1, label="Prompt Strength")
     num_steps = gr.Slider(1, 50, value=28, step=1, label="Number of Inference Steps")
     prompt = gr.Textbox(label="Prompt", value="a person reading the hamburger abendblatt newspaper")
 
-    # Erstelle ein Button-Interface für die Bildgenerierung
     generate_btn = gr.Button("Bild generieren")
 
-    # Gradio Interface
     interface = gr.Interface(
-        fn=generate_image,  # Die Funktion, die aufgerufen wird
-        inputs=[lora_scale, guidance_scale, prompt_strength, num_steps, prompt],  # Eingaben
-        outputs=gr.Image(label="Generated Image"),  # Ausgabe als Bild
+        fn=generate_image,  
+        inputs=[model_selection, lora_scale, guidance_scale, prompt_strength, num_steps, prompt], 
+        outputs=gr.Image(label="Generated Image"),
     )
 
-    # Binde den Button an die Bildgenerierung
     interface.launch(share=True)
 
 
@@ -53,5 +60,4 @@ def create_gradio_interface():
 if __name__ == "__main__":
     create_gradio_interface()
 
-
-# demo.queue().launch()
+