Update app.py

app.py

@@ -15,8 +15,24 @@ def query(payload, api_url, token):
     response = requests.post(api_url, headers=headers, json=payload)
     return io.BytesIO(response.content)
 
+# Define the function to run when the button is clicked
+def run_lora(prompt, weight):
+    print("Inside run_lora")
+    selected_lora = loras[0]  # You may need to adjust this index if you have multiple models
+    api_url = f"https://api-inference.huggingface.co/models/{selected_lora['repo']}"
+    trigger_word = selected_lora["trigger_word"]
+    token = os.getenv("API_TOKEN")  # This will read the API token set in your managed environment
+    payload = {"inputs": f"{prompt} {trigger_word}"}
+    
+    print("Calling query function...")
+    image_bytes = query(payload, api_url, token)
+    print("Query function executed successfully.")
+    return Image.open(image_bytes)
+
 # Gradio UI
-with gr.Blocks(css="custom.css") as demo:  # Added the css argument with "custom.css"
+print("Before Gradio Blocks")
+with gr.Blocks(css="custom.css") as demo:
+    print("Inside Gradio Blocks")
     title = gr.HTML(
         """<h1><img src="https://i.imgur.com/vT48NAO.png" alt="LoRA"> LoRA the Explorer</h1>""",
         elem_id="title",
@@ -34,22 +50,10 @@ with gr.Blocks(css="custom.css") as demo:  # Added the css argument with "custom
     weight = gr.Slider(0, 10, value=1, step=0.1, label="LoRA weight")
     result = gr.Image(interactive=False, label="Generated Image", elem_id="result-image")
 
-    # Define the function to run when the button is clicked
-    def run_lora(prompt, weight):
-        selected_lora = loras[0]  # You may need to adjust this index if you have multiple models
-        api_url = f"https://api-inference.huggingface.co/models/{selected_lora['repo']}"
-        trigger_word = selected_lora["trigger_word"]
-        token = os.getenv("API_TOKEN")  # This will read the API token set in your managed environment
-        payload = {"inputs": f"{prompt} {trigger_word}"}
-        
-        print("Calling query function...")
-        image_bytes = query(payload, api_url, token)
-        print("Query function executed successfully.")
-        return Image.open(image_bytes)
-
-    print("Starting Gradio UI...")
-    gr.Interface(
-        fn=run_lora,
-        inputs=[prompt, weight],
-        outputs=[result],
-    ).launch()
+print("Before Gradio Interface")
+gr.Interface(
+    fn=run_lora,
+    inputs=[prompt, weight],
+    outputs=[result],
+).launch()
+print("After Gradio Interface")