Upload app.py

app.py

@@ -1,50 +1,43 @@
 import gradio as gr
-import requests
-from PIL import Image
-from io import BytesIO
+from gradio_client import Client
 
-API_URL = "https://YOUR-BACKEND-SPACE.hf.space/run/predict"
+# 🔴 Replace with your backend Space URL
+BACKEND_URL = "https://florafeng-uclip-img-gen.hf.space"
 
+client = Client(BACKEND_URL)
 
-def generate(prompt, seed):
-    response = requests.post(
-        API_URL,
-        json={
-            "data": [prompt, int(seed)]
-        }
-    )
-
-    if response.status_code != 200:
-        return f"Error: {response.status_code} - {response.text}"
-
-    data = response.json()
-
-    result = data["data"][0]
 
-    # If backend returns a PIL image serialized as URL/path
-    if isinstance(result, str) and result.startswith("http"):
-        img_bytes = requests.get(result).content
-        image = Image.open(BytesIO(img_bytes))
-    else:
-        # fallback (Gradio usually handles this automatically)
+def generate(prompt, seed):
+    try:
+        result = client.predict(
+            prompt,
+            seed,
+            api_name="/predict"  # default for gr.Interface
+        )
         return result
 
-    return image
+    except Exception as e:
+        return f"Error: {str(e)}"
 
 
 with gr.Blocks() as demo:
     gr.Markdown("# 🎨 AI Bedroom Generator")
 
-    prompt = gr.Textbox(
-        label="Prompt",
-        value="a unique bedroom with dolls, pillows, paintings"
-    )
-
-    seed = gr.Number(value=123, label="Seed")
+    with gr.Row():
+        prompt = gr.Textbox(
+            label="Prompt",
+            value="a unique bedroom with dolls, pillows, paintings"
+        )
+        seed = gr.Number(value=123, label="Seed")
 
     btn = gr.Button("Generate")
-    output = gr.Image()
 
-    btn.click(generate, inputs=[prompt, seed], outputs=output)
+    output = gr.Image(label="Generated Image")
+
+    btn.click(
+        fn=generate,
+        inputs=[prompt, seed],
+        outputs=output
+    )
 
 demo.launch()