Update app.py

app.py

@@ -1,75 +1,8 @@
 import gradio as gr
-import numpy as np
-import random
 import os
-import requests
 
-# Fetch the API token from environment variable
+# Load the API token from the environment variable
 hf_token = os.getenv("HF_TOKEN")
-API_URL = "https://api-inference.huggingface.co/models/ZB-Tech/Text-to-Image"
+interface = gr.load("models/ZB-Tech/Text-to-Image", token=hf_token)
 
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, np.iinfo(np.int32).max)
-
-    headers = {"Authorization": f"Bearer {hf_token}"}
-    payload = {"inputs": prompt}  # Start with minimal payload
-    response = requests.post(API_URL, headers=headers, json=payload)
-
-    if response.status_code != 200:
-        raise Exception(f"API Error: {response.status_code} - {response.text}")
-
-    # Assuming the response is an image (binary data)
-    image = response.content  # Gradio will handle binary image data
-
-    return image, seed
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template (ZB-Tech Model via API)")
-
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
-
-        result = gr.Image(label="Result", show_label=False)
-
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[prompt],
-        outputs=[result],
-    )
-
-if __name__ == "__main__":
-    demo.launch()
+interface.launch()