Create app.py

app.py

@@ -0,0 +1,70 @@
+import gradio as gr
+import requests
+import base64
+from PIL import Image
+from io import BytesIO
+from dotenv import load_dotenv
+import os
+
+# Load the .env file
+load_dotenv()
+
+# Get the API key from the environment variable
+api_key = os.getenv("API_KEY")
+
+# Define the endpoint URL
+invoke_url = "https://ai.api.nvidia.com/v1/genai/stabilityai/sdxl-turbo"
+
+# Define the function that interacts with the API
+def generate_image(prompt, seed=0, sampler="K_EULER_ANCESTRAL", steps=2):
+    # Define the headers with the correct API key
+    headers = {
+        "Authorization": f"Bearer {api_key}",
+        "Accept": "application/json",
+    }
+
+    # Define the payload for the POST request
+    payload = {
+        "text_prompts": [{"text": prompt}],
+        "seed": seed,
+        "sampler": sampler,
+        "steps": steps
+    }
+
+    # Make the POST request to the API
+    response = requests.post(invoke_url, headers=headers, json=payload)
+
+    # Raise an error if the request was unsuccessful
+    response.raise_for_status()
+
+    # Get the response body
+    response_body = response.json()
+
+    # Extract the base64 string from the response
+    base64_str = response_body['artifacts'][0]['base64']
+
+    # Decode the base64 string
+    image_data = base64.b64decode(base64_str)
+
+    # Open the image using PIL
+    image = Image.open(BytesIO(image_data))
+
+    return image
+
+# Define the Gradio interface
+inputs = [
+    gr.Textbox(label="Prompt", placeholder="Enter your image description here"),
+    gr.Number(label="Seed", value=0, precision=0),
+    gr.Dropdown(label="Sampler", choices=["K_EULER_ANCESTRAL", "DDIM", "PLMS"], value="K_EULER_ANCESTRAL"),
+    gr.Slider(label="Steps", minimum=1, maximum=100, value=2)
+]
+
+outputs = gr.Image(label="Generated Image")
+
+gr.Interface(
+    fn=generate_image,
+    inputs=inputs,
+    outputs=outputs,
+    title="Stable Diffusion XL Image Generator",
+    description="Generate images using Stable Diffusion XL by providing a text prompt."
+).launch()