Create app.py

app.py

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+import gradio as gr
+import requests
+import json
+import os
+
+# Hugging Face Inference API setup
+API_URL = "https://api-inference.huggingface.co/models/Benevolent/PonyDiffusionV10"
+HF_API_TOKEN = os.getenv("HF_API_TOKEN")  # Get token from environment variable
+headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}
+
+def query(payload):
+    response = requests.post(API_URL, headers=headers, json=payload)
+    return json.loads(response.content)
+
+# Function to call the HF Inference API
+def generate_image(prompt):
+    payload = {
+        "inputs": prompt,
+    }
+    response = query(payload)
+    if 'error' in response:
+        return f"Error: {response['error']}"
+    # Check for the generated image
+    if isinstance(response, list) and len(response) > 0 and "generated_image" in response[0]:
+        return response[0]["generated_image"]  # Base64 encoded image
+    return "No image returned from model"
+
+# Gradio Blocks Web UI
+with gr.Blocks() as demo:
+    with gr.Row():
+        gr.Markdown("# PonyDiffusion V10 Text-to-Image Generator")
+    
+    with gr.Row():
+        prompt = gr.Textbox(label="Enter a prompt for the image")
+
+    with gr.Row():
+        generate_button = gr.Button("Generate Image")
+        output_image = gr.Image()
+
+    # When generate_button is clicked, call generate_image
+    generate_button.click(fn=generate_image, inputs=prompt, outputs=output_image)
+
+# Launch the app
+demo.launch()