Create app.py

app.py

@@ -0,0 +1,53 @@
+import gradio as gr
+import requests
+import io
+from PIL import Image
+import json
+import os
+
+# Load LoRAs from JSON
+with open('loras.json', 'r') as f:
+    loras = json.load(f)
+
+# API call function
+def query(payload, api_url, token):
+    headers = {"Authorization": f"Bearer {token}"}
+    response = requests.post(api_url, headers=headers, json=payload)
+    return io.BytesIO(response.content)
+
+# Gradio UI
+with gr.Blocks(css="custom.css") as demo:
+    title = gr.HTML(
+        """<h1><img src="https://i.imgur.com/vT48NAO.png" alt="LoRA"> LoRA the Explorer</h1>""",
+        elem_id="title",
+    )
+    selected_state = gr.State()
+    gallery = gr.Gallery(
+        value=[(item["image"], item["title"]) for item in loras],
+        label="LoRA Gallery",
+        allow_preview=False,
+        columns=3,
+        elem_id="gallery",
+        show_share_button=False
+    )
+    prompt = gr.Textbox(label="Prompt", show_label=False, lines=1, max_lines=1, placeholder="Type a prompt after selecting a LoRA", elem_id="prompt")
+    advanced_options = gr.Accordion("Advanced options", open=False)
+    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_state):
+        selected_lora = loras[selected_state]
+        api_url = f"https://api-inference.huggingface.co/models/{selected_lora['repo']}"
+        trigger_word = selected_lora["trigger_word"]
+        token = os.getenv("API_TOKEN")
+        payload = {"inputs": f"{prompt} {trigger_word}"}
+        
+        image_bytes = query(payload, api_url, token)
+        return Image.open(image_bytes)
+
+    prompt.submit(
+        fn=run_lora,
+        inputs=[prompt, weight, selected_state],
+        outputs=[result],
+    )