Update app.py

app.py

@@ -1,88 +1,28 @@
 import gradio as gr
 import requests
-import io
-import random
-import os
-import time
 from PIL import Image
-from deep_translator import GoogleTranslator
-import json
-
-
+import io
 
-API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-3-medium-diffusers"
+API_URL = "https://api-inference.huggingface.co/models/Blane187/kana-arima-s1-ponyxl-lora-nochekaiser"
 API_TOKEN = os.getenv("HF_READ_TOKEN")
-headers = {"Authorization": f"Bearer {API_TOKEN}"}
-timeout = 100
 
-def query(prompt, is_negative=False, steps=30, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1, strength=0.7):
-    if prompt == "" or prompt == None:
-        return None
+headers = {"Authorization": f"Bearer {API_TOKEN}"}
 
-    key = random.randint(0, 999)
-    
-    API_TOKEN = random.choice([os.getenv("HF_READ_TOKEN")])
-    headers = {"Authorization": f"Bearer {API_TOKEN}"}
-    
-    prompt = GoogleTranslator(source='ru', target='en').translate(prompt)
-    print(f'\033[1mGeneration {key} translation:\033[0m {prompt}')
+def query(inputs):
+    response = requests.post(API_URL, headers=headers, json={"inputs": inputs})
+    image_bytes = response.content
+    image = Image.open(io.BytesIO(image_bytes))
+    return image
 
-    prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
-    print(f'\033[1mGeneration {key}:\033[0m {prompt}')
+with gr.Blocks() as demo:
+    gr.Markdown("## Generate an Image using Hugging Face Model")
     
-    payload = {
-        "inputs": prompt,
-        "is_negative": is_negative,
-        "steps": steps,
-        "cfg_scale": cfg_scale,
-        "seed": seed if seed != -1 else random.randint(1, 1000000000),
-        "strength": strength
-    }
-
-    response = requests.post(API_URL, headers=headers, json=payload, timeout=timeout)
-    if response.status_code != 200:
-        print(f"Error: Failed to get image. Response status: {response.status_code}")
-        print(f"Response content: {response.text}")
-        if response.status_code == 503:
-            raise gr.Error(f"{response.status_code} : The model is being loaded")
-        raise gr.Error(f"{response.status_code}")
+    with gr.Row():
+        prompt_input = gr.Textbox(label="Enter a prompt", placeholder="Astronaut riding a horse")
+        generate_btn = gr.Button("Generate Image")
     
-    try:
-        image_bytes = response.content
-        image = Image.open(io.BytesIO(image_bytes))
-        print(f'\033[1mGeneration {key} completed!\033[0m ({prompt})')
-        return image
-    except Exception as e:
-        print(f"Error when trying to open the image: {e}")
-        return None
-
-css = """
-#app-container {
-    max-width: 600px;
-    margin-left: auto;
-    margin-right: auto;
-}
-"""
+    output_image = gr.Image(label="Generated Image")
 
-with gr.Blocks(theme='Hev832/niceandsimple', css=css) as app:
-    gr.HTML("<center><h1>Stable Diffusion 3 Medium</h1></center>")
-    with gr.Column(elem_id="app-container"):
-        with gr.Row():
-            with gr.Column(elem_id="prompt-container"):
-                with gr.Row():
-                    text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here", lines=2, elem_id="prompt-text-input")
-                    text_button = gr.Button("Run", variant='primary', elem_id="gen-button")
-                with gr.Row():
-                    with gr.Accordion("Advanced Settings", open=False):
-                        negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="What should not be in the image", value="(deformed, distorted, disfigured), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, misspellings, typos", lines=3, elem_id="negative-prompt-text-input")
-                        steps = gr.Slider(label="Sampling steps", value=35, minimum=1, maximum=100, step=1)
-                        cfg = gr.Slider(label="CFG Scale", value=7, minimum=1, maximum=20, step=1)
-                        method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"])
-                        strength = gr.Slider(label="Strength", value=0.7, minimum=0, maximum=1, step=0.001)
-                        seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=1000000000, step=1)
-        with gr.Row():
-            image_output = gr.Image(type="pil", label="Image Output", elem_id="gallery")
-        
-        text_button.click(query, inputs=[text_prompt, negative_prompt, steps, cfg, method, seed, strength], outputs=image_output)
+    generate_btn.click(fn=query, inputs=prompt_input, outputs=output_image)
 
-app.launch(show_api=False, share=False)
+demo.launch()