Update app.py

app.py

@@ -1,60 +1,85 @@
 import gradio as gr
-import numpy as np
+import asyncio
+import aiohttp
+import os
 import random
 
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
-import torch
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
+# --- GLIF API Configuration ---
+GLIF_API_TOKEN = os.getenv("GLIF_API_TOKEN0")
+GLIF_API_URL = "https://simple-api.glif.app"
+glif_token_id = 0
+glif_tokens_tried = 0
+no_of_accounts = 11
+
+
+if not GLIF_API_TOKEN:
+    raise ValueError("GLIF_API_TOKEN must be set.")
+
+
+# --- Asynchronous Image Generation Function ---
+async def generate_image_async(prompt, aspect_ratio, style):
+    global glif_token_id
+    global GLIF_API_TOKEN
+    global glif_tokens_tried
+    global no_of_accounts
+    payload = {
+        "id": "cm3ugmzv2002gnckiosrwk6xi",
+        "inputs": [prompt, aspect_ratio, style],
+    }
+    headers = {"Authorization": f"Bearer {GLIF_API_TOKEN}"}
+
+    async with aiohttp.ClientSession() as session:
+        try:
+            async with session.post(
+                GLIF_API_URL, json=payload, headers=headers, timeout=15
+            ) as response:
+                response.raise_for_status()
+                response_data = await response.json()
+                if "error" in response_data:
+                    if 'error 429' in response_data['error']:
+                        if glif_tokens_tried<no_of_accounts:
+                            glif_token_id = (glif_token_id+1)%no_of_accounts
+                            glif_tokens_tried+=1
+                            GLIF_API_TOKEN = os.getenv(f"GLIF_API_TOKEN{glif_token_id}")
+                            response_data = await generate_image_async(prompt, aspect_ratio, style)
+                            glif_tokens_tried = 0
+                            return response_data
+                        response_data = "No credits available"
+                    return response_data
+                elif "output" in response_data:
+                    image_url = response_data["output"]
+                    async with session.get(image_url) as image_response:
+                            image_response.raise_for_status()
+                            image_bytes = await image_response.read()
+                            return image_bytes
+                else:
+                     return "Error: Unexpected response from server"
+
+        except asyncio.TimeoutError:
+            return "Error: API request timed out."
+        except aiohttp.ClientError as e:
+            return f"API request failed: {e}"
+        except Exception as e:
+            return f"Exception:{e}"
+
+# --- Gradio Interface Function ---
+async def infer(
     prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
+    aspect_ratio,
+    style,
+    progress=gr.Progress(track_tqdm=True)
 ):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
-
-    return image, seed
+    
+    image_bytes_or_error = await generate_image_async(prompt, aspect_ratio, style)
 
+    return image_bytes_or_error
 
+# --- Gradio UI Setup ---
 examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
+    ["Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", "1:1", "anime"],
+    ["An astronaut riding a green horse", "16:9", "realistic"],
+    ["A delicious ceviche cheesecake slice", "4:3", "anime"],
 ]
 
 css = """
@@ -66,7 +91,7 @@ css = """
 
 with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
+        gr.Markdown(" # GLIF Text-to-Image")
 
         with gr.Row():
             prompt = gr.Text(
@@ -81,74 +106,41 @@ with gr.Blocks(css=css) as demo:
 
         result = gr.Image(label="Result", show_label=False)
 
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
+        with gr.Accordion("Settings", open=True):
+            aspect_ratio = gr.Dropdown(
+                label="Aspect Ratio",
+                choices=[
+                    "1:1",
+                    "3:4",
+                    "4:3",
+                    "9:16",
+                    "16:9",
+                    "9:21",
+                    "21:9",
+                ],
+                value="1:1",
             )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
+            style = gr.Dropdown(
+                label = "Style",
+                choices = [
+                    "anime",
+                    "realistic"
+                ],
+                value = "anime"
             )
 
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
+        gr.Examples(examples=examples, inputs=[prompt, aspect_ratio, style])
+
+    demo.on(
         triggers=[run_button.click, prompt.submit],
         fn=infer,
         inputs=[
             prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
+            aspect_ratio,
+            style,
         ],
-        outputs=[result, seed],
+        outputs=[result],
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()