Update app.py

app.py

@@ -1,142 +1,106 @@
-import gradio as gr
-import numpy as np
+from flask import Flask, request, jsonify
+import aiohttp
+import asyncio
+import io
+import cloudinary
+import cloudinary.uploader
+import cloudinary.api
 import random
-#import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
-import torch
+import os
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo" #Replace to the model you would like to use
+app = Flask(__name__)
 
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
+# Configure Cloudinary
+cloudinary.config(
+    cloud_name='dpnixluze',
+    api_key='417356221754679',
+    api_secret='MjsdHI-8vvYg-yF8p5__aK_8OYs'
+)
 
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
+API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-xl-base-1.0"
+HEADERS = {"Authorization": "Bearer "}
 
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
+async def query(session, payload):
+    try:
+        async with session.post(API_URL, headers=HEADERS, json=payload) as response:
+            response.raise_for_status()
+            return await response.read()
+    except aiohttp.ClientError as e:
+        print(f"Error during API request: {e}")
+        return None
 
-#@spaces.GPU #[uncomment to use ZeroGPU]
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
+async def retry_query(payload, retries=5, delay=30):
+    async with aiohttp.ClientSession() as session:
+        for i in range(retries):
+            image_bytes = await query(session, payload)
+            if image_bytes:
+                return image_bytes
+            print(f"Retrying ({i + 1}/{retries})...")
+            await asyncio.sleep(delay)
+    return None
 
-    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
+@app.route('/generate-image', methods=['POST'])
+async def generate_image():
+    try:
+        data = request.json  # This is synchronous
+        positive_prompt = data.get('positive_prompt', 'emma stone')
+        negative_prompt = data.get('negative_prompt', '[deformed | disfigured] , poorly drawn, [bad : wrong] anatomy, [extra | missing | floating | disconnected] limb, (mutated hands and fingers) , blurry,clothes, bad lighting, low-quality, deformed, text, poorly drawn, holding camera, bad art, bad angle, boring, low-resolution, worst quality, bad composition, disfigured')
+        style = data.get('style','');
+       
 
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
+        if style == "cinematic":
+            style = "cinematic shot, dynamic lighting, 75mm, Technicolor, Panavision, cinemascope, sharp focus, fine details, 8k, HDR, realism, realistic, key visual, film still, superb cinematic color grading, depth of field"
+        elif style == "realistic":
+            style = "realistic style, natural lighting, true-to-life details, high resolution, sharp focus, fine textures, authentic colors, accurate proportions, high dynamic range, clear and photorealistic"  
+        elif style == "sci-fi":
+            style = "sci-fi style, futuristic technology, cyberpunk cityscape, neon lights, advanced robotics, alien landscapes, holographic interfaces, space exploration, dystopian themes, high-tech machinery"
+        elif style == "disney":
+            style = "3d, Disney character style, animated, cartoonish, whimsical, colorful, playful, charming, magical, fantastical, cute, endearing, family-friendly, storybook quality, iconic, expressive, vibrant colors, smooth lines, simple shapes, happy and adventurous"    
+        else:
+            style = "fantasy style, magical landscapes, mythical creatures, enchanted forests, fairy tale elements, mystical realms, legendary beings, glowing effects, ethereal atmosphere, magical artifacts, ancient ruins"  
+       
+        seed = random.randint(0, 10000)
 
-css="""
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
+        payload = {
+            "inputs": f"{positive_prompt}, {style}",
+            "negative_prompt": negative_prompt,
+            "options": { 
+                "resolution": "4096×2160",
+                "quality": "high",
+                "seed": seed
+            }
+        }
 
-with gr.Blocks(css=css) as demo:
-    
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""
-        # Text-to-Image Gradio Template
-        """)
-        
-        with gr.Row():
-            
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-            
-            run_button = gr.Button("Run", scale=0)
-        
-        result = gr.Image(label="Result", show_label=False)
+        image_urls = []
 
-        with gr.Accordion("Advanced Settings", open=False):
-            
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-            
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-            
-            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(
-        triggers=[run_button.click, prompt.submit],
-        fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result, seed]
-    )
+        for image_count in range(1):  # Generate 3 images
+            # Retry mechanism with error handling
+            for attempt in range(3):  # Try up to 3 times per image
+                image_bytes = await retry_query(payload)
+                if image_bytes:
+                    try:
+                        # Upload image to Cloudinary
+                        upload_response = cloudinary.uploader.upload(io.BytesIO(image_bytes), resource_type="image")
+                        cloudinary_url = upload_response.get('secure_url')
 
-demo.queue().launch()
+                        if cloudinary_url:
+                            image_urls.append(cloudinary_url)
+                            break  # Break out of retry loop for this image
+                        else:
+                            raise Exception('Failed to upload image to Cloudinary.')
+                    except Exception as upload_exception:
+                        print(f"Upload attempt {attempt + 1} failed: {upload_exception}")
+                else:
+                    print(f"Image generation attempt {attempt + 1} failed")
+
+        if image_urls:
+            return jsonify({'image_urls': image_urls})
+        else:
+            return jsonify({'error': 'Failed to generate and upload images after multiple attempts.'}), 500
+
+    except Exception as e:
+        print(f"Exception occurred: {e}")
+        return jsonify({'error': 'An unexpected error occurred.'}), 500
+
+if __name__ == '__main__':
+    app.run(debug=True)