Update myapp.py

myapp.py

@@ -1,72 +1,35 @@
-import os
-import io
-import random
-import torch
-from flask import Flask, jsonify, request, send_file
-from flask_cors import CORS
+from flask import Flask, request, jsonify
 from diffusers import DiffusionPipeline
-import numpy as np
+import torch
+from PIL import Image
+import os
 
 # Initialize the Flask app
 myapp = Flask(__name__)
-CORS(myapp)  # Enable CORS if needed
 
-# Load the model
-device = "cpu"
-repo = "prompthero/openjourney-v4"
-pipe = DiffusionPipeline.from_pretrained(repo).to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
+# Load the Diffusion pipeline
+pipe = DiffusionPipeline.from_pretrained("Yntec/PicXReal").to("cuda")
 
 @myapp.route('/')
-def home():
-    return "Welcome to the Image Generation API!"  # Basic home response
+def index():
+    return "Welcome to the Image Generation API!"
 
 @myapp.route('/generate_image', methods=['POST'])
 def generate_image():
     data = request.json
-    
-    # Get inputs from request JSON
-    prompt = data.get('prompt', '')
-    negative_prompt = data.get('negative_prompt', None)
-    seed = data.get('seed', 0)
-    randomize_seed = data.get('randomize_seed', True)
-    
-    # Get width and height and ensure they are divisible by 8
-    width = data.get('width', 1024)
-    height = data.get('height', 1024)
-    
-    # Round width and height to the nearest multiple of 8
-    width = (width // 8) * 8
-    height = (height // 8) * 8
+    prompt = data.get('prompt', 'Astronaut in a jungle, cold color palette, muted colors, detailed, 8k')
 
-    guidance_scale = data.get('guidance_scale', 5.0)
-    num_inference_steps = data.get('num_inference_steps', 28)
-    
-    # Randomize seed if requested
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    
     # Generate the image
-    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]
-    
-    # Save the image to a byte array
-    img_byte_arr = io.BytesIO()
-    image.save(img_byte_arr, format='PNG')
-    img_byte_arr.seek(0)  # Move the pointer to the start of the byte array
-    
-    # Return the image as a response
-    return send_file(img_byte_arr, mimetype='image/png')
+    image = pipe(prompt).images[0]
+
+    # Convert to PIL Image and save
+    pil_image = Image.fromarray(image.numpy())
+    output_path = f"{prompt.replace(' ', '_')}.png"  # Create a file name based on the prompt
+    pil_image.save(output_path)
+
+    # Return the path to the generated image
+    return jsonify({'image_path': output_path})
 
-# Add this block to make sure your app runs when called
 if __name__ == "__main__":
-    myapp.run(host='0.0.0.0', port=7860)  # Run the Flask app on port 7860
+    # Set the host and port
+    myapp.run(host='0.0.0.0', port=7860)