Update app.py

app.py

@@ -1,34 +1,40 @@
-import gradio as gr  # For building the web interface
-from PIL import Image  # To handle image processing
-import numpy as np  # To create dummy images
-
-# Function to create a dummy image for testing
-def create_dummy_image(text):
-    # Create a blank white image
-    img = np.ones((256, 256, 3), dtype=np.uint8) * 255  # White image
-    return Image.fromarray(img)
-
-# Main function to generate comic panels from a user-provided story
-def generate_comic_panels(story_description):
-    scenes = story_description.split(". ")  # Split story into scenes using periods
-    images = []
-
-    for scene in scenes:
-        if len(scene.strip()) > 0:  # Skip empty sentences
-            image = create_dummy_image(scene)  # Create a dummy image for each scene
-            images.append(image)  # Append the image to the list
-
-    return images  # Return the list of images to be displayed as comic panels
-
-# Gradio interface setup
-demo = gr.Interface(
-    fn=generate_comic_panels,  # Function to call when user interacts
-    inputs=gr.Textbox(lines=5, placeholder="Enter your short story description here..."),  # Story input
-    outputs=gr.Gallery(label="Generated Comic Panels"),  # Display images in a gallery format
-    title="GenArt Narrative",
-    description="Enter a short story description, and we'll transform it into a comic strip!"
-)
-
-# Launch the app
-if __name__ == "__main__":
-    demo.launch()
+import torch
+from transformers import DallEProcessor, DallEModel
+from PIL import Image
+import requests
+from flask import Flask, request, jsonify
+import io
+
+app = Flask(__name__)
+
+# Initialize the DALL-E mini model and processor
+model_name = "dalle-mini/dalle-mini"
+processor = DallEProcessor.from_pretrained(model_name)
+model = DallEModel.from_pretrained(model_name)
+
+@app.route('/generate', methods=['POST'])
+def generate_image():
+    data = request.json
+    prompt = data.get('prompt', '')
+
+    # Generate images from prompt
+    inputs = processor(text=[prompt], return_tensors="pt")
+    outputs = model(**inputs)
+
+    # Post-process the generated image
+    generated_image = outputs.logits.argmax(-1)[0]
+    image = Image.fromarray(generated_image)
+
+    # Save image to a BytesIO object
+    img_byte_arr = io.BytesIO()
+    image.save(img_byte_arr, format='PNG')
+    img_byte_arr = img_byte_arr.getvalue()
+
+    return jsonify({'image': img_byte_arr.hex()})
+
+@app.route('/')
+def home():
+    return "Welcome to GenArt Narrative!"
+
+if __name__ == '__main__':
+    app.run(debug=True)