Upload app.py

app.py

@@ -0,0 +1,48 @@
+import os
+import random
+import gradio as gr
+import requests
+from PIL import Image
+
+from utils import read_css_from_file
+from inference import generate_image_from_text, generate_image_from_text_with_persistent_storage
+# Read CSS from file
+css = read_css_from_file("style.css")
+
+DESCRIPTION = '''
+    <div id="content_align">
+        <span style="color:darkred;font-size:32px:font-weight:bold">
+        EfficientCLIP-GAN Models Image Generation Demo
+        </span>
+    </div>
+    <div id="content_align">
+        <span style="color:blue;font-size:16px:font-weight:bold">
+        Generate images directly from text prompts
+        </span>
+    </div>
+    <div id="content_align" style="margin-top: 10px;">
+    </div>
+'''
+
+# Creating Gradio interface
+with gr.Blocks(css=css) as app:
+    gr.Warning("This 💻 demo uses the EfficientCLIP-GAN model which is trained on CUB dataset 🐦🐥.\nKeep your prompt coherent to the birds domain.\nIf you like the demo, don't forget to click on the like 💖 button.")
+    gr.Markdown(DESCRIPTION)
+    with gr.Row():
+        with gr.Column():
+            text_prompt = gr.Textbox(label="Input Prompt", placeholder="", lines=3)
+    generate_button = gr.Button("Generate Image", variant='primary')
+
+    with gr.Row():
+        with gr.Column():
+            image_output1 = gr.Image(type="pil", label="Image Output 1")
+            image_output2 = gr.Image(type="pil", label="Image Output 2")
+
+        with gr.Column():
+            image_output3 = gr.Image(type="pil", label="Image Output 3")
+            image_output4 = gr.Image(type="pil", label="Image Output 4")
+
+    generate_button.click(generate_image_from_text, inputs=[text_prompt], outputs=[image_output1, image_output2, image_output3, image_output4])
+
+# Launch the app
+app.launch()