Create app.py

app.py

@@ -0,0 +1,171 @@
+import os
+import cv2
+import spaces
+from PIL import Image
+import gradio as gr
+import numpy as np
+import random
+import base64
+import requests
+import json
+import time
+
+# Add a new function for text-to-image generation
+def generate_garment_image(prompt):
+    # This is a placeholder function. You'll need to implement actual text-to-image generation here.
+    # For example, you might use a service like DALL-E, Stable Diffusion, or any other text-to-image model.
+    # For now, we'll just return a placeholder image.
+    placeholder_image = np.zeros((256, 256, 3), dtype=np.uint8)
+    cv2.putText(placeholder_image, prompt, (10, 128), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
+    return placeholder_image
+
+def tryon(person_img, garment_prompt, seed, randomize_seed):
+    post_start_time = time.time()
+    if person_img is None or garment_prompt == "":
+        return None, None, "Empty image or prompt"
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    
+    # Generate garment image from prompt
+    garment_img = generate_garment_image(garment_prompt)
+    
+    encoded_person_img = cv2.imencode('.jpg', cv2.cvtColor(person_img, cv2.COLOR_RGB2BGR))[1].tobytes()
+    encoded_person_img = base64.b64encode(encoded_person_img).decode('utf-8')
+    encoded_garment_img = cv2.imencode('.jpg', cv2.cvtColor(garment_img, cv2.COLOR_RGB2BGR))[1].tobytes()
+    encoded_garment_img = base64.b64encode(encoded_garment_img).decode('utf-8')
+
+    # Rest of the function remains the same
+    # ...
+
+def start_tryon(person_img, garment_prompt, seed, randomize_seed):
+    start_time = time.time()
+    if person_img is None or garment_prompt == "":
+        return None, None, "Empty image or prompt"
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    
+    # Generate garment image from prompt
+    garment_img = generate_garment_image(garment_prompt)
+    
+    encoded_person_img = cv2.imencode('.jpg', cv2.cvtColor(person_img, cv2.COLOR_RGB2BGR))[1].tobytes()
+    encoded_person_img = base64.b64encode(encoded_person_img).decode('utf-8')
+    encoded_garment_img = cv2.imencode('.jpg', cv2.cvtColor(garment_img, cv2.COLOR_RGB2BGR))[1].tobytes()
+    encoded_garment_img = base64.b64encode(encoded_garment_img).decode('utf-8')
+
+    # Rest of the function remains the same
+    # ...
+
+MAX_SEED = 999999
+
+example_path = os.path.join(os.path.dirname(__file__), 'assets')
+
+human_list = os.listdir(os.path.join(example_path,"human"))
+human_list_path = [os.path.join(example_path,"human",human) for human in human_list]
+
+css="""
+#col-left {
+    margin: 0 auto;
+    max-width: 430px;
+}
+#col-mid {
+    margin: 0 auto;
+    max-width: 430px;
+}
+#col-right {
+    margin: 0 auto;
+    max-width: 430px;
+}
+#col-showcase {
+    margin: 0 auto;
+    max-width: 1100px;
+}
+#button {
+    color: blue;
+}
+"""
+
+def load_description(fp):
+    with open(fp, 'r', encoding='utf-8') as f:
+        content = f.read()
+    return content
+
+with gr.Blocks(css=css) as Tryon:
+    gr.HTML(load_description("assets/title.md"))
+    with gr.Row():
+        with gr.Column(elem_id = "col-left"):
+            gr.HTML("""
+            <div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
+                <div>
+                Step 1.  Upload a person image ⬇️
+                </div>
+            </div>
+            """)
+        with gr.Column(elem_id = "col-mid"):
+            gr.HTML("""
+            <div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
+                <div>
+                Step 2. Enter a garment description ⬇️
+                </div>
+            </div>
+            """)
+        with gr.Column(elem_id = "col-right"):
+            gr.HTML("""
+            <div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
+                <div>
+                Step 3. Press "Run" to get try-on results
+                </div>
+            </div>
+            """)
+    with gr.Row():
+        with gr.Column(elem_id = "col-left"):
+            imgs = gr.Image(label="Person image", sources='upload', type="numpy")
+            example = gr.Examples(
+                inputs=imgs,
+                examples_per_page=12,
+                examples=human_list_path
+            )
+        with gr.Column(elem_id = "col-mid"):
+            garm_prompt = gr.Textbox(label="Garment description", placeholder="Enter a description of the garment...")
+            example_prompts = gr.Examples(
+                inputs=garm_prompt,
+                examples=["A red t-shirt", "Blue jeans", "A floral summer dress", "A black leather jacket"]
+            )
+        with gr.Column(elem_id = "col-right"):
+            image_out = gr.Image(label="Result", show_share_button=False)
+            with gr.Row():
+                seed = gr.Slider(
+                    label="Seed",
+                    minimum=0,
+                    maximum=MAX_SEED,
+                    step=1,
+                    value=0,
+                )
+                randomize_seed = gr.Checkbox(label="Random seed", value=True)
+            with gr.Row():
+                seed_used = gr.Number(label="Seed used")
+                result_info = gr.Text(label="Response")
+            test_button = gr.Button(value="Run", elem_id="button")
+
+    test_button.click(fn=tryon, inputs=[imgs, garm_prompt, seed, randomize_seed], outputs=[image_out, seed_used, result_info], api_name='tryon', concurrency_limit=40)
+
+    with gr.Column(elem_id = "col-showcase"):
+        gr.HTML("""
+        <div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
+            <div> </div>
+            <br>
+            <div>
+            Virtual try-on examples in pairs of person images and garment descriptions
+            </div>
+        </div>
+        """)
+        show_case = gr.Examples(
+            examples=[
+                ["assets/examples/model2.png", "A blue t-shirt", "assets/examples/result2.png"],
+                ["assets/examples/model3.png", "A red dress", "assets/examples/result3.png"],
+                ["assets/examples/model1.png", "A black suit", "assets/examples/result1.png"],
+            ],
+            inputs=[imgs, garm_prompt, image_out],
+            label=None
+        )
+
+Tryon.launch()