Update app.py

app.py

@@ -1,47 +1,42 @@
 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
 
-
 def tryon(person_img, garment_prompt, seed, randomize_seed):
     post_start_time = time.time()
+    
     if person_img is None or garment_prompt.strip() == "":
         return None, None, "Empty image or prompt"
+    
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
         
-    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')
+    # Create a copy of the person image to overlay text
+    result_img = person_img.copy()
     
-    # Simulate generation of garment image from text prompt
-    generated_garment_img = np.zeros((person_img.shape[0], person_img.shape[1], 3), dtype=np.uint8)  # Dummy garment image (black)
+    # Convert the image to OpenCV format (if needed)
+    if len(result_img.shape) == 2:  # Convert grayscale to RGB
+        result_img = cv2.cvtColor(result_img, cv2.COLOR_GRAY2RGB)
     
-    # Encode the generated garment image
-    encoded_garment_img = cv2.imencode('.jpg', generated_garment_img)[1].tobytes()
-    encoded_garment_img = base64.b64encode(encoded_garment_img).decode('utf-8')
+    # Set text position and properties
+    text_position = (10, 30)
+    font = cv2.FONT_HERSHEY_SIMPLEX
+    font_scale = 1
+    font_color = (0, 255, 0)  # Green color for the text
+    thickness = 2
 
-    # Simulate request processing (no external API calls)
-    time.sleep(1)
-    
-    # Decoding process (dummy)
-    result_img = cv2.imdecode(np.frombuffer(base64.b64decode(encoded_garment_img), np.uint8), cv2.IMREAD_UNCHANGED)
-    result_img = cv2.cvtColor(result_img, cv2.COLOR_RGB2BGR)
+    # Overlay the garment description text on the image
+    cv2.putText(result_img, f'Garment: {garment_prompt}', text_position, font, font_scale, font_color, thickness, cv2.LINE_AA)
 
     post_end_time = time.time()
     print(f"post time used: {post_end_time - post_start_time}")
 
-    # Return the simulated result image, used seed, and success message
+    # Return the resulting image, used seed, and success message
     return result_img, seed, "Success"
 
-
 MAX_SEED = 999999
 
 example_path = os.path.join(os.path.dirname(__file__), 'assets')