Spaces:
Sleeping
Sleeping
| # import cv2 | |
| # import gradio as gr | |
| # import numpy as np | |
| # import os | |
| # from PIL import Image | |
| # from try_on_diffusion_client import TryOnDiffusionClient # <‑ the file you just pulled | |
| # # --- 1. Initialise the API client ----------------------------------------- | |
| # API_URL = "https://try-on-diffusion.p.rapidapi.com" # RapidAPI endpoint | |
| # API_KEY = os.getenv("RAPIDAPI_KEY") # put your key in an env var | |
| # client = TryOnDiffusionClient(base_url=API_URL, api_key=API_KEY) | |
| # # --- 2. The Gradio callback ------------------------------------------------ | |
| # def try_on(user_img, outfit_img, height, chest, waist, sleeve): | |
| # # 1️⃣ Force 3‑channel RGB and uint8 dtype | |
| # user_rgb = user_img.convert("RGB") | |
| # outfit_rgb = outfit_img.convert("RGB") | |
| # avatar_bgr = cv2.cvtColor(np.array(user_rgb, dtype=np.uint8), cv2.COLOR_RGB2BGR) | |
| # clothing_bgr = cv2.cvtColor(np.array(outfit_rgb, dtype=np.uint8), cv2.COLOR_RGB2BGR) | |
| # # 2️⃣ Call the API (unchanged) | |
| # resp = client.try_on_file( | |
| # clothing_image = clothing_bgr, | |
| # avatar_image = avatar_bgr, | |
| # seed = -1 | |
| # ) | |
| # if resp.status_code != 200 or resp.image is None: | |
| # return None, f"API error {resp.status_code}: {resp.error_details}" | |
| # result_rgb = cv2.cvtColor(resp.image, cv2.COLOR_BGR2RGB) | |
| # # 3️⃣ Simple fit message | |
| # fit_msg = "✅ Fit looks OK" | |
| # if chest < 80: | |
| # fit_msg = "⚠️ Chest measurement suggests a tight fit." | |
| # return Image.fromarray(result_rgb), fit_msg | |
| # # --- 3. Create the Gradio Interface ------------------------------------------ | |
| # demo = gr.Interface( | |
| # fn=try_on, | |
| # inputs=[ | |
| # gr.Image(type="pil", label="Upload your image"), | |
| # gr.Image(type="pil", label="Upload outfit image"), | |
| # gr.Number(label="Height (cm)"), | |
| # gr.Number(label="Chest (cm)"), | |
| # gr.Number(label="Waist (cm)"), | |
| # gr.Number(label="Sleeve length (cm)") | |
| # ], | |
| # outputs=[ | |
| # gr.Image(type="pil", label="Try-On Result"), | |
| # gr.Text(label="Fit Advice") | |
| # ], | |
| # title="👕 Virtual Try-On", | |
| # description="Upload your image and an outfit to see how it might look on you!" | |
| # ) | |
| # # --- 4. Launch the app ------------------------------------------------------- | |
| # if __name__ == "__main__": | |
| # print("Launching Virtual Try-On App...") | |
| # demo.launch() | |
| import cv2 | |
| import gradio as gr | |
| import numpy as np | |
| import os | |
| from PIL import Image | |
| from try_on_diffusion_client import TryOnDiffusionClient | |
| # --- 1. Initialize the API client ----------------------------------------- | |
| API_URL = "https://try-on-diffusion.p.rapidapi.com" | |
| API_KEY = os.getenv("RAPIDAPI_KEY") # This will be set as a secret in HF Spaces | |
| if not API_KEY: | |
| print("⚠️ RAPIDAPI_KEY not found. Please set it in Hugging Face Spaces secrets.") | |
| client = TryOnDiffusionClient(base_url=API_URL, api_key=API_KEY) | |
| # --- 2. The Gradio callback ------------------------------------------------ | |
| def try_on(user_img, outfit_img, height, chest, waist, sleeve): | |
| """ | |
| Process the virtual try-on request | |
| """ | |
| # Check if API key is available | |
| if not API_KEY: | |
| return None, "❌ API key not configured. Please contact the app administrator." | |
| # Validate inputs | |
| if user_img is None or outfit_img is None: | |
| return None, "❌ Please upload both user image and outfit image." | |
| try: | |
| # Convert images to RGB and ensure uint8 dtype | |
| user_rgb = user_img.convert("RGB") | |
| outfit_rgb = outfit_img.convert("RGB") | |
| avatar_bgr = cv2.cvtColor(np.array(user_rgb, dtype=np.uint8), cv2.COLOR_RGB2BGR) | |
| clothing_bgr = cv2.cvtColor(np.array(outfit_rgb, dtype=np.uint8), cv2.COLOR_RGB2BGR) | |
| # Call the API | |
| resp = client.try_on_file( | |
| clothing_image=clothing_bgr, | |
| avatar_image=avatar_bgr, | |
| seed=-1 | |
| ) | |
| if resp.status_code != 200 or resp.image is None: | |
| error_msg = resp.error_details if resp.error_details else f"API error {resp.status_code}" | |
| return None, f"❌ {error_msg}" | |
| # Convert result back to RGB | |
| result_rgb = cv2.cvtColor(resp.image, cv2.COLOR_BGR2RGB) | |
| # Generate fit advice | |
| fit_msg = generate_fit_advice(height, chest, waist, sleeve) | |
| return Image.fromarray(result_rgb), fit_msg | |
| except Exception as e: | |
| return None, f"❌ Error processing request: {str(e)}" | |
| def generate_fit_advice(height, chest, waist, sleeve): | |
| """ | |
| Generate basic fit advice based on measurements | |
| """ | |
| advice = ["✅ Fit Analysis:"] | |
| if chest and chest < 80: | |
| advice.append("⚠️ Chest measurement suggests the item might be tight.") | |
| elif chest and chest > 120: | |
| advice.append("ℹ️ Chest measurement suggests a loose fit.") | |
| else: | |
| advice.append("✅ Chest measurement looks good.") | |
| if waist and chest and abs(chest - waist) < 10: | |
| advice.append("ℹ️ Similar chest and waist measurements - consider fitted styles.") | |
| if height and height < 160: | |
| advice.append("ℹ️ For shorter stature, consider checking garment length.") | |
| elif height and height > 185: | |
| advice.append("ℹ️ For taller stature, ensure adequate garment length.") | |
| return "\n".join(advice) | |
| # --- 3. Create the Gradio Interface ------------------------------------------ | |
| with gr.Blocks(theme=gr.themes.Soft(), title="Virtual Try-On") as demo: | |
| gr.Markdown( | |
| """ | |
| # 👕 Virtual Try-On App | |
| Upload your photo and a clothing item to see how it would look on you! | |
| **Tips for best results:** | |
| - Use clear, well-lit photos | |
| - Person should be facing forward | |
| - Clothing item should be clearly visible | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| user_img = gr.Image(type="pil", label="Your Photo", height=300) | |
| outfit_img = gr.Image(type="pil", label="Clothing Item", height=300) | |
| with gr.Column(): | |
| gr.Markdown("### 📏 Your Measurements (Optional)") | |
| height = gr.Number(label="Height (cm)", value=170, minimum=140, maximum=220) | |
| chest = gr.Number(label="Chest (cm)", value=90, minimum=70, maximum=140) | |
| waist = gr.Number(label="Waist (cm)", value=80, minimum=60, maximum=130) | |
| sleeve = gr.Number(label="Sleeve Length (cm)", value=60, minimum=50, maximum=80) | |
| submit_btn = gr.Button("✨ Try On Outfit", variant="primary", size="lg") | |
| with gr.Row(): | |
| result_img = gr.Image(type="pil", label="Try-On Result", height=400) | |
| fit_advice = gr.Textbox(label="Fit Advice", lines=6, max_lines=10) | |
| # Examples section | |
| gr.Markdown("### 📸 Example Images") | |
| gr.Markdown("*Upload your own images or try with sample photos*") | |
| submit_btn.click( | |
| fn=try_on, | |
| inputs=[user_img, outfit_img, height, chest, waist, sleeve], | |
| outputs=[result_img, fit_advice] | |
| ) | |
| # --- 4. Launch the app ------------------------------------------------------- | |
| if __name__ == "__main__": | |
| print("🚀 Launching Virtual Try-On App...") | |
| demo.launch() |