main working commit

.gitattributes

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.gitignore

@@ -0,0 +1,2 @@
+.env
+venv

app.py

@@ -1,7 +1,260 @@
 import gradio as gr
+import json
+import base64
+import requests
+import time
+import os
+from dotenv import load_dotenv
+import numpy as np
+from PIL import Image
+import io
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Load API key from .env file
+load_dotenv()
+API_KEY = os.getenv('API_KEY')
+CURRENT_URL = os.getenv('CURRENT_URL')
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+# API endpoints
+TRYON_URL = CURRENT_URL + 'api/tryon/'
+FETCH_URL = CURRENT_URL + 'api/tryon_state/'
+
+# Headers for API requests
+headers = {
+    'Authorization': 'Bearer ' + API_KEY,
+    'Content-Type': 'application/json',
+}
+
+# Create example directories if they don't exist
+os.makedirs("examples/garments", exist_ok=True)
+os.makedirs("examples/persons", exist_ok=True)
+
+# Paths to example images (you'll need to add these files)
+sample_garments = [
+    "samples/garments/g1.jpg",
+    "samples/garments/g2.jpg",
+]
+
+sample_humans = [
+    "samples/humans/h1.jpg",
+    "samples/humans/h2.jpg",
+    
+]
+
+def preprocess_image(img, target_size=None):
+    """Preprocess image without resizing if target_size is None"""
+    if img is None:
+        return None
+    
+    # Convert numpy array to PIL Image if it's a numpy array
+    if isinstance(img, np.ndarray):
+        img = Image.fromarray(img.astype('uint8'))
+    
+    # Only resize if target_size is specified
+    if target_size is not None:
+        img = img.resize(target_size, Image.LANCZOS)
+    
+    return img
+
+def virtual_tryon(garment_img, person_img):
+    # Convert images to base64
+    if person_img is None or garment_img is None:
+        return None
+    
+    # Preprocess images without resizing
+    human_pil = preprocess_image(person_img)
+    garment_pil = preprocess_image(garment_img)
+    
+    human_buffer = io.BytesIO()
+    garment_buffer = io.BytesIO()
+    
+    human_pil.save(human_buffer, format="JPEG")
+    garment_pil.save(garment_buffer, format="JPEG")
+    
+    human_base64_image = base64.b64encode(human_buffer.getvalue()).decode('utf-8')
+    garment_base64_image = base64.b64encode(garment_buffer.getvalue()).decode('utf-8')
+    
+    # Prepare data for API request
+    data = {
+        'human_image_base64': human_base64_image,  
+        'garment_image_base64': garment_base64_image,
+    }
+    
+    # Make API request to start tryon process
+    response = requests.post(TRYON_URL, headers=headers, data=json.dumps(data))
+    
+    if response.status_code != 200:
+        return None
+    
+    json_response = response.json()
+    tryon_pk = json_response['tryon_pk']
+    
+    # Poll for result
+    time_elapsed = 0
+    while time_elapsed < 60:  # Timeout after 60 seconds
+        fetch_response = requests.post(FETCH_URL, headers=headers, data=json.dumps({
+            'tryon_pk': tryon_pk,
+        }))
+        
+        if fetch_response.status_code != 200:
+            return None
+            
+        json_response = fetch_response.json()
+        
+        if json_response.get('message') != 'success':
+            return None
+            
+        if json_response.get('status') == 'done':
+            # Download the result image
+            result_url = json_response['s3_url']
+            img_response = requests.get(result_url)
+            if img_response.status_code == 200:
+                return Image.open(io.BytesIO(img_response.content))
+        
+        time.sleep(2)
+        time_elapsed += 2
+    
+    return None
+
+custom_css = """
+body, .gradio-container {
+    font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif;
+    background-color: #121212;
+    color: white;
+}
+
+h1, h2, h3 {
+    color: white !important;
+}
+
+.container {
+    max-width: 1200px;
+    margin: 0 auto;
+}
+
+.image-container img {
+    object-fit: contain;
+    max-height: 450px;
+    width: auto;
+    margin: 0 auto;
+    display: block;
+    border-radius: 8px;
+}
+
+.examples-container {
+    display: grid;
+    grid-template-columns: repeat(auto-fill, minmax(150px, 1fr));
+    gap: 10px;
+    margin-top: 10px;
+}
+
+.examples-container img {
+    height: 120px;
+    object-fit: cover;
+    border-radius: 8px;
+    cursor: pointer;
+    transition: transform 0.2s;
+}
+
+.examples-container img:hover {
+    transform: scale(1.05);
+}
+
+button#try-on-button {
+    background-color: #FF6B00 !important;
+    color: white !important;
+    border: none !important;
+    padding: 12px 20px !important;
+    font-weight: 600 !important;
+    border-radius: 8px !important;
+    cursor: pointer !important;
+    transition: background-color 0.3s !important;
+}
+
+button#try-on-button:hover {
+    background-color: #FF8C33 !important;
+}
+
+footer {visibility: hidden}
+"""
+
+# Create Gradio interface
+with gr.Blocks(theme=gr.themes.Base(), css=custom_css) as demo:
+    gr.HTML("<h1 style='text-align: center; margin-bottom: 20px;'>AlphaBakeVirtual Try-On</h1>")
+
+    with gr.Row():
+        # First column - Garment
+        with gr.Column(scale=1):
+            gr.Markdown("### Garment Image")
+            garment_input = gr.Image(
+                label="Upload a garment image",
+                type="pil",
+                elem_id="garment-image",
+                elem_classes=["image-container"],
+                height=350
+            )
+            
+            # Add example garment images
+            gr.Examples(
+                examples=sample_garments,
+                inputs=garment_input,
+                label="Garment Examples",
+                examples_per_page=4
+            )
+            
+        # Second column - Person
+        with gr.Column(scale=1):
+            gr.Markdown("### Person Image")
+            person_input = gr.Image(
+                label="Upload a person image",
+                type="pil",
+                elem_id="person-image",
+                elem_classes=["image-container"],
+                height=350
+            )
+            
+            # Add example person images
+            gr.Examples(
+                examples=sample_humans,
+                inputs=person_input,
+                label="Person Examples",
+                examples_per_page=4
+            )
+        
+        # Third column - Garment options & result
+        with gr.Column(scale=1):
+            
+            # Try-on button
+            try_on_button = gr.Button("Try On", elem_id="try-on-button", variant="primary", size="lg")
+            
+            # Result image
+            output_image = gr.Image(
+                label="Result", 
+                type="pil",
+                elem_classes=["result-image"],
+                height=400
+            )
+    
+    
+    # Validation function
+    def validate_inputs(garment_img, person_img, garment_type, sleeve_length, garment_length):
+        if garment_img is None:
+            raise gr.Error("Please upload a garment image")
+        if person_img is None:
+            raise gr.Error("Please upload a person image")
+       
+        # If all validations pass, proceed with try-on
+        try:
+            result = virtual_tryon(garment_img, person_img)
+            return result
+        except Exception as e:
+            raise gr.Error(f"Error: {str(e)}")
+    
+    # Connect button to validation and try-on functions
+    try_on_button.click(
+        fn=validate_inputs,
+        inputs=[garment_input, person_input],
+        outputs=output_image
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+python-dotenv
+requests
+numpy
+pillow