app.py

import gradio as gr
from dotenv import load_dotenv
import requests
from flask import Flask, jsonify, request, send_file
from botocore.exceptions import ClientError
from botocore.client import Config
import boto3
from urllib.parse import urlparse
import os
from PIL import Image
from io import BytesIO
import uuid


load_dotenv()

AWS_ACCESS_KEY_ID = os.getenv("AWS_ACCESS_KEY_ID")
AWS_SECRET_ACCESS_KEY = os.getenv("AWS_SECRET_ACCESS_KEY")
BUCKET_NAME = "tech-tailor"
s3_client = boto3.client(
    "s3",
    region_name='ap-south-1',
    aws_access_key_id=AWS_ACCESS_KEY_ID, 
    aws_secret_access_key=AWS_SECRET_ACCESS_KEY,
    config=Config(signature_version='s3v4')
)
MODAL_INFERENCE_ENDPOINT_URL = os.getenv("MODAL_INFERENCE_ENDPOINT_URL")
app = Flask(__name__)

GARM_SAVE_DIR = "garment_images"
MODE_SAVE_DIR = "model_images"

garment_upload_dir = "gradio_demo_garment/"
model_upload_dir = "gradio_demo_model/"

def load_image_from_url(image_url):
    try:
        response = requests.get(image_url)
        if "image" in response.headers["Content-Type"]:
            img = Image.open(BytesIO(response.content))
            return img
        else:
            return None
    except Exception as e:
        print(f"Error loading image: {e}")
        return None
    
def process_cloth_image(image_url):
    if image_url:
        try:
            response = requests.get(image_url)
            response.raise_for_status()
            img = Image.open(BytesIO(response.content))
            img = img.convert("RGB")
            img_width, img_height = img.size
            target_width = 768
            target_height = 1024
            scale_width = target_width / img_width
            scale_height = target_height / img_height
            scale_factor = min(scale_width, scale_height)
            new_width = int(img_width * scale_factor)
            new_height = int(img_height * scale_factor)
            img = img.resize((new_width, new_height), Image.Resampling.LANCZOS)
            new_img = Image.new("RGB", (target_width, target_height), (0, 0, 0))
            left_padding = (target_width - new_width) // 2
            top_padding = (target_height - new_height) // 2
            new_img.paste(img, (left_padding, top_padding))
            img_byte_array = BytesIO()
            new_img.save(img_byte_array, format="JPEG")
            img_byte_array.seek(0) 
            filename = f"{uuid.uuid4().hex}.jpg"
            s3_client.put_object(Body = img_byte_array, Bucket = BUCKET_NAME, Key = garment_upload_dir + filename, ContentType= 'image/jpeg')
            garment_url = s3_client.generate_presigned_url(
                'get_object', 
                Params={'Bucket': BUCKET_NAME, 'Key': garment_upload_dir + filename}, 
                ExpiresIn=3600
            )
            return garment_url
        
        except requests.exceptions.RequestException as e:
            return f"Error downloading image: {e}"
        except Exception as e:
            return f"Error processing image: {e}"
    else:
        return "No image provided"

def process_model_image(image):
    img = image.convert("RGB")
    img_width, img_height = img.size
    target_width = 768
    target_height = 1024
    scale_width = target_width / img_width
    scale_height = target_height / img_height
    scale_factor = min(scale_width, scale_height)
    new_width = int(img_width * scale_factor)
    new_height = int(img_height * scale_factor)
    img = img.resize((new_width, new_height), Image.Resampling.LANCZOS)
    new_img = Image.new("RGB", (target_width, target_height), (0, 0, 0))
    left_padding = (target_width - new_width) // 2
    top_padding = (target_height - new_height) // 2
    new_img.paste(img, (left_padding, top_padding))
    img_byte_array = BytesIO()
    new_img.save(img_byte_array, format="JPEG")
    img_byte_array.seek(0) 
    filename = f"{uuid.uuid4().hex}.jpg"
    s3_client.put_object(Body = img_byte_array, Bucket = BUCKET_NAME, Key = model_upload_dir + filename, ContentType = 'image/jpeg')
    model_url = s3_client.generate_presigned_url(
        'get_object', 
        Params={'Bucket': BUCKET_NAME, 'Key': model_upload_dir + filename}, 
        ExpiresIn=3600
    )
    return model_url

    
def display_image(image, image_url):
    garment_file_path = process_cloth_image(image_url)
    model_file_path = process_model_image(image)
    print(garment_file_path, model_file_path)
    payload = {
        "human_image_url": model_file_path,
        "garment_image_url": garment_file_path
    }
    print(payload)
    results = []
    try:
        print("Entering Modal block")
        response = requests.post(MODAL_INFERENCE_ENDPOINT_URL, json=payload)
        if response.status_code == 200:
            result_data = response.json()
            url = result_data["url"]
            response = requests.get(url)
            img = Image.open(BytesIO(response.content))
            img_resized = img.resize((512, 682))
            return img_resized
        else:
            results.append({"error": f"Failed to process the garment image. Status Code: {response.status_code}"})
    except requests.exceptions.RequestException as e:
        results.append({"error": f"Request failed for the garment image. Error: {str(e)}"})
    return ""        

def generate_presigned_url(object_url):
    parsed_url = urlparse(object_url)
    path_parts = parsed_url.path.lstrip('/').split('/', 1)
    object_key = path_parts[1] if len(path_parts) > 1 else ''
    print(f"Extracted Object Key: {object_key}")
    try:
        presigned_url = s3_client.generate_presigned_url(
            'get_object',
            Params={
                'Bucket': BUCKET_NAME,
                'Key': object_key
            },
            ExpiresIn=3600
        )
        return presigned_url
    except Exception as e:
        print(f"Error generating pre-signed URL: {e}")
        return None

# Gradio app with improved layout
with gr.Blocks() as demo:
    gr.Markdown("## Virtual Try-On")

    # Fixed layout for desktop, responsive only for mobile
    with gr.Row(elem_id="responsive-container"):
        image_url_input = gr.Textbox(
            label="Image URL",
            placeholder="Enter image URL here",
            interactive=True,
            elem_id="url-input"
        )
        input_garment_image = gr.Image(
            label="Garment Image",
            type="pil",
            interactive=True,
            elem_id="garment-image"
        )
        uploaded_image = gr.Image(
            label="Upload or Capture Image",
            type="pil",
            interactive=True,
            elem_id="uploaded-image"
        )
        output_display = gr.Image(
            label="Displayed Image or URL Result",
            elem_id="output-image"
        )

    image_url_input.change(
        load_image_from_url,  # Function to validate and load the image
        inputs=image_url_input,
        outputs=input_garment_image
    )

    submit_btn = gr.Button("Submit", elem_id="submit-btn")
    submit_btn.click(
        display_image, 
        inputs=[uploaded_image, image_url_input], 
        outputs=output_display
    )


# Custom CSS for mobile responsiveness
custom_css = """
/* Default layout for PC screens */
#responsive-container {
    display: flex;
    flex-direction: row;
    gap: 20px;
    justify-content: center;
    align-items: center;
}
#responsive-container .gr-box {
    flex: 1;
    max-width: 384px;
}

/* Responsive layout for mobile screens */
@media (max-width: 768px) {
    #responsive-container {
        flex-wrap: wrap;
        flex-direction: column;
    }
    #responsive-container .gr-box {
        flex: 1 1 100%;
        max-width: 100%;
    }
}
"""

demo.launch(share=True)