Upload a virtual try on

.gitignore

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+# Ignore user uploads and generated output
+static/uploads/*
+static/outputs/*
+
+# But keep the directories themselves
+!static/uploads/.gitkeep
+!static/outputs/.gitkeep

Dockerfile

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+FROM python:3.10.12-slim
+
+WORKDIR /app
+
+# Set cache directories
+ENV HF_HOME=/app/.cache
+ENV MPLCONFIGDIR=/app/.cache
+
+# Install dependencies
+RUN apt-get update && apt-get install -y libgl1-mesa-glx libglib2.0-0 && apt-get clean
+
+# Create cache directory
+RUN mkdir -p /app/.cache && chmod -R 777 /app/.cache
+
+# Install Python dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Pre-download model
+RUN python -c "from transformers import SamModel, SamProcessor; \
+    SamModel.from_pretrained('Zigeng/SlimSAM-uniform-50', cache_dir='/app/.cache'); \
+    SamProcessor.from_pretrained('Zigeng/SlimSAM-uniform-50', cache_dir='/app/.cache')"
+
+# Copy app code
+COPY . .
+
+# Set port
+ENV PORT=7860
+
+# Run with Gunicorn
+CMD ["gunicorn", "app:app", "--bind", "0.0.0.0:7860", "--workers", "2"]

app.py

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+from flask import Flask, render_template, request, send_from_directory
+from PIL import Image
+import os, torch, cv2, mediapipe as mp
+from transformers import SamModel, SamProcessor, logging as hf_logging
+from torchvision import transforms
+from diffusers.utils import load_image
+from flask_cors import CORS 
+
+app= Flask(__name__)
+CORS(app)
+
+# Enable Hugging Face detailed logs (shows model download progress)
+hf_logging.set_verbosity_info()
+
+
+UPLOAD_FOLDER = '/tmp/uploads'
+OUTPUT_FOLDER = '/tmp/outputs'
+
+if not os.path.exists(UPLOAD_FOLDER):
+    print(f"[WARN] {UPLOAD_FOLDER} does not exist. Creating...")
+    os.makedirs(UPLOAD_FOLDER, exist_ok=True)
+
+if not os.path.exists(OUTPUT_FOLDER):
+    print(f"[WARN] {OUTPUT_FOLDER} does not exist. Creating...")
+    os.makedirs(OUTPUT_FOLDER, exist_ok=True)
+
+
+# Lazy-load model
+model, processor = None, None
+
+def load_model():
+    global model, processor
+    if model is None or processor is None:
+        print("[INFO] Loading SAM model and processor...")
+        model = SamModel.from_pretrained("Zigeng/SlimSAM-uniform-50", cache_dir="/app/.cache")
+        processor = SamProcessor.from_pretrained("Zigeng/SlimSAM-uniform-50", cache_dir="/app/.cache")
+        print("[INFO] Model and processor loaded successfully!")
+
+@app.before_request
+def log_request_info():
+    print(f"[INFO] Incoming request: {request.method} {request.path}")
+
+@app.route('/health')
+def health():
+    return "OK", 200
+
+# Route to serve outputs dynamically
+@app.route('/outputs/<filename>')
+def serve_output(filename):
+    return send_from_directory(OUTPUT_FOLDER, filename)
+
+@app.route('/', methods=['GET', 'POST'])
+def index():
+    print(f"[INFO] Handling {request.method} on /")
+    if request.method == 'POST':
+        try:
+            load_model()
+
+            # Save uploaded images
+            person_file = request.files['person_image']
+            tshirt_file = request.files['tshirt_image']
+            person_path = os.path.join(UPLOAD_FOLDER, 'person.jpg')
+            tshirt_path = os.path.join(UPLOAD_FOLDER, 'tshirt.png')
+            person_file.save(person_path)
+            tshirt_file.save(tshirt_path)
+            print(f"[INFO] Saved files to {UPLOAD_FOLDER}")
+
+            # Pose detection
+            mp_pose = mp.solutions.pose
+            pose = mp_pose.Pose()
+            image = cv2.imread(person_path)
+            if image is None:
+                return "No image detected."
+            image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+            results = pose.process(image_rgb)
+            if not results.pose_landmarks:
+                return "No pose detected."
+            height, width, _ = image.shape
+            landmarks = results.pose_landmarks.landmark
+            left_shoulder = (int(landmarks[11].x * width), int(landmarks[11].y * height))
+            right_shoulder = (int(landmarks[12].x * width), int(landmarks[12].y * height))
+            print(f"[INFO] Shoulder coordinates: {left_shoulder}, {right_shoulder}")
+
+            # SAM model inference
+            img = load_image(person_path)
+            new_tshirt = load_image(tshirt_path)
+            input_points = [[[left_shoulder[0], left_shoulder[1]], [right_shoulder[0], right_shoulder[1]]]]
+            inputs = processor(img, input_points=input_points, return_tensors="pt")
+            outputs = model(**inputs)
+            masks = processor.image_processor.post_process_masks(
+                outputs.pred_masks.cpu(),
+                inputs["original_sizes"].cpu(),
+                inputs["reshaped_input_sizes"].cpu()
+            )
+            mask_tensor = masks[0][0][2].to(dtype=torch.uint8)
+            mask = transforms.ToPILImage()(mask_tensor * 255)
+
+            # Combine images
+            new_tshirt = new_tshirt.resize(img.size, Image.LANCZOS)
+            img_with_new_tshirt = Image.composite(new_tshirt, img, mask)
+            result_path = os.path.join(OUTPUT_FOLDER, 'result.jpg')
+            img_with_new_tshirt.save(result_path)
+            print(f"[INFO] Result saved to {result_path}")
+
+            # Serve via dynamic route
+            return render_template('index.html', result_img='/outputs/result.jpg')
+
+        except Exception as e:
+            print(f"[ERROR] {e}")
+            return f"Error: {e}"
+
+    return render_template('index.html')
+
+if __name__ == '__main__':
+        
+    print("[INFO] Starting Flask server...")
+    app.run(debug=true, host='0.0.0.0')

requirements.txt

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+Flask
+gunicorn
+Pillow
+opencv-python
+torch
+torchvision
+mediapipe
+transformers
+diffusers
+safetensors
+flask-cors

runtime.txt

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+python-3.10.12

templates/index.html

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+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+    <meta charset="UTF-8">
+    <title>Virtual Fashion Try-On</title>
+    <script src="https://cdn.tailwindcss.com"></script>
+</head>
+
+<body class="bg-gray-900 text-white min-h-screen flex flex-col items-center py-10">
+
+    <h1 class="text-4xl font-bold text-blue-400 mb-10">Virtual Fashion Try-On</h1>
+
+    <form action="/" method="post" enctype="multipart/form-data" class="w-full max-w-4xl bg-gray-800 rounded-2xl shadow-lg p-8 space-y-6">
+
+        <div class="grid grid-cols-1 md:grid-cols-2 gap-8">
+            <!-- Person Image Upload -->
+            <div>
+                <h2 class="text-lg font-semibold mb-2">Upload your photo</h2>
+                <label for="person_image" class="flex flex-col items-center justify-center border-2 border-dashed border-gray-600 rounded-xl p-6 hover:bg-gray-700 cursor-pointer relative">
+                    <svg xmlns="http://www.w3.org/2000/svg" class="h-10 w-10 text-gray-400 mb-2" fill="none" viewBox="0 0 24 24" stroke="currentColor">
+                        <path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M7 16v4m0 0h10m-10 0v-4m0 0h10m-10 0V5m0 0h10m-10 0H5m14 0h-2" />
+                    </svg>
+                    <p class="text-gray-400">Drag & drop or click to upload</p>
+                    <input id="person_image" type="file" name="person_image" class="hidden" required onchange="showFileName('person_image', 'person_filename')">
+                </label>
+                <p id="person_filename" class="text-green-400 text-sm mt-2 text-center"></p>
+            </div>
+
+            <!-- T-Shirt Image Upload -->
+            <div>
+                <h2 class="text-lg font-semibold mb-2">Upload garment image</h2>
+                <label for="tshirt_image" class="flex flex-col items-center justify-center border-2 border-dashed border-gray-600 rounded-xl p-6 hover:bg-gray-700 cursor-pointer relative">
+                    <svg xmlns="http://www.w3.org/2000/svg" class="h-10 w-10 text-gray-400 mb-2" fill="none" viewBox="0 0 24 24" stroke="currentColor">
+                        <path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M7 16v4m0 0h10m-10 0v-4m0 0h10m-10 0V5m0 0h10m-10 0H5m14 0h-2" />
+                    </svg>
+                    <p class="text-gray-400">Drag & drop or click to upload</p>
+                    <input id="tshirt_image" type="file" name="tshirt_image" class="hidden" required onchange="showFileName('tshirt_image', 'tshirt_filename')">
+                </label>
+                <p id="tshirt_filename" class="text-green-400 text-sm mt-2 text-center"></p>
+            </div>
+        </div>
+
+        <!-- Submit Button -->
+        <div class="flex justify-center">
+            <button type="submit" class="bg-pink-500 hover:bg-pink-600 text-white font-semibold py-3 px-8 rounded-xl shadow-md transition">
+                🚀 Perform Virtual Try-On
+            </button>
+        </div>
+
+    </form>
+
+    {% if result_img %}
+    <div class="w-full max-w-4xl mt-10 bg-gray-800 rounded-2xl shadow-lg p-8">
+        <h2 class="text-2xl font-bold mb-6 text-center">🎉 Your Virtual Try-On Result</h2>
+        <div class="flex justify-center">
+            <img src="{{ result_img }}" alt="Result Image">
+        </div>
+    </div>
+    {% endif %}
+
+    <script>
+        function showFileName(inputId, filenameId) {
+            const input = document.getElementById(inputId);
+            const filename = document.getElementById(filenameId);
+            if (input.files.length > 0) {
+                filename.textContent = "✔️ " + input.files[0].name + " uploaded";
+            } else {
+                filename.textContent = "";
+            }
+        }
+    </script>
+
+</body>
+
+</html>