Add app.py with basic Gradio interface for image upscaling

Initial commit of AI Image Upscaler using Gradio. This app allows users to upload an image and receive a 2x 4x 6x 8x upscaled version. Built with PIL and Gradio.

app.py

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+import gradio as gr
+import os
+import uuid
+import tempfile
+import zipfile
+import numpy as np
+from PIL import Image
+import onnxruntime as ort
+
+MODEL_PATHS = {
+    "Real-ESRGAN": "models/Real-ESRGAN_4x.onnx",
+    "Real-ESRGAN Anime6B": "models/Real-ESRGAN_Anime6B_4x.onnx"
+}
+
+def load_model(model_name):
+    return ort.InferenceSession(MODEL_PATHS[model_name], providers=["CPUExecutionProvider"])
+
+def upscale_single_image(img, session):
+    image = Image.open(img).convert("RGB")
+    img_array = np.array(image).astype(np.float32) / 255.0
+    img_array = img_array.transpose(2, 0, 1)[None, :, :, :]
+
+    output = session.run(None, {"input": img_array})[0]
+    output = np.clip(output.squeeze().transpose(1, 2, 0), 0, 1)
+    upscaled = Image.fromarray((output * 255).astype(np.uint8))
+
+    original_name = os.path.basename(img.name)
+    name, ext = os.path.splitext(original_name)
+    output_name = f"{name}_upscaled{ext}"
+
+    temp_path = os.path.join(tempfile.gettempdir(), output_name)
+    upscaled.save(temp_path)
+    return output_name, temp_path
+
+def upscale_batch(images, resolution, model_name):
+    session = load_model(model_name)
+    results = []
+    for img in images:
+        name, path = upscale_single_image(img, session)
+        results.append((name, path))
+    return results
+
+def prepare_display(images_info):
+    return [(name, path) for name, path in images_info]
+
+def generate_zip(images_info):
+    zip_name = f"upscaled_batch_{uuid.uuid4().hex[:8]}.zip"
+    zip_path = os.path.join(tempfile.gettempdir(), zip_name)
+    with zipfile.ZipFile(zip_path, "w") as zipf:
+        for name, path in images_info:
+            zipf.write(path, arcname=name)
+    return zip_path
+
+with gr.Blocks(title="Ultra Pixel Image Upscaling") as app:
+    gr.Markdown("## Ultra Pixel Image Upscaling")
+    gr.Markdown("Perfect AI upscaling with zero distortion — bigger, better, sharper. Supports up to 16K resolution.")
+
+    with gr.Row():
+        file_input = gr.File(file_types=["image"], file_count="multiple", label="Upload Images")
+        resolution = gr.Dropdown(["2K", "4K", "6K", "8K", "16K"], value="4K", label="Choose Resolution")
+        model_choice = gr.Dropdown(["Real-ESRGAN", "Real-ESRGAN Anime6B"], value="Real-ESRGAN", label="Model")
+
+    upscale_btn = gr.Button("Start Upscaling")
+    output_gallery = gr.Gallery(label="Upscaled Images").style(grid=4)
+    zip_btn = gr.Button("Download All as ZIP")
+    zip_output = gr.File(label="ZIP File")
+
+    session_data = gr.State([])
+
+    def process_and_store(files, resolution, model_choice):
+        results = upscale_batch(files, resolution, model_choice)
+        return prepare_display(results), results
+
+    def return_zip(saved_state):
+        return generate_zip(saved_state)
+
+    upscale_btn.click(fn=process_and_store, inputs=[file_input, resolution, model_choice],
+                      outputs=[output_gallery, session_data])
+    zip_btn.click(fn=return_zip, inputs=[session_data], outputs=zip_output)
+
+app.launch()