Create app.py

app.py

@@ -0,0 +1,64 @@
+# app.py
+
+import cv2
+import numpy as np
+import tensorflow as tf
+import gradio as gr
+
+# 1. Load your trained model
+model = tf.keras.models.load_model("best_model.h5")
+
+# 2. Denoising pipeline function
+def process_and_denoise(image):
+    """
+    Input: HxW grayscale or color image (numpy array).
+    Outputs: (orig, noisy, denoised) all as 64×64 uint8 arrays.
+    """
+    # --- Preprocess Original ---
+    # If color, convert to gray:
+    if image.ndim == 3 and image.shape[2] == 3:
+        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    else:
+        # could be (H,W,1) or (H,W)
+        gray = image[...,0] if image.ndim == 3 else image
+
+    # Resize to 64×64
+    orig = cv2.resize(gray, (64,64))
+    orig_norm = orig.astype(np.float32) / 255.0
+
+    # --- Add Gaussian Noise ---
+    sigma = 0.1  # same noise level you trained with
+    noisy = orig_norm + sigma * np.random.randn(*orig_norm.shape)
+    noisy = np.clip(noisy, 0.0, 1.0)
+
+    # --- Denoise with the autoencoder ---
+    inp = noisy[np.newaxis, ..., np.newaxis]   # shape (1,64,64,1)
+    pred = model.predict(inp)[0, ..., 0]       # shape (64,64)
+
+    # --- Convert all to uint8 for display ---
+    orig_disp   = (orig_norm * 255).astype(np.uint8)
+    noisy_disp  = (noisy    * 255).astype(np.uint8)
+    recon_disp  = (pred     * 255).astype(np.uint8)
+
+    return orig_disp, noisy_disp, recon_disp
+
+# 3. Build Gradio interface with 3 outputs
+demo = gr.Interface(
+    fn=process_and_denoise,
+    inputs=gr.Image(type="numpy", label="Input Image"),
+    outputs=[
+        gr.Image(type="numpy", label="Original (64×64)"),
+        gr.Image(type="numpy", label="Noisy (σ=0.1)"),
+        gr.Image(type="numpy", label="Reconstructed")
+    ],
+    title="Denoising Autoencoder Demo",
+    description=(
+        "Upload any image (grayscale or color). "
+        "This app converts it to 64×64 grayscale, adds Gaussian noise, "
+        "then denoises it with the trained autoencoder. "
+        "See all three side by side!"
+    )
+)
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", share=True)