Upload app.py

app.py

@@ -1,15 +1,29 @@
-import gradio as gr
+import os
+import requests
 import tensorflow as tf
+import gradio as gr
 import numpy as np
 
-# Load the model
-model = tf.keras.models.load_model("my_mnist_model.keras")
+MODEL_URL = "https://drive.google.com/uc?export=download&id=1ECjloRVUkgnKACeBZA06UU_-JddZQ5Z5"
+MODEL_PATH = "my_mnist_model.keras"
+
+def download_file_from_google_drive(url, destination):
+    if not os.path.exists(destination):
+        print("Downloading model from Google Drive...")
+        response = requests.get(url, stream=True)
+        with open(destination, "wb") as f:
+            for chunk in response.iter_content(chunk_size=8192):
+                if chunk:
+                    f.write(chunk)
+        print("Download complete.")
+
+download_file_from_google_drive(MODEL_URL, MODEL_PATH)
+
+model = tf.keras.models.load_model(MODEL_PATH)
 
 def predict_digit(image):
-    # Convert to grayscale if needed
     if image.ndim == 3:
         image = image[..., 0]
-    # Resize and preprocess
     image = np.array(image).astype("float32")
     image = image.reshape(1, 28, 28)
     image = image / 255.0
@@ -18,7 +32,7 @@ def predict_digit(image):
 
 iface = gr.Interface(
     fn=predict_digit,
-    inputs=gr.Image(shape=(28, 28), image_mode='L', source="canvas", tool="editor"),
+    inputs=gr.Image(image_mode='L', source="canvas", tool="editor", height=28, width=28),
     outputs=gr.Label(num_top_classes=1),
     title="MNIST Digit Classifier",
     description="Draw a digit (0-9) and the model will predict it."