Merge pull request #65 from truefoundry/md_fix_grdio_app

Updated the train mnist example to fix gradio deployment and updated truefoundry cli min version

Author: mitanshudodia

train-and-deploy-mnist/deploy_model.ipynb

@@ -29,7 +29,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install -U -q \"truefoundry>=0.4.1,<0.5.0\" \"tensorflow==2.15.0\" \"matplotlib==3.8.2\""
+    "%pip install -U -q \"truefoundry>=0.5.6,<0.6.0\" \"tensorflow==2.15.0\" \"matplotlib==3.8.2\""
    ]
   },
   {

train-and-deploy-mnist/deploy_model/gradio_demo.py

@@ -15,9 +15,12 @@ def get_inference(img_arr):
     fn=get_inference,
     inputs="image",
     outputs="label",
-    examples=[["sample_images/3.jpg"], ["sample_images/8.jpg"]],
+    examples=[
+        ["sample_images/3.jpg"],
+        ["sample_images/8.jpg"],
+        ["sample_images/7.png"]
+    ],
 )
-
 interface.launch(
     server_name="0.0.0.0",
     server_port=8000,

train-and-deploy-mnist/deploy_model/predict.py

@@ -11,9 +11,11 @@ def load_model(model_path: str) -> tf.keras.Model:
 def predict_fn(model, img_arr: np.ndarray) -> str:
     # Preprocess the image before passing it to the model
     img_arr = tf.expand_dims(img_arr, 0)
-    img_arr = img_arr[:, :, :, 0]  # Keep only the first channel (grayscale)
 
-    # Make predictions
+    # Resize to (1, 28, 28, 1)
+    resized_image = tf.image.resize(img_arr, [28, 28])
+    img_arr = resized_image[:, :, :, 0]  # Keep only the first channel (grayscale)
+
     predictions = model.predict(img_arr)
     predicted_label = tf.argmax(predictions[0]).numpy()
 

train-and-deploy-mnist/deploy_model/requirements.txt

@@ -1,2 +1,2 @@
 gradio==4.43.0
-tensorflow>=0.5.1
+tensorflow===2.15.0

train-and-deploy-mnist/deploy_model/sample_images/7.png

@@ -1,3 +1,3 @@
 matplotlib==3.8.2
 tensorflow==2.15.0
-truefoundry>=0.5.1
+truefoundry>=0.5.6,<0.6.0

train-and-deploy-mnist/train_job/requirements.txt

@@ -37,7 +37,7 @@
    },
    "outputs": [],
    "source": [
-    "%pip install -U -q \"truefoundry>=0.5.1\" \"tensorflow==2.15.0\" \"matplotlib==3.8.2\""
+    "%pip install -U -q \"truefoundry>=0.5.6,<0.6.0\" \"tensorflow==2.15.0\" \"matplotlib==3.8.2\""
    ]
   },
   {
@@ -252,7 +252,7 @@
     "    tf.keras.layers.Dense(10, activation='softmax')\n",
     "])\n",
     "\n",
-    "optimizer = tf.keras.optimizers.Adam(learning_rate=0.1)\n",
+    "optimizer = tf.keras.optimizers.Adam(learning_rate=0.01)\n",
     "\n",
     "# Compile the model\n",
     "model.compile(optimizer=optimizer, loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n"