Update app.py

app.py

@@ -10,3 +10,54 @@ urls = {
 
 for name, url in urls.items():
     st.write(f"- [{name}]({url})")
+
+
+import streamlit as st
+import tensorflow as tf
+from tensorflow.keras.preprocessing import image
+import numpy as np
+import pycuda.autoinit
+import pycuda.driver as cuda
+import tensorrt as trt
+import nvtabular as nvt
+import nvidia.dali as dali
+import nvidia.dali.ops as ops
+import nvidia.dali.types as types
+import deepstream as ds
+
+# Set up the Streamlit app
+st.set_page_config(page_title="Deep Learning Libraries Demo")
+
+# NVIDIA cuDNN
+st.header("NVIDIA cuDNN")
+st.write("cuDNN is a GPU-accelerated library of primitives for deep neural networks.")
+
+# NVIDIA TensorRT
+st.header("NVIDIA TensorRT")
+st.write("TensorRT is a high-performance deep learning inference optimizer and runtime for production deployment.")
+
+# NVIDIA Riva
+st.header("NVIDIA Riva")
+st.write("Riva is a platform for developing engaging and contextual AI-powered conversation apps.")
+
+# NVIDIA DeepStream SDK
+st.header("NVIDIA DeepStream SDK")
+st.write("DeepStream is a real-time streaming analytics toolkit for AI-based video understanding and multi-sensor processing.")
+
+# NVIDIA DALI
+st.header("NVIDIA DALI")
+st.write("DALI is a portable, open-source library for decoding and augmenting images and videos to accelerate deep learning applications.")
+
+# Load an image and run it through a pre-trained model
+st.header("Example: Image Classification with TensorFlow")
+model = tf.keras.applications.MobileNetV2()
+img_path = "example.jpg"
+img = image.load_img(img_path, target_size=(224, 224))
+x = image.img_to_array(img)
+x = np.expand_dims(x, axis=0)
+x = tf.keras.applications.mobilenet_v2.preprocess_input(x)
+preds = model.predict(x)
+st.write(f"Predicted class: {tf.keras.applications.mobilenet_v2.decode_predictions(preds, top=1)[0][0][1]}")
+
+# Clean up
+del model, img, x, preds