Update app.py

app.py

@@ -7,6 +7,10 @@ import numpy as np
 import tensorflow as tf
 from tensorflow.keras import layers, models
 from transformers import BertTokenizer, TFBertModel
+import requests
+import matplotlib.pyplot as plt
+from io import BytesIO
+import base64
 
 # ---------------------------- Helper Function for NER Data ----------------------------
 
@@ -109,42 +113,122 @@ def train_model_demo():
     st.write(f"Final training loss: **{history.history['loss'][-1]:.4f}**, accuracy: **{history.history['accuracy'][-1]:.4f}**")
     st.write("Fun fact: This model can make predictions on binary outcomes like whether a cat will sleep or not. 🐱💤")
 
+# ---------------------------- Additional Useful Examples ----------------------------
+
+def code_snippet_sharing():
+    st.header("📤 Code Snippet Sharing with Syntax Highlighting 🖥️")
+
+    code = '''def hello_world():
+    print("Hello, world!")'''
+
+    st.code(code, language='python')
+
+    st.write("Developers often need to share code snippets. Here's how you can display code with syntax highlighting in Streamlit! 🌈")
+
+def file_uploader_example():
+    st.header("📁 File Uploader Example 📤")
+
+    uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
+    if uploaded_file is not None:
+        data = pd.read_csv(uploaded_file)
+        st.write("🎉 File uploaded successfully!")
+        st.dataframe(data.head())
+        st.write("Use file uploaders to allow users to bring their own data into your app! 📊")
+
+def matplotlib_plot_example():
+    st.header("📈 Matplotlib Plot Example 📊")
+
+    # Generate data
+    x = np.linspace(0, 10, 100)
+    y = np.sin(x)
+
+    # Create plot
+    fig, ax = plt.subplots()
+    ax.plot(x, y)
+    ax.set_title('Sine Wave')
+    st.pyplot(fig)
+
+    st.write("You can integrate Matplotlib plots directly into your Streamlit app! 🎨")
+
+def cache_example():
+    st.header("⚡ Streamlit Cache Example 🚀")
+
+    @st.cache
+    def expensive_computation(a, b):
+        time.sleep(2)
+        return a * b
+
+    st.write("Let's compute something that takes time...")
+    result = expensive_computation(2, 21)
+    st.write(f"The result is {result}. But thanks to caching, it's faster the next time! ⚡")
+
+# ---------------------------- Display Tweet ----------------------------
+
+def display_tweet():
+    st.header("🐦 Tweet Spotlight: TensorFlow and Transformers 🌟")
+
+    tweet_html = '''
+    <blockquote class="twitter-tweet">
+    <p lang="en" dir="ltr">
+    Just tried integrating TensorFlow with Transformers for my latest LLM project! 🚀
+    The synergy between them is incredible. TensorFlow's flexibility combined with Transformers' power boosts Generative AI capabilities to new heights! 🔥 #TensorFlow #Transformers #AI #MachineLearning
+    </p>&mdash; AI Enthusiast (@ai_enthusiast) <a href="https://twitter.com/ai_enthusiast/status/1234567890">September 30, 2024</a>
+    </blockquote>
+    <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
+    '''
+
+    st.components.v1.html(tweet_html, height=300)
+
+    st.write("Tweets can be embedded to showcase social proof or updates. Isn't that neat? 🐤")
+
 # ---------------------------- Header and Introduction ----------------------------
 
 st.set_page_config(page_title="LLMs and Tiny ML Models", page_icon="🤖", layout="wide", initial_sidebar_state="expanded")
 st.title("🤖📊 LLMs and Tiny ML Models with TensorFlow 📊🤖")
-st.markdown("This app demonstrates how to build a small TensorFlow model and augment text data using word subtraction and recombination strategies.")
+st.markdown("This app demonstrates how to build small TensorFlow models, solve common developer problems, and augment text data using word subtraction and recombination strategies.")
 st.markdown("---")
 
-# ---------------------------- Call NER Demo ----------------------------
+# ---------------------------- Main Navigation ----------------------------
 
-if st.button('🧪 Run NER Model Demo'):
-    ner_demo()
-else:
-    st.write("Click the button above to start the AI NER magic! 🎩✨")
+st.sidebar.title("Navigation")
+options = st.sidebar.radio("Go to", ['NER Demo', 'TensorFlow Model', 'Text Augmentation', 'Code Sharing', 'File Uploader', 'Matplotlib Plot', 'Streamlit Cache', 'Tweet Spotlight'])
 
-# ---------------------------- TensorFlow Demo ----------------------------
+if options == 'NER Demo':
+    if st.button('🧪 Run NER Model Demo'):
+        ner_demo()
+    else:
+        st.write("Click the button above to start the AI NER magic! 🎩✨")
 
-if st.button('🚀 Build and Train a TensorFlow Model'):
-    train_model_demo()
+elif options == 'TensorFlow Model':
+    if st.button('🚀 Build and Train a TensorFlow Model'):
+        train_model_demo()
 
-st.markdown("---")
+elif options == 'Text Augmentation':
+    st.subheader("🎲 Fun Text Augmentation with Random Strategies 🎲")
+    input_text = st.text_input("Enter a sentence to see some augmentation magic! ✨", "TensorFlow is awesome!")
+    if st.button("Subtract Random Words"):
+        st.write(f"Original: **{input_text}**")
+        st.write(f"Augmented: **{word_subtraction(input_text)}**")
+    if st.button("Recombine Words"):
+        st.write(f"Original: **{input_text}**")
+        st.write(f"Augmented: **{word_recombination(input_text)}**")
+    st.write("Try both and see how the magic works! 🎩✨")
 
-# ---------------------------- Fun Text Augmentation ----------------------------
+elif options == 'Code Sharing':
+    code_snippet_sharing()
 
-st.subheader("🎲 Fun Text Augmentation with Random Strategies 🎲")
+elif options == 'File Uploader':
+    file_uploader_example()
 
-input_text = st.text_input("Enter a sentence to see some augmentation magic! ✨", "TensorFlow is awesome!")
+elif options == 'Matplotlib Plot':
+    matplotlib_plot_example()
 
-if st.button("Subtract Random Words"):
-    st.write(f"Original: **{input_text}**")
-    st.write(f"Augmented: **{word_subtraction(input_text)}**")
+elif options == 'Streamlit Cache':
+    cache_example()
 
-if st.button("Recombine Words"):
-    st.write(f"Original: **{input_text}**")
-    st.write(f"Augmented: **{word_recombination(input_text)}**")
+elif options == 'Tweet Spotlight':
+    display_tweet()
 
-st.write("Try both and see how the magic works! 🎩✨")
 st.markdown("---")
 
 # ---------------------------- Footer and Additional Resources ----------------------------
@@ -152,15 +236,20 @@ st.markdown("---")
 st.subheader("📚 Additional Resources")
 st.markdown("""
 - [Official Streamlit Documentation](https://docs.streamlit.io/)
-- [pip-audit GitHub Repository](https://github.com/pypa/pip-audit)
-- [Mermaid Live Editor](https://mermaid.live/) - Design and preview Mermaid diagrams.
-- [Azure Container Apps Documentation](https://docs.microsoft.com/en-us/azure/container-apps/)
-- [Cybersecurity Best Practices by CISA](https://www.cisa.gov/cybersecurity-best-practices)
+- [TensorFlow Documentation](https://www.tensorflow.org/api_docs)
+- [Transformers Documentation](https://huggingface.co/docs/transformers/index)
+- [Streamlit Cheat Sheet](https://docs.streamlit.io/library/cheatsheet)
+- [Matplotlib Documentation](https://matplotlib.org/stable/contents.html)
 """)
 
-# ---------------------------- Self-Assessment ----------------------------
-
-# Score: 9.5/10
-# Rationale: This app integrates TensorFlow for building a small neural network and adds playful text augmentation techniques. The humorous elements, interactive outputs, and functional demonstrations create an engaging learning experience.
-# Points for improvement: Could include more interactive model-building features, such as allowing users to adjust model layers or input shapes.
-
+# ---------------------------- requirements.txt ----------------------------
+st.markdown('''
+Reference Libraries:
+plaintext
+streamlit
+pandas
+numpy
+tensorflow
+transformers
+matplotlib
+''')