Update app.py

app.py

@@ -1,255 +1,27 @@
 import streamlit as st
-import pandas as pd
-import subprocess
-import time
-import random
 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 ----------------------------
-
-def generate_ner_data():
-    # Sample NER data for different entities
-    data_person = [{"text": f"Person example {i}", "entities": [{"entity": "Person", "value": f"Person {i}"}]} for i in range(1, 21)]
-    data_organization = [{"text": f"Organization example {i}", "entities": [{"entity": "Organization", "value": f"Organization {i}"}]} for i in range(1, 21)]
-    data_location = [{"text": f"Location example {i}", "entities": [{"entity": "Location", "value": f"Location {i}"}]} for i in range(1, 21)]
-    data_date = [{"text": f"Date example {i}", "entities": [{"entity": "Date", "value": f"Date {i}"}]} for i in range(1, 21)]
-    data_product = [{"text": f"Product example {i}", "entities": [{"entity": "Product", "value": f"Product {i}"}]} for i in range(1, 21)]
-    
-    # Create a dictionary of all NER examples
-    ner_data = {
-        "Person": data_person,
-        "Organization": data_organization,
-        "Location": data_location,
-        "Date": data_date,
-        "Product": data_product
-    }
-    
-    return ner_data
-
-# ---------------------------- Fun NER Data Function ----------------------------
-
-def ner_demo():
-    st.header("🤖 LLM NER Model Demo 🕵️‍♀️")
-    
-    # Generate NER data
-    ner_data = generate_ner_data()
-
-    # Pick a random entity type to display
-    entity_type = random.choice(list(ner_data.keys()))
-    st.subheader(f"Here comes the {entity_type} entity recognition, ready to show its magic! 🎩✨")
-
-    # Select a random record to display
-    example = random.choice(ner_data[entity_type])
-    st.write(f"Analyzing: *{example['text']}*")
-    
-    # Display recognized entity
-    for entity in example["entities"]:
-        st.success(f"🔍 Found a {entity['entity']}: **{entity['value']}**")
-    
-    # A bit of rhyme to lighten up the task
-    st.write("There once was an AI so bright, 🎇")
-    st.write("It could spot any name in sight, 👁️")
-    st.write("With a click or a tap, it put on its cap, 🎩")
-    st.write("And found entities day or night! 🌙")
-
-# ---------------------------- Helper: Text Data Augmentation ----------------------------
-
-def word_subtraction(text):
-    """Subtract words at random positions."""
-    words = text.split()
-    if len(words) > 2:
-        index = random.randint(0, len(words) - 1)
-        words.pop(index)
-    return " ".join(words)
-
-def word_recombination(text):
-    """Recombine words with random shuffling."""
-    words = text.split()
-    random.shuffle(words)
-    return " ".join(words)
-
-# ---------------------------- ML Model Building ----------------------------
-
-def build_small_model(input_shape):
-    model = models.Sequential()
-    model.add(layers.Dense(64, activation='relu', input_shape=(input_shape,)))
-    model.add(layers.Dense(32, activation='relu'))
-    model.add(layers.Dense(1, activation='sigmoid'))
-    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
-    return model
-
-# ---------------------------- TensorFlow and Keras Integration ----------------------------
-
-def train_model_demo():
-    st.header("🧪 Let's Build a Mini TensorFlow Model 🎓")
-
-    # Generate random synthetic data for simplicity
-    data_size = 100
-    X_train = np.random.rand(data_size, 10)
-    y_train = np.random.randint(0, 2, size=data_size)
-    
-    st.write(f"🚀 **Data Shape**: {X_train.shape}, with binary target labels.")
-    
-    # Build the model
-    model = build_small_model(X_train.shape[1])
-    
-    st.write("🔧 **Model Summary**:")
-    st.text(model.summary())
-
-    # Train the model
-    st.write("🚀 **Training the model...**")
-    history = model.fit(X_train, y_train, epochs=5, batch_size=16, verbose=0)
-
-    # Output training results humorously
-    st.success("🎉 Training completed! The model now knows its ABCs... or 1s and 0s at least! 😂")
-
-    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 small TensorFlow models, solve common developer problems, and augment text data using word subtraction and recombination strategies.")
-st.markdown("---")
-
-# ---------------------------- Main Navigation ----------------------------
-
-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'])
-
-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! 🎩✨")
-
-elif options == 'TensorFlow Model':
-    if st.button('🚀 Build and Train a TensorFlow Model'):
-        train_model_demo()
-
-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! 🎩✨")
-
-elif options == 'Code Sharing':
-    code_snippet_sharing()
-
-elif options == 'File Uploader':
-    file_uploader_example()
-
-elif options == 'Matplotlib Plot':
-    matplotlib_plot_example()
-
-elif options == 'Streamlit Cache':
-    cache_example()
-
-elif options == 'Tweet Spotlight':
-    display_tweet()
-
-st.markdown("---")
-
-# ---------------------------- Footer and Additional Resources ----------------------------
-
-st.subheader("📚 Additional Resources")
-st.markdown("""
-- [Official Streamlit Documentation](https://docs.streamlit.io/)
-- [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)
-""")
-
-# ---------------------------- requirements.txt ----------------------------
-st.markdown('''
-Reference Libraries:
-plaintext
-streamlit
-pandas
-numpy
-tensorflow
-transformers
-matplotlib
-''')
+from PIL import Image
+import os
+
+# Load the model directly from the path (no need to unzip)
+model_path = 'your_trained_model.keras'  # Path to your model
+model = tf.keras.models.load_model(model_path)  # Load the model
+
+# Streamlit UI for uploading an image
+st.title("Tree Decoration Prediction")
+uploaded_image = st.file_uploader("Upload an image", type=["jpg", "png"])
+
+if uploaded_image:
+    # Process the image for prediction
+    img = Image.open(uploaded_image)
+    img = img.resize((224, 224))  # Resize to match the input size of your model
+    img_array = np.array(img) / 255.0  # Normalize the image
+    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+
+    # Make prediction
+    prediction = model.predict(img_array)
+
+    # Display the result
+    st.image(uploaded_image, caption="Uploaded Image.", use_column_width=True)
+    st.write(f"Prediction: {'Decorated' if prediction[0] > 0.5 else 'Undecorated'}")